Search results for: online algorithm

Authors: Ben Morrow, Patrick O'Hara, Natalie Ban, Tunai Marques, Molly Fraser, Christopher Bone

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Relative to commercial fisheries, recreational fisheries are often poorly understood and pose various challenges for monitoring frameworks. In British Columbia (BC), Canada, Pacific salmon are heavily targeted by recreational fishers while also being a key source of nutrient flow and crucial prey for a variety of marine and terrestrial fauna, including endangered Southern Resident killer whales (Orcinus orca). Although commercial fisheries were historically responsible for the majority of salmon retention, recreational fishing now comprises both greater effort and retention. The current monitoring scheme for recreational salmon fisheries involves aerial-roving creel surveys. However, this method has been identified as costly and having low predictive power as it is often limited to sampling fragments of fluid and temporally dynamic fisheries. This study used imagery from two shore-based autonomous cameras in a highly active recreational fishery around Sooke, BC, and evaluated their efficacy in supplementing existing aerial-roving surveys for monitoring a recreational salmon fishery. This study involved continuous monitoring and high temporal resolution (over one million images analyzed in a single fishing season), using a deep learning-based vessel detection algorithm and a custom image annotation tool to efficiently thin datasets. This allowed for the quantification of peak-season effort from a busy harbour, species-specific retention estimates, high levels of detected fishing events at a nearby popular fishing location, as well as the proportion of the fishery management area represented by cameras. Then, this study demonstrated how it could substantially enhance the temporal resolution of a fishery through diel activity pattern analyses, scaled monthly to visualize clusters of activity. This work also highlighted considerable off-season fishing detection, currently unaccounted for in the existing monitoring framework. These results demonstrate several distinct applications of autonomous cameras for providing enhanced detail currently unavailable in the current monitoring framework, each of which has important considerations for the managerial allocation of resources. Further, the approach and methodology can benefit other studies that apply shore-based camera monitoring, supplement aerial-roving creel surveys to improve fine-scale temporal understanding, inform the optimal timing of creel surveys, and improve the predictive power of recreational stock assessments to preserve important and endangered fish species.

Keywords: cameras, monitoring, recreational fishing, stock assessment

Procedia PDF Downloads 88

Authors: Caroline Spencer, Suzanne Cross, Dudley McArdle, Frank Archer

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Objectives: The evolution of the Victorian Compendium of Community-Based Resilience Building Case Studies and its capacity to help communities implement activities that encourage adaptation to disaster risk reduction and promote community resilience in rural and urban locations provide this paper's objectives. Background: Between 2012 and 2019, community groups presented at the Monash University Disaster Resilience Initiative (MUDRI) 'Advancing Community Resilience Annual Forums', provided opportunities for communities to impart local resilience activities, how to solve challenges and share unforeseen learning and be considered for inclusion in the Compendium. A key tenet of the Compendium encourages compiling and sharing of grass-roots resilience building activities to help communities before, during, and after unexpected emergencies. The online Compendium provides free access for anyone wanting to help communities build expertise, reduce program duplication, and save valuable community resources. Identifying case study features across the emergency phases and analyzing critical success factors helps communities understand what worked and what did not work to achieve success and avoid known barriers. International exemplars inform the Compendium, which represents an Australian first and enhances Victorian community resilience initiatives. Emergency Management Victoria provided seed funding for the Compendium. MUDRI matched this support and continues to fund the project. A joint Steering Committee with broad-based user input and Human ethics approval guides its continued growth. Methods: A thematic analysis of the Compendium identified case study features, including critical success factors. Results: The Compendium comprises 38 case studies, representing all eight Victorian regions. Case studies addressed emergency phases, before (29), during (7), and after (17) events. Case studies addressed all hazards (23), bushfires (11), heat (2), fire safety (1), and house fires (1). Twenty case studies used a framework. Thirty received funding, of which nine received less than $20,000 and five received more than $100,000. Twenty-nine addressed a whole of community perspective. Case studies revealed unique and valuable learning in diverse settings. Critical success factors included strong governance; board support, leadership, and trust; partnerships; commitment, adaptability, and stamina; community-led initiatives. Other success factors included a paid facilitator and local government support; external funding, and celebrating success. Anecdotally, we are aware that community groups reference Compendium and that its value adds to community resilience planning. Discussion: The Compendium offers an innovative contribution to resilience research and practice. It augments the seven resilience characteristics to strengthen and encourage communities as outlined in the Statewide Community Resilience Framework for Emergency Management; brings together people from across sectors to deliver distinct, yet connected actions to strengthen resilience as a part of the Rockefeller funded Resilient Melbourne Strategy, and supports communities and economies to be resilient when a shock occurs as identified in the recently published Australian National Disaster Risk Reduction Framework. Each case study offers learning about connecting with community and how to increase their resilience to disaster risks and to keep their community safe from unexpected emergencies. Conclusion: The Compendium enables diverse communities to adopt or adapt proven resilience activities, thereby preserving valuable community resources and offers the opportunity to extend to a national or international Compendium.

Keywords: case study, community, compendium, disaster risk reduction, resilience

Procedia PDF Downloads 96

Authors: Farhan A. Alenizi

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Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

Procedia PDF Downloads 137

Authors: Brayden Mi

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Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

Procedia PDF Downloads 49

Authors: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

Procedia PDF Downloads 223

Authors: Francois Brambati, Daniele Ruscio, Federica Biassoni, Rebecca Hueting, Alessandra Tedeschi

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This research, in the frame of social acceptance of renewable energies and community-based production and consumption models, aims at (1) supporting a data-driven approachable to dealing with climate change and (2) identifying & quantifying the psycho-sociological dimensions and factors that could support the transition from a technology-driven approach to a consumer-driven approach throughout the emerging “prosumer business models.” In addition to the existing Social Acceptance dimensions, this research tries to identify a purely individual psychological fourth dimension to understand processes and factors underling individual acceptance and adoption of renewable energy business models, realizing a Prosumer Acceptance Index. Questionnaire data collection has been performed throughout an online survey platform, combining standardized and ad-hoc questions adapted for the research purposes. To identify the main factors (individual/social) influencing the relation with renewable energy technology (RET) adoption, a Factorial Analysis has been conducted to identify the latent variables that are related to each other, revealing 5 latent psychological factors: Factor 1. Concern about environmental issues: global environmental issues awareness, strong beliefs and pro-environmental attitudes rising concern on environmental issues. Factor 2. Interest in energy sharing: attentiveness to solutions for local community’s collective consumption, to reduce individual environmental impact, sustainably improve the local community, and sell extra energy to the general electricity grid. Factor 3. Concern on climate change: environmental issues consequences on climate change awareness, especially on a global scale level, developing pro-environmental attitudes on global climate change course and sensitivity about behaviours aimed at mitigating such human impact. Factor 4. Social influence: social support seeking from peers. With RET, advice from significant others is looked for internalizing common perceived social norms of the national/geographical region. Factor 5. Impact on bill cost: inclination to adopt a RET when economic incentives from the behaviour perception affect the decision-making process could result in less expensive or unvaried bills. Linear regression has been conducted to identify and quantify the factors that could better predict behavioural intention to become a prosumer. An overall scale measuring “acceptance of a renewable energy solution” was used as the dependent variable, allowing us to quantify the five factors that contribute to measuring: awareness of environmental issues and climate change; environmental attitudes; social influence; and environmental risk perception. Three variables can significantly measure and predict the scores of the “Acceptance in becoming a prosumer” ad hoc scale. Variable 1. Attitude: the agreement to specific environmental issues and global climate change issues of concerns and evaluations towards a behavioural intention. Variable 2. Economic incentive: the perceived behavioural control and its related environmental risk perception, in terms of perceived short-term benefits and long-term costs, both part of the decision-making process as expected outcomes of the behaviour itself. Variable 3. Age: despite fewer economic possibilities, younger adults seem to be more sensitive to environmental dimensions and issues as opposed to older adults. This research can facilitate policymakers and relevant stakeholders to better understand which relevant psycho-sociological factors are intervening in these processes and what and how specifically target when proposing change towards sustainable energy production and consumption.

Keywords: behavioural intention, environmental risk perception, prosumer, renewable energy technology, social acceptance

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Authors: Korbinian Schmidtner, Rudolf Sailer, Perry Bartelt, Wolfgang Fellin, Jan-Thomas Fischer, Matthias Granig

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This study focuses on the evaluation of snow avalanche simulations, based on a survey that has been carried out among avalanche experts. In the last decades, the application of avalanche simulation tools has gained recognition within the realm of hazard management. Traditionally, avalanche runout models were used to predict extreme avalanche runout and prepare avalanche maps. This has changed rather dramatically with the application of numerical models. For safety regulations such as road safety simulation tools are now being coupled with real-time meteorological measurements to predict frequent avalanche hazard. That places new demands on model accuracy and requires the simulation of physical processes that previously could be ignored. These simulation tools are based on a deterministic description of the avalanche movement allowing to predict certain quantities (e.g. pressure, velocities, flow heights, runout lengths etc.) of the avalanche flow. Because of the highly variable regimes of the flowing snow, no uniform rheological law describing the motion of an avalanche is known. Therefore, analogies to fluid dynamical laws of other materials are stated. To transfer these constitutional laws to snow flows, certain assumptions and adjustments have to be imposed. Besides these limitations, there exist high uncertainties regarding the initial and boundary conditions. Further challenges arise when implementing the underlying flow model equations into an algorithm executable by a computer. This implementation is constrained by the choice of adequate numerical methods and their computational feasibility. Hence, the model development is compelled to introduce further simplifications and the related uncertainties. In the light of these issues many questions arise on avalanche simulations, on their assets and drawbacks, on potentials for improvements as well as their application in practice. To address these questions a survey among experts in the field of avalanche science (e.g. researchers, practitioners, engineers) from various countries has been conducted. In the questionnaire, special attention is drawn on the expert’s opinion regarding the influence of certain variables on the simulation result, their uncertainty and the reliability of the results. Furthermore, it was tested to which degree a simulation result influences the decision making for a hazard assessment. A discrepancy could be found between a large uncertainty of the simulation input parameters as compared to a relatively high reliability of the results. This contradiction can be explained taking into account how the experts employ the simulations. The credibility of the simulations is the result of a rather thoroughly simulation study, where different assumptions are tested, comparing the results of different flow models along with the use of supplemental data such as chronicles, field observation, silent witnesses i.a. which are regarded as essential for the hazard assessment and for sanctioning simulation results. As the importance of avalanche simulations grows within the hazard management along with their further development studies focusing on the modeling fashion could contribute to a better understanding how knowledge of the avalanche process can be gained by running simulations.

Keywords: expert interview, hazard management, modeling, simulation, snow avalanche

Procedia PDF Downloads 297

Authors: Ramesh P., Aby Joseph

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Micro Inverters provide Module Embedded Solution for harvesting energy from small-scale solar photovoltaic (PV) panels. In addition to higher modularity & reliability (25 years of life), the MicroInverter has inherent advantages such as avoidance of long DC cables, eliminates module mismatch losses, minimizes partial shading effect, improves safety and flexibility in installations etc. Due to the above-stated benefits, the renewable energy technology with Solar Photovoltaic (PV) Micro Inverter becomes more widespread in Village Nano Grid application ensuring grid independence for rural communities and areas without access to electricity. While the primary objective of this paper is to discuss the problems related to rural electrification, this concept can also be extended to urban installation with grid connectivity. This work presents a comprehensive analysis of the power circuit design, control methodologies and prototyping of 300Wₚ Single Phase PV Micro Inverter. This paper investigates two different topologies for PV Micro Inverters, based on the first hand on Single Stage Flyback/ Forward PV Micro-Inverter configuration and the other hand on the Double stage configuration including DC-DC converter, H bridge DC-AC Inverter. This work covers Power Decoupling techniques to reduce the input filter capacitor size to buffer double line (100 Hz) ripple energy and eliminates the use of electrolytic capacitors. The propagation of the double line oscillation reflected back to PV module will affect the Maximum Power Point Tracking (MPPT) performance. Also, the grid current will be distorted. To mitigate this issue, an independent MPPT control algorithm is developed in this work to reject the propagation of this double line ripple oscillation to PV side to improve the MPPT performance and grid side to improve current quality. Here, the power hardware topology accepts wide input voltage variation and consists of suitably rated MOSFET switches, Galvanically Isolated gate drivers, high-frequency magnetics and Film capacitors with a long lifespan. The digital controller hardware platform inbuilt with the external peripheral interface is developed using floating point microcontroller TMS320F2806x from Texas Instruments. The firmware governing the operation of the PV Micro Inverter is written in C language and was developed using code composer studio Integrated Development Environment (IDE). In this work, the prototype hardware for the Single Phase Photovoltaic Micro Inverter with Double stage configuration was developed and the comparative analysis between the above mentioned configurations with experimental results will be presented.

Keywords: double line oscillation, micro inverter, MPPT, nano grid, power decoupling

Procedia PDF Downloads 110

Authors: Svetlana Petrikova, Alexander Yu Kostinskiy

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Management of research evaluation in the Higher School of Economics (HSE) originates from the HSE Academic Fund created in 2004 to facilitate and support academic research and presents its results to international academic community. As the means to inspire the applicants, science projects went through competitive selection process evaluated by the group of experts. Drastic development of HSE, quantity of applied projects for each Academic Fund competition and the need to coordinate the conduct of expert evaluation resulted in founding of the Office for Research Evaluation in 2013. The Office’s primary objective is management of research evaluation of science projects. The standards to conduct the evaluation are defined as follows: - The exercise of the process approach, the unification of the functioning of department. - The uniformity of regulatory, organizational and methodological framework. - The development of proper on-line evaluation system. - The broad involvement of external Russian and international experts, the renouncement of the usage of own employees. - The development of an algorithm to make a correspondence between experts and science projects. - The methodical usage of opened/closed international and Russian databases to extend the expert database. - The transparency of evaluation results – free access to assessment while keeping experts confidentiality. The management of research evaluation of projects is based on the sole standard, organization and financing. The standard way of conducting research evaluation at HSE is based upon Regulations on basic principles for research evaluation at HSE. These Regulations have been developed from the moment of establishment of the Office for Research Evaluation and are based on conventional corporate standards for regulatory document management. The management system of research evaluation is implemented on the process approach basis. Process approach means deployment of work as a process, which is the aggregation of interrelated and interacting activities processing inputs into outputs. Inputs are firstly client asking for the assessment to be conducted, defining the conditions for organizing and carrying of the assessment and secondly the applicant with proper for the competition application; output is assessment given to the client. While exercising process approach to clarify interrelation and interacting main parties or subjects of the assessment are determined and the way for interaction between them forms up. Parties to expert assessment are: - Ordering Party – The department of the university taking the decision to subject a project to expert assessment; - Providing Party – The department of the university authorized to provide such assessment by the Ordering Party; - Performing Party – The legal and natural entities that have expertise in the area of research evaluation. Experts assess projects in accordance with criteria and states of expert opinions approved by the Ordering Party. Objects of assessment generally are applications or HSE competition project reports. Mainly assessments are deployed for internal needs, i.e. the most ordering parties are HSE branches and departments, but assessment can also be conducted for external clients. The financing of research evaluation at HSE is based on the established corporate culture and traditions of HSE.

Keywords: expert assessment, management of research evaluation, process approach, research evaluation

Procedia PDF Downloads 225

Authors: Andrey Cherdantsev, Mikhail Cherdantsev, Sergey Isaenkov, Dmitriy Markovich

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In annular gas-liquid flow, liquid flows as a film along pipe walls sheared by high-velocity gas stream. Film surface is covered by large-scale disturbance waves which affect pressure drop and heat transfer in the system and are necessary for entrainment of liquid droplets from film surface into the core of gas stream. Disturbance waves are a highly complex and their properties are affected by numerous parameters. One of such aspects is flow development, i.e., change of flow properties with the distance from the inlet. In the present work, this question is studied using brightness-based laser-induced fluorescence (BBLIF) technique. This method enables one to perform simultaneous measurements of local film thickness in large number of points with high sampling frequency. In the present experiments first 50 cm of upward and downward annular flow in a vertical pipe of 11.7 mm i.d. is studied with temporal resolution of 10 kHz and spatial resolution of 0.5 mm. Thus, spatiotemporal evolution of film surface can be investigated, including scenarios of formation, acceleration and coalescence of disturbance waves. The behaviour of disturbance waves' velocity depending on phases flow rates and downstream distance was investigated. Besides measuring the waves properties, the goal of the work was to investigate the interrelation between disturbance waves properties and integral characteristics of the flow such as interfacial shear stress and flow rate of dispersed phase. In particular, it was shown that the initial acceleration of disturbance waves, defined by the value of shear stress, linearly decays with downstream distance. This lack of acceleration which may even lead to deceleration is related to liquid entrainment. Flow rate of disperse phase linearly grows with downstream distance. During entrainment events, liquid is extracted directly from disturbance waves, reducing their mass, area of interaction to the gas shear and, hence, velocity. Passing frequency of disturbance waves at each downstream position was measured automatically with a new algorithm of identification of characteristic lines of individual disturbance waves. Scenarios of coalescence of individual disturbance waves were identified. Transition from initial high-frequency Kelvin-Helmholtz waves appearing at the inlet to highly nonlinear disturbance waves with lower frequency was studied near the inlet using 3D realisation of BBLIF method in the same cylindrical channel and in a rectangular duct with cross-section of 5 mm by 50 mm. It was shown that the initial waves are generally two-dimensional but are promptly broken into localised three-dimensional wavelets. Coalescence of these wavelets leads to formation of quasi two-dimensional disturbance waves. Using cross-correlation analysis, loss and restoration of two-dimensionality of film surface with downstream distance were studied quantitatively. It was shown that all the processes occur closer to the inlet at higher gas velocities.

Keywords: annular flow, disturbance waves, entrainment, flow development

Procedia PDF Downloads 228

Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

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The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

Procedia PDF Downloads 63

Authors: Ehsan Javanmardemamgheisi, Javad Mehr, Jason Ali-Lavroff, Damien Holloway, Michael Davis

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During the last decades, a growing interest in faster and more efficient waterborne transportation has led to the development of high-speed vessels for both commercial and military applications. To satisfy this global demand, a wide variety of arrangements of high-speed crafts have been proposed by designers. Among them, high-speed catamarans have proven themselves to be a suitable Roll-on/Roll-off configuration for carrying passengers and cargo due to widely spaced demi hulls, a wide deck zone, and a high ratio of deadweight to displacement. To improve passenger comfort and crew workability and enhance the operability and performance of high-speed catamarans, mitigating the severity of motions and structural loads using Ride Control Systems (RCS) is essential.In this paper, a set of towing tank tests was conducted on a 2.5 m scaled model of a 112 m Incat Tasmania high-speed catamaran in irregular head seas to investigate the effect of different ride control algorithms including linear and nonlinear versions of the heave control, pitch control, and local control on motion responses and passenger comfort of the full-scale ship. The RCS included a centre bow-fitted T-Foil and two transom-mounted stern tabs. All the experiments were conducted at the Australian Maritime College (AMC) towing tank at a model speed of 2.89 m/s (37 knots full scale), a modal period of 1.5 sec (10 sec full scale) and two significant wave heights of 60 mm and 90 mm, representing full-scale wave heights of 2.7 m and 4 m, respectively. Spectral analyses were performed using Welch’s power spectral density method on the vertical motion time records of the catamaran model to calculate heave and pitch Response Amplitude Operators (RAOs). Then, noting that passenger discomfort arises from vertical accelerations and that the vertical accelerations vary at different longitudinal locations within the passenger cabin due to the variations in amplitude and relative phase of the pitch and heave motions, the vertical accelerations were calculated at three longitudinal locations (LCG, T-Foil, and stern tabs). Finally, frequency-weighted Root Mean Square (RMS) vertical accelerations were calculated to estimate Motion Sickness Dose Value (MSDV) of the ship based on ISO 2631-recommendations. It was demonstrated that in small seas, implementing a nonlinear pitch control algorithm reduces the peak pitch motions by 41%, the vertical accelerations at the forward location by 46%, and motion sickness at the forward position by around 20% which provides great potential for further improvement in passenger comfort, crew workability, and operability of high-speed catamarans.

Keywords: high-speed catamarans, ride control system, response amplitude operators, vertical accelerations, motion sickness, irregular waves, towing tank tests.

Procedia PDF Downloads 49

Authors: Zhuang Yiwen

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The paper examines the challenges facing the Singapore healthcare system related to the tracking and traceability of medical devices. One of the major challenges identified is the lack of a standard coding system for medical devices, which makes it difficult to track them effectively. The paper suggests the use of the Unique Device Identifier (UDI) as a single standard for medical devices to improve tracking and reduce errors. The paper also explores the use of barcoding and image recognition to identify and document medical devices in nursing practices. In nursing practices, the use of barcodes for identifying medical devices is common. However, the information contained in these barcodes is often inconsistent, making it challenging to identify which segment contains the model identifier. Moreover, the use of barcodes may be improved with the use of UDI, but many subsidized accessories may still lack barcodes. The paper suggests that the readiness for UDI and barcode standardization requires standardized information, fields, and logic in electronic medical record (EMR), operating theatre (OT), and billing systems, as well as barcode scanners that can read various formats and selectively parse barcode segments. Nursing workflow and data flow also need to be taken into account. The paper also explores the use of image recognition, specifically the Tesseract OCR engine, to identify and document implants in public hospitals due to limitations in barcode scanning. The study found that the solution requires an implant information database and checking output against the database. The solution also requires customization of the algorithm, cropping out objects affecting text recognition, and applying adjustments. The solution requires additional resources and costs for a mobile/hardware device, which may pose space constraints and require maintenance of sterile criteria. The integration with EMR is also necessary, and the solution require changes in the user's workflow. The paper suggests that the long-term use of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) as a supporting terminology to improve clinical documentation and data exchange in healthcare. SNOMED CT provides a standardized way of documenting and sharing clinical information with respect to procedure, patient and device documentation, which can facilitate interoperability and data exchange. In conclusion, the paper highlights the challenges facing the Singapore healthcare system related to the tracking and traceability of medical devices. The paper suggests the use of UDI and barcode standardization to improve tracking and reduce errors. It also explores the use of image recognition to identify and document medical devices in nursing practices. The paper emphasizes the importance of standardized information, fields, and logic in EMR, OT, and billing systems, as well as barcode scanners that can read various formats and selectively parse barcode segments. These recommendations could help the Singapore healthcare system to improve tracking and traceability of medical devices and ultimately enhance patient safety.

Keywords: medical device tracking, unique device identifier, barcoding and image recognition, systematized nomenclature of medicine clinical terms

Procedia PDF Downloads 47

Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

Procedia PDF Downloads 246

Authors: Dimitrios Tzilopoulos, Panagiotis Symeonidis, Michael Loufakis, Dimosthenis Ioannidis, Dimitrios Tzovaras

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The domestic film industry in Greece has traditionally relied heavily on state support. While film productions are crucial for the country's economy, it has not fully capitalized on attracting and promoting foreign productions. The lack of motivation, organized state support for attraction and licensing, and the absence of location scouting have hindered its potential. Although recent legislative changes have addressed the first two of these issues, the development of a comprehensive location database and a search engine that would effectively support location scouting at the pre-production location scouting is still in its early stages. In addition to the expected benefits of the film, television, marketing, and multimedia industries, a location-scouting service platform has the potential to yield significant financial gains locally and nationally. By promoting featured places like cultural and archaeological sites, natural monuments, and attraction points for visitors, it plays a vital role in both cultural promotion and facilitating tourism development. This study introduces LOCATION3, an internet platform revolutionizing film production location management. It interconnects location providers, film crews, and multimedia stakeholders, offering a comprehensive environment for seamless collaboration. The platform's central geodatabase (PostgreSQL) stores each location’s attributes, while web technologies like HTML, JavaScript, CSS, React.js, and Redux power the user-friendly interface. Advanced functionalities, utilizing deep learning models, developed in Python, are integrated via Node.js. Visual data presentation is achieved using the JS Leaflet library, delivering an interactive map experience. LOCATION3 sets a new standard, offering a range of essential features to enhance the management of film production locations. Firstly, it empowers users to effortlessly upload audiovisual material enriched with geospatial and temporal data, such as location coordinates, photographs, videos, 360-degree panoramas, and 3D location models. With the help of cutting-edge deep learning algorithms, the application automatically tags these materials, while users can also manually tag them. Moreover, the application allows users to record locations directly through its user-friendly mobile application. Users can then embark on seamless location searches, employing spatial or descriptive criteria. This intelligent search functionality considers a combination of relevant tags, dominant colors, architectural characteristics, emotional associations, and unique location traits. One of the application's standout features is the ability to explore locations by their visual similarity to other materials, facilitated by a reverse image search. Also, the interactive map serves as both a dynamic display for locations and a versatile filter, adapting to the user's preferences and effortlessly enhancing location searches. To further streamline the process, the application facilitates the creation of location lightboxes, enabling users to efficiently organize and share their content via email. Going above and beyond location management, the platform also provides invaluable liaison, matchmaking, and online marketplace services. This powerful functionality bridges the gap between visual and three-dimensional geospatial material providers, local agencies, film companies, production companies, etc. so that those interested in a specific location can access additional material beyond what is stored on the platform, as well as access production services supporting the functioning and completion of productions in a location (equipment provision, transportation, catering, accommodation, etc.).

Keywords: deep learning models, film industry, geospatial data management, location scouting

Procedia PDF Downloads 46

Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

Procedia PDF Downloads 239

Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

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Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

Procedia PDF Downloads 178

Authors: Bensaid A., Mostephaoui T., Nedjai R.

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A considerable area of Algerian lands are threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mécheriadepartment generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of PlanetScope PSB.SB sensors images by September 29, 2021. As a second step, we prospect the use of a multi-scale geographic object-based image analysis (GEOBIA) approach to segment the high spatial resolution images acquired on heterogeneous surfaces that vary according to human influence on the environment. We have used the fractal net evolution approach (FNEA) algorithm to segment images (Baatz&Schäpe, 2000). Multispectral data, a digital terrain model layer, ground truth data, a normalized difference vegetation index (NDVI) layer, and a first-order texture (entropy) layer were used to segment the multispectral images at three segmentation scales, with an emphasis on accurately delineating the boundaries and components of the sand accumulation areas (Dune, dunes fields, nebka, and barkhane). It is important to note that each auxiliary data contributed to improve the segmentation at different scales. The silted areas were classified using a nearest neighbor approach over the Naâma area using imagery. The classification of silted areas was successfully achieved over all study areas with an accuracy greater than 85%, although the results suggest that, overall, a higher degree of landscape heterogeneity may have a negative effect on segmentation and classification. Some areas suffered from the greatest over-segmentation and lowest mapping accuracy (Kappa: 0.79), which was partially attributed to confounding a greater proportion of mixed siltation classes from both sandy areas and bare ground patches. This research has demonstrated a technique based on very high-resolution images for mapping sanded and degraded areas using GEOBIA, which can be applied to the study of other lands in the steppe areas of the northern countries of the African continent.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

Procedia PDF Downloads 78

Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

Procedia PDF Downloads 114

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 261

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

Procedia PDF Downloads 116

Authors: Rowan Moore, Kai Scheffler, Eugen Damoc, Jennifer Sutton, Aaron Bailey, Stephane Houel, Simon Cubbon, Jonathan Josephs

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Purpose: MS analysis of monoclonal antibodies (mAbs) at the protein and peptide levels is critical during development and production of biopharmaceuticals. The compositions of current generation therapeutic proteins are often complex due to various modifications which may affect efficacy. Intact proteins analyzed by MS are detected in higher charge states that also provide more complexity in mass spectra. Protein analysis in native or native-like conditions with zero or minimal organic solvent and neutral or weakly acidic pH decreases charge state value resulting in mAb detection at higher m/z ranges with more spatial resolution. Methods: Three commercially available mAbs were used for all experiments. Intact proteins were desalted online using size exclusion chromatography (SEC) or reversed phase chromatography coupled on-line with a mass spectrometer. For streamlined use of the LC- MS platform we used a single SEC column and alternately selected specific mobile phases to perform separations in either denaturing or native-like conditions: buffer A (20 % ACN, 0.1 % FA) with Buffer B (100 mM ammonium acetate). For peptide analysis mAbs were proteolytically digested with and without prior reduction and alkylation. The mass spectrometer used for all experiments was a commercially available Thermo Scientific™ hybrid Quadrupole-Orbitrap™ mass spectrometer, equipped with the new BioPharma option which includes a new High Mass Range (HMR) mode that allows for improved high mass transmission and mass detection up to 8000 m/z. Results: We have analyzed the profiles of three mAbs under reducing and native conditions by direct infusion with offline desalting and with on-line desalting via size exclusion and reversed phase type columns. The presence of high salt under denaturing conditions was found to influence the observed charge state envelope and impact mass accuracy after spectral deconvolution. The significantly lower charge states observed under native conditions improves the spatial resolution of protein signals and has significant benefits for the analysis of antibody mixtures, e.g. lysine variants, degradants or sequence variants. This type of analysis requires the detection of masses beyond the standard mass range ranging up to 6000 m/z requiring the extended capabilities available in the new HMR mode. We have compared each antibody sample that was analyzed individually with mixtures in various relative concentrations. For this type of analysis, we observed that apparent native structures persist and ESI is benefited by the addition of low amounts of acetonitrile and formic acid in combination with the ammonium acetate-buffered mobile phase. For analyses on the peptide level we analyzed reduced/alkylated, and non-reduced proteolytic digests of the individual antibodies separated via reversed phase chromatography aiming to retrieve as much information as possible regarding sequence coverage, disulfide bridges, post-translational modifications such as various glycans, sequence variants, and their relative quantification. All data acquired were submitted to a single software package for analysis aiming to obtain a complete picture of the molecules analyzed. Here we demonstrate the capabilities of the mass spectrometer to fully characterize homogeneous and heterogeneous therapeutic proteins on one single platform. Conclusion: Full characterization of heterogeneous intact protein mixtures by improved mass separation on a quadrupole-Orbitrap™ mass spectrometer with extended capabilities has been demonstrated.

Keywords: disulfide bond analysis, intact analysis, native analysis, mass spectrometry, monoclonal antibodies, peptide mapping, post-translational modifications, sequence variants, size exclusion chromatography, therapeutic protein analysis, UHPLC

Procedia PDF Downloads 338

Authors: Saqib Aziz, Brad Alexander, Christoph Gengnagel, Stefan Weinzierl

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This paper explores a generative design, simulation, and optimization workflow for the integration of acoustical diffuser and/or absorber geometry with embedded coupled Helmholtz-resonators for full-scale 3D printed building components. Large-scale additive manufacturing in conjunction with algorithmic CAD design tools enables a vast amount of control when creating geometry. This is advantageous regarding the increasing demands of comfort standards for indoor spaces and the use of more resourceful and sustainable construction methods and materials. The presented methodology highlights these new technological advancements and offers a multimodal and integrative design solution with the potential for an immediate application in the AEC-Industry. In principle, the methodology can be applied to a wide range of structural elements that can be manufactured by additive manufacturing processes. The current paper focuses on a case study of an application for a biaxial load-bearing beam grillage made of reinforced concrete, which allows for a variety of applications through the combination of additive prefabricated semi-finished parts and in-situ concrete supplementation. The semi-prefabricated parts or formwork bodies form the basic framework of the supporting structure and at the same time have acoustic absorption and diffusion properties that are precisely acoustically programmed for the space underneath the structure. To this end, a hybrid validation strategy is being explored using a digital and cross-platform simulation environment, verified with physical prototyping. The iterative workflow starts with the generation of a parametric design model for the acoustical geometry using the algorithmic visual scripting editor Grasshopper3D inside the building information modeling (BIM) software Revit. Various geometric attributes (i.e., bottleneck and cavity dimensions) of the resonator are parameterized and fed to a numerical optimization algorithm which can modify the geometry with the goal of increasing absorption at resonance and increasing the bandwidth of the effective absorption range. Using Rhino.Inside and LiveLink for Revit, the generative model was imported directly into the Multiphysics simulation environment COMSOL. The geometry was further modified and prepared for simulation in a semi-automated process. The incident and scattered pressure fields were simulated from which the surface normal absorption coefficients were calculated. This reciprocal process was repeated to further optimize the geometric parameters. Subsequently the numerical models were compared to a set of 3D concrete printed physical twin models, which were tested in a .25 m x .25 m impedance tube. The empirical results served to improve the starting parameter settings of the initial numerical model. The geometry resulting from the numerical optimization was finally returned to grasshopper for further implementation in an interdisciplinary study.

Keywords: acoustical design, additive manufacturing, computational design, multimodal optimization

Procedia PDF Downloads 135

Authors: Mohamed Sameer Mughal, Andrew D. Ross, Damian J. Fearon

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Background: The fast-paced changing landscape of UK Higher Education Institution (HEIs) is poised by changes and challenges affecting Middle Managers (MM) in their job roles. MM contribute to the success of HEIs by balancing the equilibrium and pass organization strategies from senior staff towards operationalization directives to junior staff. However, this study showcased from the data analyzed during the semi structured interviews; MM job role is becoming more complex due to changes and challenges creating colossal pressures and workloads in day-to-day working. Current development programmes provisions by Human Resources (HR) departments in such HEIs are not feasible, applicable, and matching the true essence and requirements of MM who suggest that programmes offered by HR are too generic to suit their precise needs and require tailor made espousal to work effectively in their pertinent job roles. Methodologies: This study aims to capture demands of MM Development Needs (DN) by means of a conceptual model as conclusive part of the research that is divided into 2 phases. Phase 1 initiated by carrying out 2 pilot interviews with a retired Emeritus status professor and HR programmes development coordinator. Key themes from the pilot and literature review subsidized into formulation of 22 set of questions (Kvale and Brinkmann) in form of interviewing questionnaire during qualitative data collection. Data strategy and collection consisted of purposeful sampling of 12 semi structured interviews (n=12) lasting approximately an hour for all participants. The MM interviewed were at faculty and departmental levels which included; deans (n=2), head of departments (n=4), subject leaders (n=2), and lastly programme leaders (n=4). Participants recruitment was carried out via emails and snowballing technique. The interviews data was transcribed (verbatim) and managed using Computer Assisted Qualitative Data Analysis using Nvivo ver.11 software. Data was meticulously analyzed using Miles and Huberman inductive approach of positivistic style grounded theory, whereby key themes and categories emerged from the rich data collected. The data was precisely coded and classified into case studies (Robert Yin); with a main case study, sub cases (4 classes of MM) and embedded cases (12 individual MMs). Major Findings: An interim conceptual model emerged from analyzing the data with main concepts that included; key performance indicators (KPI’s), HEI effectiveness and outlook, practices, processes and procedures, support mechanisms, student events, rules, regulations and policies, career progression, reporting/accountability, changes and challenges, and lastly skills and attributes. Conclusion: Dynamic elements affecting MM includes; increase in government pressures, student numbers, irrelevant development programmes, bureaucratic structures, transparency and accountability, organization policies, skills sets… can only be confronted by employing structured development programmes originated by HR that are not provided generically. Future Work: Stage 2 (Quantitative method) of the study plans to validate the interim conceptual model externally through fully completed online survey questionnaire (Bram Oppenheim) from external HEIs (n=150). The total sample targeted is 1500 MM. Author contribution focuses on enhancing management theory and narrow the gap between by HR and MM development programme provision.

Keywords: development needs (DN), higher education institutions (HEIs), human resources (HR), middle managers (MM)

Procedia PDF Downloads 210

Authors: Lolwa Alansari, Abdelhamid Azhaghdani, Sufia Athar, Hanen Mrabet, Annaliza Cruz, Tamara Alshadafat, Almunzer Zakaria

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Introduction: Improving the patient experience is a fundamental pillar of healthcare's quadruple aims. Recognizing the importance of patient experiences and perceptions in healthcare interactions is pivotal for driving quality improvement. This abstract centers around the Patient Experience Program, an endeavor crafted with the purpose of comprehending and elevating the experiences of patients in the Obstetrics & Gynecology Emergency Department (OB/GYN ED). Methodology: This comprehensive endeavor unfolded through a structured sequence of phases following Plan-Do-Study-Act (PDSA) model, spanning over 12 months, focused on enhancing patient experiences in the Obstetrics & Gynecology Emergency Department (OB/GYN ED). The study meticulously examined the journeys of patients with acute obstetrics and gynecological conditions, collecting data from over 100 participants monthly. The inclusive approach covered patients of different priority levels (1-5) admitted for acute conditions, with no exclusions. Historical data from March and April 2022 serves as a benchmark for comparison, strengthening causality claims by providing a baseline understanding of OB/GYN ED performance before interventions. Additionally, the methodology includes the incorporation of staff engagement surveys to comprehensively understand the experiences of healthcare professionals with the implemented improvements. Data extraction involved administering open-ended questions and comment sections to gather rich qualitative insights. The survey covered various aspects of the patient journey, including communication, emotional support, timely access to care, care coordination, and patient-centered decision-making. The project's data analysis utilized a mixed-methods approach, combining qualitative techniques to identify recurring themes and extract actionable insights and quantitative methods to assess patient satisfaction scores and relevant metrics over time, facilitating the measurement of intervention impact and longitudinal tracking of changes. From the themes we discovered in both the online and in-person patient experience surveys, several key findings emerged that guided us in initiating improvements, including effective communication and information sharing, providing emotional support and empathy, ensuring timely access to care, fostering care coordination and continuity, and promoting patient-centered decision-making. Results: The project yielded substantial positive outcomes, significantly improving patient experiences in the OB/GYN ED. Patient satisfaction levels rose from 62% to a consistent 98%, with notable improvements in satisfaction with care plan information and physician care. Waiting time satisfaction increased from 68% to a steady 97%. The project positively impacted nurses' and midwives' job satisfaction, increasing from 64% to an impressive 94%. Operational metrics displayed positive trends, including a decrease in the "left without being seen" rate from 3% to 1%, the discharge against medical advice rate dropping from 8% to 1%, and the absconded rate reducing from 3% to 0%. These outcomes underscore the project's effectiveness in enhancing both patient and staff experiences in the healthcare setting. Conclusion: The use of a patient experience questionnaire has been substantiated by evidence-based research as an effective tool for improving the patient experience, guiding interventions, and enhancing overall healthcare quality in the OB/GYN ED. The project's interventions have resulted in a more efficient allocation of resources, reduced hospital stays, and minimized unnecessary resource utilization. This, in turn, contributes to cost savings for the healthcare facility.

Keywords: patient experience, patient survey, person centered care, quality initiatives

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Authors: Alaa Fezai, Anuj Sharma, Wolfgang Mueller-Hirsch, André Zimmermann

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Viscoelastic materials have been widely used in the automotive industry over the last few decades with different functionalities. Besides their main application as a simple and efficient surface damping treatment, they may ensure optimal operating conditions for on-board electronics as thermal interface or sealing layers. The dynamic behavior of viscoelastic materials is generally dependent on many environmental factors, the most important being temperature and strain rate or frequency. Prior to the reliability analysis of systems including viscoelastic layers, it is, therefore, crucial to accurately predict the dynamic and lifetime behavior of these materials. This includes the identification of the dynamic material parameters under critical temperature and frequency conditions along with a precise damage localization and identification methodology. The goal of this work is twofold. The first part aims at applying an inverse viscoelastic material-characterization approach for a wide frequency range and under different temperature conditions. For this sake, dynamic measurements are carried on a single lap joint specimen using an electrodynamic shaker and an environmental chamber. The specimen consists of aluminum beams assembled to adapter plates through a viscoelastic adhesive layer. The experimental setup is reproduced in finite element (FE) simulations, and frequency response functions (FRF) are calculated. The parameters of both the generalized Maxwell model and the fractional derivatives model are identified through an optimization algorithm minimizing the difference between the simulated and the measured FRFs. The second goal of the current work is to guarantee an on-line detection of the damage, i.e., delamination in the viscoelastic bonding of the described specimen during frequency monitored end-of-life testing. For this purpose, an inverse technique, which determines the damage location and size based on the modal frequency shift and on the change of the mode shapes, is presented. This includes a preliminary FE model-based study correlating the delamination location and size to the change in the modal parameters and a subsequent experimental validation achieved through dynamic measurements of specimen with different, pre-generated crack scenarios and comparing it to the virgin specimen. The main advantage of the inverse characterization approach presented in the first part resides in the ability of adequately identifying the material damping and stiffness behavior of soft viscoelastic materials over a wide frequency range and under critical temperature conditions. Classic forward characterization techniques such as dynamic mechanical analysis are usually linked to limitations under critical temperature and frequency conditions due to the material behavior of soft viscoelastic materials. Furthermore, the inverse damage detection described in the second part guarantees an accurate prediction of not only the damage size but also its location using a simple test setup and outlines; therefore, the significance of inverse numerical-experimental approaches in predicting the dynamic behavior of soft bonding layers applied in automotive electronics.

Keywords: damage detection, dynamic characterization, inverse approaches, vibration testing, viscoelastic layers

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Michael Brave, Mark Kroll, Steven Karch, Charles Wetli, Michael Graham, Sebastian Kunz, Dorin Panescu

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Background: Conducted electrical weapons (CEW) are now used in 107 countries and are a common law enforcement less-lethal force practice in the United Kingdom (UK), United States of America (USA), Canada, Australia, New Zealand, and others. Use of these devices is rarely temporally associated with the occurrence of sudden arrest-related deaths (ARD). Because such deaths are uncommon, few Medical Examiners (MEs) ever encounter one, and even fewer offices have established comprehensive investigative protocols. Without sufficient scientific data, the role, if any, played by a CEW in a given case is largely supplanted by conjecture often defaulting to a CEW-induced fatal cardiac arrhythmia. In addition to the difficulty in investigating individual deaths, the lack of information also detrimentally affects being able to define and evaluate the ARD cohort generally. More comprehensive, better information leads to better interpretation in individual cases and also to better research. The purpose of this presentation is to provide MEs with a comprehensive evidence-based checklist to assist in the assessment of CEW-ARD cases. Methods: PUBMED and Sociology/Criminology data bases were queried to find all medical, scientific, electrical, modeling, engineering, and sociology/criminology peer-reviewed literature for mentions of CEW or synonymous terms. Each paper was then individually reviewed to identify those that discussed possible bioelectrical mechanisms relating CEW to ARD. A Naranjo-type pharmacovigilance algorithm was also employed, when relevant, to identify and quantify possible direct CEW electrical myocardial stimulation. Additionally, CEW operational manuals and training materials were reviewed to allow incorporation of CEW-specific technical parameters. Results: Total relevant PUBMED citations of CEWs were less than 250, and reports of death extremely rare. Much relevant information was available from Sociology/Criminology data bases. Once the relevant published papers were identified, and reviewed, we compiled an annotated checklist of data that we consider critical to a thorough CEW-involved ARD investigation. Conclusion: We have developed an evidenced-based checklist that can be used by MEs and their staffs to assist them in identifying, collecting, documenting, maintaining, and objectively analyzing the role, if any, played by a CEW in any specific case of sudden death temporally associated with the use of a CEW. Even in cases where the collected information is deemed by the ME as insufficient for formulating an opinion or diagnosis to a reasonable degree of medical certainty, information collected as per the checklist will often be adequate for other stakeholders to use as a basis for informed decisions. Having reviewed the appropriate materials in a significant number of cases careful examination of the heart and brain is likely adequate. Channelopathy testing should be considered in some cases, however it may be considered cost prohibitive (aprox $3000). Law enforcement agencies may want to consider establishing a reserve fund to help manage such rare cases. The expense may stay the enormous costs associated with incident-precipitated litigation.

Keywords: ARD, CEW, police, TASER

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Kaushikk Iyer, Richard M Hall, David Keeling

Abstract:

Hardware-in-the-loop (HIL) simulation is an advanced technique for developing and testing complex real-time control systems. This paper presents the benefits of HIL simulation and how it can be implemented and used effectively to develop, test, and validate advanced control algorithms used in a spinal joint Wear simulator for the Tribological testing of spinal disc prostheses. spinal wear simulator is technologically the most advanced machine currently employed For the in-vitro testing of newly developed spinal Discimplants. However, the existing control techniques, such as a simple position control Does not allow the simulator to test non-sinusoidal waveforms. Thus, there is a need for better and advanced control methods that can be developed and tested Rigorouslybut safely before deploying it into the real simulator. A benchtop HILsetupis was created for experimentation, controller verification, and validation purposes, allowing different control strategies to be tested rapidly in a safe environment. The HIL simulation aspect in this setup attempts to replicate similar spinal motion and loading conditions. The spinal joint wear simulator containsa four-Barlinkpowered by electromechanical actuators. LabVIEW software is used to design a kinematic model of the spinal wear Simulator to Validatehow each link contributes towards the final motion of the implant under test. As a result, the implant articulates with an angular motion specified in the international standards, ISO-18192-1, that define fixed, simplified, and sinusoid motion and load profiles for wear testing of cervical disc implants. Using a PID controller, a velocity-based position control algorithm was developed to interface with the benchtop setup that performs HIL simulation. In addition to PID, a fuzzy logic controller (FLC) was also developed that acts as a supervisory controller. FLC provides intelligence to the PID controller by By automatically tuning the controller for profiles that vary in amplitude, shape, and frequency. This combination of the fuzzy-PID controller is novel to the wear testing application for spinal simulators and demonstrated superior performance against PIDwhen tested for a spectrum of frequency. Kaushikk Iyer is a Ph.D. Student at the University of Leeds and an employee at Key Engineering Solutions, Leeds, United Kingdom, (e-mail: [email protected], phone: +44 740 541 5502). Richard M Hall is with the University of Leeds, the United Kingdom as a professor in the Mechanical Engineering Department (e-mail: [email protected]). David Keeling is the managing director of Key Engineering Solutions, Leeds, United Kingdom (e-mail: [email protected]). Results obtained are successfully validated against the load and motion tolerances specified by the ISO18192-1 standard and fall within limits, that is, ±0.5° at the maxima and minima of the motion and ±2 % of the complete cycle for phasing. The simulation results prove the efficacy of the test setup using HIL simulation to verify and validate the accuracy and robustness of the prospective controller before its deployment into the spinal wear simulator. This method of testing controllers enables a wide range of possibilities to test advanced control algorithms that can potentially test even profiles of patients performing various dailyliving activities.

Keywords: Fuzzy-PID controller, hardware-in-the-loop (HIL), real-time simulation, spinal wear simulator

Procedia PDF Downloads 150