Search results for: pointing accuracy

Authors: Angelis P. Barlampas

Abstract:

Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

Procedia PDF Downloads 42

Authors: Helga Heylen

Abstract:

With dogs saving wildlife in New Zealand since 1890 and governments in Africa, Australia and Canada trusting them to give the best results, Conservation Dogs Ireland want to introduce more detection dogs to protect Europe's native wildlife. Conservation detection dogs are fast, portable and endlessly trainable. They are a cost-effective, highly sensitive and non-invasive way to detect protected and invasive species and wildlife disease. Conservation dogs find targets up to 40 times faster than any other method. They give results instantly, with near-perfect accuracy. They can search for multiple targets simultaneously, with no reduction in efficacy The European Red List indicates the decline in biodiversity has been most rapid in the past 50 years, and the risk of extinction never higher. Just two examples of major threats dogs are trained to tackle are: (I)Japanese Knotweed (Fallopia Japonica), not only a serious threat to ecosystems, crops, structures like bridges and roads - it can wipe out the entire value of a house. The property industry and homeowners are only just waking up to the full extent of the nightmare. When those working in construction on the roads move topsoil with a trace of Japanese Knotweed, it suffices to start a new colony. Japanese Knotweed grows up to 7cm a day. It can stay dormant and resprout after 20 years. In the UK, the cost of removing Japanese Knotweed from the London Olympic site in 2012 was around £70m (€83m). UK banks already no longer lend on a house that has Japanese Knotweed on-site. Legally, landowners are now obliged to excavate Japanese Knotweed and have it removed to a landfill. More and more, we see Japanese Knotweed grow where a new house has been constructed, and topsoil has been brought in. Conservation dogs are trained to detect small fragments of any part of the plant on sites and in topsoil. (II)Zebra mussels (Dreissena Polymorpha) are a threat to many waterways in the world. They colonize rivers, canals, docks, lakes, reservoirs, water pipes and cooling systems. They live up to 3 years and will release up to one million eggs each year. Zebra mussels attach to surfaces like rocks, anchors, boat hulls, intake pipes and boat engines. They cause changes in nutrient cycles, reduction of plankton and increased plant growth around lake edges, leading to the decline of Europe's native mussel and fish populations. There is no solution, only costly measures to keep it at bay. With many interconnected networks of waterways, they have spread uncontrollably. Conservation detection dogs detect the Zebra mussel from its early larvae stage, which is still invisible to the human eye. Detection dogs are more thorough and cost-effective than any other conservation method, and will greatly complement and speed up the work of biologists, surveyors, developers, ecologists and researchers.

Keywords: native biodiversity, conservation detection dogs, invasive species, Japanese Knotweed, zebra mussel

Procedia PDF Downloads 165

Authors: Ali Kadir, O. Anwar Beg

Abstract:

Thermal barrier coatings are among the most popular methods for providing corrosion protection in high temperature applications including aircraft engine systems, external spacecraft structures, rocket chambers etc. Many different materials are available for such coatings, of which ceramics generally perform the best. Motivated by these applications, the current investigation presents detailed finite element simulations of coating stress analysis for a 3- dimensional, 3-layered model of a test sample representing a typical gas turbine component scenario. Structural steel is selected for the main inner layer, Titanium (Ti) alloy for the middle layer and Silicon Carbide (SiC) for the outermost layer. The model dimensions are 20 mm (width), 10 mm (height) and three 1mm deep layers. ANSYS software is employed to conduct three types of analysis- static structural, thermal stress analysis and also computational fluid dynamic erosion/corrosion analysis (via ANSYS FLUENT). The specified geometry which corresponds to corrosion test samples exactly is discretized using a body-sizing meshing approach, comprising mainly of tetrahedron cells. Refinements were concentrated at the connection points between the layers to shift the focus towards the static effects dissipated between them. A detailed grid independence study is conducted to confirm the accuracy of the selected mesh densities. To recreate gas turbine scenarios; in the stress analysis simulations, static loading and thermal environment conditions of up to 1000 N and 1000 degrees Kelvin are imposed. The default solver was used to set the controls for the simulation with the fixed support being set as one side of the model while subjecting the opposite side to a tabular force of 500 and 1000 Newtons. Equivalent elastic strain, total deformation, equivalent stress and strain energy were computed for all cases. Each analysis was duplicated twice to remove one of the layers each time, to allow testing of the static and thermal effects with each of the coatings. ANSYS FLUENT simulation was conducted to study the effect of corrosion on the model under similar thermal conditions. The momentum and energy equations were solved and the viscous heating option was applied to represent improved thermal physics of heat transfer between the layers of the structures. A Discrete Phase Model (DPM) in ANSYS FLUENT was employed which allows for the injection of continuous uniform air particles onto the model, thereby enabling an option for calculating the corrosion factor caused by hot air injection (particles prescribed 5 m/s velocity and 1273.15 K). Extensive visualization of results is provided. The simulations reveal interesting features associated with coating response to realistic gas turbine loading conditions including significantly different stress concentrations with different coatings.

Keywords: thermal coating, corrosion, ANSYS FEA, CFD

Procedia PDF Downloads 118

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Hamadi Cherif, Christophe Coquelet, Paolo Stringari, Denis Clodic, Laura Pellegrini, Stefania Moioli, Stefano Langè

Abstract:

Biogas is a promising technology which can be used as a vehicle fuel, for heat and electricity production, or injected in the national gas grid. It is storable, transportable, not intermittent and substitutable for fossil fuels. This gas produced from the wastewater treatment by degradation of organic matter under anaerobic conditions is mainly composed of methane and carbon dioxide. To be used as a renewable fuel, biogas, whose energy comes only from methane, must be purified from carbon dioxide and other impurities such as water vapor, siloxanes and hydrogen sulfide. Purification of biogas for this application particularly requires the removal of hydrogen sulfide, which negatively affects the operation and viability of equipment especially pumps, heat exchangers and pipes, causing their corrosion. Several methods are available to eliminate hydrogen sulfide from biogas. Herein, reactive absorption in structured packed column by means of chemical absorption in aqueous sodium hydroxide solutions is considered. This study is based on simulations using Aspen Plus™ V8.0, and comparisons are done with data from an industrial pilot plant treating 85 Nm3/h of biogas which contains about 30 ppm of hydrogen sulfide. The rate-based model approach has been used for simulations in order to determine the efficiencies of separation for different operating conditions. To describe vapor-liquid equilibrium, a γ/ϕ approach has been considered: the Electrolyte NRTL model has been adopted to represent non-idealities in the liquid phase, while the Redlich-Kwong equation of state has been used for the vapor phase. In order to validate the thermodynamic model, Henry’s law constants of each compound in water have been verified against experimental data. Default values available in Aspen Plus™ V8.0 for the properties of pure components properties as heat capacity, density, viscosity and surface tension have also been verified. The obtained results for physical and chemical properties are in a good agreement with experimental data. Reactions involved in the process have been studied rigorously. Equilibrium constants for equilibrium reactions and the reaction rate constant for the kinetically controlled reaction between carbon dioxide and the hydroxide ion have been checked. Results of simulations of the pilot plant purification section show the influence of low temperatures, concentration of sodium hydroxide and hydrodynamic parameters on the selective absorption of hydrogen sulfide. These results show an acceptable degree of accuracy when compared with the experimental data obtained from the pilot plant. Results show also the great efficiency of sodium hydroxide for the removal of hydrogen sulfide. The content of this compound in the gas leaving the column is under 1 ppm.

Keywords: biogas, hydrogen sulfide, reactive absorption, sodium hydroxide, structured packed column

Procedia PDF Downloads 315

Authors: Hisham Arab Alkabeya, A. M. Hughes, J. Adams

Abstract:

Background: People with Rheumatoid Arthritis (RA) continue to experience problems with hand function despite new drug advances and targeted medical treatment. Consequently, it is important to identify the factors that influence the impact of RA disease on hand function. This systematic review identified observational studies that reported factors that influenced the impact of RA on hand function. Methods: MEDLINE, EMBASE, CINAL, AMED, PsychINFO, and Web of Science database were searched from January 1990 up to March 2017. Full-text articles published in English that described factors related to hand functional disability in people with RA were selected following predetermined inclusion and exclusion criteria. Pertinent data were thoroughly extracted and documented using a pre-designed data extraction form by the lead author, and cross-checked by the review team for completion and accuracy. Factors related to hand function were classified under the domains of the International Classification of Functioning, Disability, and Health (ICF) framework and health-related factors. Three reviewers independently assessed the methodological quality of the included articles using the quality of cross-sectional studies (AXIS) tool. Factors related to hand function that was investigated in two or more studies were explored using a best-evidence synthesis. Results: Twenty articles form 19 studies met the inclusion criteria from 1,271 citations; all presented cross-sectional data (five high quality and 15 low quality studies), resulting in at best limited evidence in the best-evidence synthesis. For the factors classified under the ICF domains, the best-evidence synthesis indicates that there was a range of body structure and function factors that were related with hand functional disability. However, key factors were hand strength, disease activity, and pain intensity. Low functional status (physical, emotional and social) level was found to be related with limited hand function. For personal factors, there is limited evidence that gender is not related with hand function; whereas, conflicting evidence was found regarding the relationship between age and hand function. In the domain of environmental factors, there was limited evidence that work activity was not related with hand function. Regarding health-related factors, there was limited evidence that the level of the rheumatoid factor (RF) was not related to hand function. Finally, conflicting evidence was found regarding the relationship between hand function and disease duration and general health status. Conclusion: Studies focused on body structure and function factors, highlighting a lack of investigation into personal and environmental factors when considering the impact of RA on hand function. The level of evidence which exists was limited, but identified that modifiable factors such as grip or pinch strength, disease activity and pain are the most influential factors on hand function in people with RA. The review findings suggest that important personal and environmental factors that impact on hand function in people with RA are not yet considered or reported in clinical research. Well-designed longitudinal, preferably cohort, studies are now needed to better understand the causality between personal and environmental factors and hand functional disability in people with RA.

Keywords: factors, hand function, rheumatoid arthritis, systematic review

Procedia PDF Downloads 121

Authors: Haya Salah, Srinivas Sharan

Abstract:

Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

Procedia PDF Downloads 88

Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

Abstract:

To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

Procedia PDF Downloads 112

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

Abstract:

Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

Procedia PDF Downloads 228

Authors: Tianyu Wang, Nikita Karandikar

Abstract:

The uncertainty around cost, safety, and feasibility of the decarbonized shipping fuels has made it increasingly complex for the shipping companies to set pricing strategies and forecast their freight revenues going forward. The increase in the green fuel surcharges will ultimately influence the automobile’s consumer prices. The auto shipping demand (ton-miles) has been gradually shifting from long-haul to short-sea trade over the past years following the relocation of the original equipment manufacturer (OEM) manufacturing to regions such as South America and Southeast Asia. The objective of this paper is twofold: 1) to investigate the car-carriers freight revenue development over the years when the trade pattern is gradually shifting towards short-sea exports 2) to empirically identify the quantitative impact of such trade pattern shifting to mainly freight rate, but also vessel size, fleet size as well as Green House Gas (GHG) emission in Roll on-Roll Off (Ro-Ro) shipping. In this paper, a model of analyzing and forecasting ton-miles and freight revenues for the trade routes of AS-NA (Asia to North America), EU-NA (Europe to North America), and SA-NA (South America to North America) is established by deploying Automatic Identification System (AIS) data and the financial results of a selected car carrier company. More specifically, Wallenius Wilhelmsen Logistics (WALWIL), the Norwegian Ro-Ro carrier listed on Oslo Stock Exchange, is selected as the case study company in this paper. AIS-based ton-mile datasets of WALWIL vessels that are sailing into North America region from three different origins (Asia, Europe, and South America), together with WALWIL’s quarterly freight revenues as reported in trade segments, will be investigated and compared for the past five years (2018-2022). Furthermore, ordinary‐least‐square (OLS) regression is utilized to construct the ton-mile demand and freight revenue forecasting. The determinants of trade pattern shifting, such as import tariffs following the China-US trade war and fuel prices following the 0.1% Emission Control Areas (ECA) zone requirement after IMO2020 will be set as key variable inputs to the machine learning model. The model will be tested on another newly listed Norwegian Car Carrier, Hoegh Autoliner, to forecast its 2022 financial results and to validate the accuracy based on its actual results. GHG emissions on the three routes will be compared and discussed based on a constant emission per mile assumption and voyage distances. Our findings will provide important insights about 1) the trade-off evaluation between revenue reduction and energy saving with the new ton-mile pattern and 2) how the trade flow shifting would influence the future need for the vessel and fleet size.

Keywords: AIS, automobile exports, maritime big data, trade flows

Procedia PDF Downloads 91

Authors: Neoclis Hadjigeorgiou, Spyridon Angelopoulos, Evangelos V. Hristoforou, Paul P. Sotiriadis

Abstract:

The increasing demand for accurate and reliable magnetic measurements over the past decades has paved the way for the development of different types of magnetic sensing systems as well as of more advanced measurement techniques. Anisotropic Magneto Resistance (AMR) sensors have emerged as a promising solution for applications requiring high resolution, providing an ideal balance between performance and cost. However, certain issues of AMR sensors such as non-linear response and measurement noise are rarely discussed in the relevant literature. In this work, an analog closed loop compensation system is proposed, developed and tested as a means to eliminate the non-linearity of AMR response, reduce the 1/f noise and enhance the sensitivity of magnetic sensor. Additional performance aspects, such as cross-axis and hysteresis effects are also examined. This system was analyzed using an analytical model and a P-Spice model, considering both the sensor itself as well as the accompanying electronic circuitry. In addition, a commercial closed loop architecture Flux-Gate sensor (calibrated and certified), has been used for comparison purposes. Three different experimental setups have been constructed for the purposes of this work, each one utilized for DC magnetic field measurements, AC magnetic field measurements and Noise density measurements respectively. The DC magnetic field measurements have been conducted in laboratory environment employing a cubic Helmholtz coil setup in order to calibrate and characterize the system under consideration. A high-accuracy DC power supply has been used for providing the operating current to the Helmholtz coils. The results were recorded by a multichannel voltmeter The AC magnetic field measurements have been conducted in laboratory environment employing a cubic Helmholtz coil setup in order to examine the effective bandwidth not only of the proposed system but also for the Flux-Gate sensor. A voltage controlled current source driven by a function generator has been utilized for the Helmholtz coil excitation. The result was observed by the oscilloscope. The third experimental apparatus incorporated an AC magnetic shielding construction composed of several layers of electric steel that had been demagnetized prior to the experimental process. Each sensor was placed alone and the response was captured by the oscilloscope. The preliminary experimental results indicate that closed loop AMR response presented a maximum deviation of 0.36% with respect to the ideal linear response, while the corresponding values for the open loop AMR system and the Fluxgate sensor reached 2% and 0.01% respectively. Moreover, the noise density of the proposed close loop AMR sensor system remained almost as low as the noise density of the AMR sensor itself, yet considerably higher than that of the Flux-Gate sensor. All relevant numerical data are presented in the paper.

Keywords: AMR sensor, chopper, closed loop, electronic noise, magnetic noise, memory effects, flux-gate sensor, linearity improvement, sensitivity improvement

Procedia PDF Downloads 400

Authors: A. Chatel, I. S. Torreguitart, T. Verstraete

Abstract:

The paper deals with a single point optimization of the LS89 turbine using an adjoint optimization and defining the design variables within a CAD system. The advantage of including the CAD model in the design system is that higher level constraints can be imposed on the shape, allowing the optimized model or component to be manufactured. However, CAD-based approaches restrict the design space compared to node-based approaches where every node is free to move. In order to preserve a rich design space, we develop a methodology to refine the CAD model during the optimization and to create the best parameterization to use at each time. This study presents a methodology to progressively refine the design space, which combines parametric effectiveness with a differential evolutionary algorithm in order to create an optimal parameterization. In this manuscript, we show that by doing the parameterization at the CAD level, we can impose higher level constraints on the shape, such as the axial chord length, the trailing edge radius and G2 geometric continuity between the suction side and pressure side at the leading edge. Additionally, the adjoint sensitivities are filtered out and only smooth shapes are produced during the optimization process. The use of algorithmic differentiation for the CAD kernel and grid generator allows computing the grid sensitivities to machine accuracy and avoid the limited arithmetic precision and the truncation error of finite differences. Then, the parametric effectiveness is computed to rate the ability of a set of CAD design parameters to produce the design shape change dictated by the adjoint sensitivities. During the optimization process, the design space is progressively enlarged using the knot insertion algorithm which allows introducing new control points whilst preserving the initial shape. The position of the inserted knots is generally assumed. However, this assumption can hinder the creation of better parameterizations that would allow producing more localized shape changes where the adjoint sensitivities dictate. To address this, we propose using a differential evolutionary algorithm to maximize the parametric effectiveness by optimizing the location of the inserted knots. This allows the optimizer to gradually explore larger design spaces and to use an optimal CAD-based parameterization during the course of the optimization. The method is tested on the LS89 turbine cascade and large aerodynamic improvements in the entropy generation are achieved whilst keeping the exit flow angle fixed. The trailing edge and axial chord length, which are kept fixed as manufacturing constraints. The optimization results show that the multilevel optimizations were more efficient than the single level optimization, even though they used the same number of design variables at the end of the multilevel optimizations. Furthermore, the multilevel optimization where the parameterization is created using the optimal knot positions results in a more efficient strategy to reach a better optimum than the multilevel optimization where the position of the knots is arbitrarily assumed.

Keywords: adjoint, CAD, knots, multilevel, optimization, parametric effectiveness

Procedia PDF Downloads 89

Authors: Gizem Kayar, Ramazan Bakir, M. Ilkay Koşar, Ceren U. Gencer, Alperen Ayyildiz

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Anatomy classes are crucial for general education of medical students, whereas learning anatomy is quite challenging and requires memorization of thousands of structures. In traditional teaching methods, learning materials are still based on books, anatomy mannequins, or videos. This results in forgetting many important structures after several years. However, more interactive teaching methods like virtual reality, augmented reality, gamification, and motion sensors are becoming more popular since such methods ease the way we learn and keep the data in mind for longer terms. During our study, we designed a virtual reality based digital head anatomy platform to investigate whether a fully interactive anatomy platform is effective to learn anatomy and to understand the level of teaching and learning optimization. The Head is one of the most complicated human anatomy structures, with thousands of tiny, unique structures. This makes the head anatomy one of the most difficult parts to understand during class sessions. Therefore, we developed a fully interactive digital tool with 3D model marking, quiz structures, 2D/3D puzzle structures, and VR support so as to integrate the power of VR and gamification. The project has been developed in Unity game engine with HTC Vive Cosmos VR headset. The head anatomy 3D model has been selected with full skeletal, muscular, integumentary, head, teeth, lymph, and vein system. The biggest issue during the development was the complexity of our model and the marking of it in the 3D world system. 3D model marking requires to access to each unique structure in the counted subsystems which means hundreds of marking needs to be done. Some parts of our 3D head model were monolithic. This is why we worked on dividing such parts to subparts which is very time-consuming. In order to subdivide monolithic parts, one must use an external modeling tool. However, such tools generally come with high learning curves, and seamless division is not ensured. Second option was to integrate tiny colliders to all unique items for mouse interaction. However, outside colliders which cover inner trigger colliders cause overlapping, and these colliders repel each other. Third option is using raycasting. However, due to its own view-based nature, raycasting has some inherent problems. As the model rotate, view direction changes very frequently, and directional computations become even harder. This is why, finally, we studied on the local coordinate system. By taking the pivot point of the model into consideration (back of the nose), each sub-structure is marked with its own local coordinate with respect to the pivot. After converting the mouse position to the world position and checking its relation with the corresponding structure’s local coordinate, we were able to mark all points correctly. The advantage of this method is its applicability and accuracy for all types of monolithic anatomical structures.

Keywords: anatomy, e-learning, virtual reality, 3D model marking

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Authors: Britt Erni, Aldara Vazquez Fernandez, Roland Maurer

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Visuospatial perspective taking (VSPT) is a process that allows to integrate spatial information from different points of view, and to transform the mental images we have of the environment to properly orient our movements and anticipate the location of landmarks during navigation. VSPT is also related to egocentric perspective transformations (imagined rotations or translations of one's point of view) and to infer the visuospatial experiences of another person (e.g. if and how another person sees objects). This process is deeply related to a wide-ranging capacity called the theory of mind (ToM), an essential cognitive function that allows us to regulate our social behaviour by attributing mental representations to individuals in order to make behavioural predictions. VSPT is often considered in the literature as the starting point of the development of the theory of mind. VSPT and ToM include several levels of knowledge that have to be assessed by specific tasks. Unfortunately, the lack of tasks assessing these functions in clinical neuropsychology leads to underestimate, in brain-damaged patients, deficits of these functions which are essential, in everyday life, to regulate our social behaviour (ToM) and to navigate in known and unknown environments (VSPT). Therefore, this study aims to create and standardize a VSPT task in order to explore the cognitive requirements of VSPT and ToM, and to specify their relationship in healthy adults and thereafter in brain-damaged patients. Two versions of a computerized VSPT task were administered to healthy participants (M = 28.18, SD = 4.8 years). In both versions the environment was a 3D representation of 10 different geometric shapes placed on a circular base. Two sets of eight pictures were generated from this: of the environment with an avatar somewhere on its periphery (locations) and of what the avatar sees from that place (views). Two types of questions were asked: a) identify the location from the view, and b) identify the view from the location. Twenty participants completed version 1 of the task and 20 completed the second version, where the views were offset by ±15° (i.e., clockwise or counterclockwise) and participants were asked to choose the closest location or the closest view. The preliminary findings revealed that version 1 is significantly easier than version 2 for accuracy (with ceiling scores for version 1). In version 2, participants responded significantly slower when they had to infer the avatar's view from the latter's location, probably because they spent more time visually exploring the different views (responses). Furthermore, men significantly performed better than women in version 1 but not in version 2. Most importantly, a sensitive task (version 2) has been created for which the participants do not seem to easily and automatically compute what someone is looking at yet which does not involve more heavily other cognitive functions. This study is further completed by including analysis on non-clinical participants with low and high degrees of schizotypy, different socio-educational status, and with a range of older adults to examine age-related and other differences in VSPT processing.

Keywords: mental transformation, spatial cognition, theory of mind, visuospatial perspective taking

Procedia PDF Downloads 164

Authors: Shamini Dhana, G. Subrahmanya VRK Rao

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The fashion industry is advancing towards a mindset of zero waste, personalization, creativity, and circularity. The trend of upcycling clothing and materials into personalized fashion is being demanded by the next generation. There is a need for a digital tool to accelerate the process towards mass customization. Dhana’s D/Sphere fashion technology platform uses digital tools to accelerate upcycling. In essence, advanced fashion garments can be designed and developed via reuse, repurposing, recreating activities, and using existing fabric and circulating materials. The D/Sphere platform has the following objectives: to provide (1) An opportunity to develop modern fashion using existing, finished materials and clothing without chemicals or water consumption; (2) The potential for an everyday customer and designer to use the medium of fashion for creative expression; (3) A solution to address the global textile waste generated by pre- and post-consumer fashion; (4) A solution to reduce carbon emissions, water, and energy consumption with the participation of all stakeholders; (5) An opportunity for brands, manufacturers, retailers to work towards zero-waste designs and as an alternative revenue stream. Other benefits of this alternative approach include sustainability metrics, trend prediction, facilitation of disassembly and remanufacture deep learning, and hyperheuristics for high accuracy. A design tool for mass personalization and customization utilizing existing circulating materials and deadstock, targeted to fashion stakeholders will lower environmental costs, increase revenues through up to date upcycled apparel, produce less textile waste during the cut-sew-stitch process, and provide a real design solution for the end customer to be part of circular fashion. The broader impact of this technology will result in a different mindset to circular fashion, increase the value of the product through multiple life cycles, find alternatives towards zero waste, and reduce the textile waste that ends up in landfills. This technology platform will be of interest to brands and companies that have the responsibility to reduce their environmental impact and contribution to climate change as it pertains to the fashion and apparel industry. Today, over 70% of the $3 trillion fashion and apparel industry ends up in landfills. To this extent, the industry needs such alternative techniques to both address global textile waste as well as provide an opportunity to include all stakeholders and drive circular fashion with new personalized products. This type of modern systems thinking is currently being explored around the world by the private sector, organizations, research institutions, and governments. This technological innovation using digital tools has the potential to revolutionize the way we look at communication, capabilities, and collaborative opportunities amongst stakeholders in the development of new personalized and customized products, as well as its positive impacts on society, our environment, and global climate change.

Keywords: circular fashion, deep learning, digital technology platform, personalization

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Authors: Elissandro Voigt Beier, Cristiano Poleto

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The main objective of the research is to assess the loss through the quantification of material culture (archaeological fragments) in rural areas, sites explored economically by machining on seasonal crops, and also permanent, in a hydrographic subsystem Camaquã River in the state of Rio Grande do Sul, Brazil. The study area consists of different micro basins and differs in area, ranging between 1,000 m² and 10,000 m², respectively the largest and the smallest, all with a large number of occurrences and outcrop locations of archaeological material and high density in intense farm environment. In the first stage of the research aimed to identify the dispersion of points of archaeological material through field survey through plot points by the Global Positioning System (GPS), within each river basin, was made use of concise bibliography on the topic in the region, helping theoretically in understanding the old landscaping with preferences of occupation for reasons of ancient historical people through the settlements relating to the practice observed in the field. The mapping was followed by the cartographic development in the region through the development of cartographic products of the land elevation, consequently were created cartographic products were to contribute to the understanding of the distribution of the absolute materials; the definition and scope of the material dispersed; and as a result of human activities the development of revolving letter by mechanization of in situ material, it was also necessary for the preparation of materials found density maps, linking natural environments conducive to ancient historical occupation with the current human occupation. The third stage of the project it is for the systematic collection of archaeological material without alteration or interference in the subsurface of the indigenous settlements, thus, the material was prepared and treated in the laboratory to remove soil excesses, cleaning through previous communication methodology, measurement and quantification. Approximately 15,000 were identified archaeological fragments belonging to different periods of ancient history of the region, all collected outside of its environmental and historical context and it also has quite changed and modified. The material was identified and cataloged considering features such as object weight, size, type of material (lithic, ceramic, bone, Historical porcelain and their true association with the ancient history) and it was disregarded its principles as individual lithology of the object and functionality same. As observed preliminary results, we can point out the change of materials by heavy mechanization and consequent soil disturbance processes, and these processes generate loading of archaeological materials. Therefore, as a next step will be sought, an estimate of potential losses through a mathematical model. It is expected by this process, to reach a reliable model of high accuracy which can be applied to an archeological site of lower density without encountering a significant error.

Keywords: degradation of heritage, quantification in archaeology, watershed, use and occupation of land

Procedia PDF Downloads 244

Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

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System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

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Authors: Chuyu Huang, Itsuki Minemi, Kuanlin Chen, Yuki Hirose

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It remains unclear how language speakers are able to perceive phonological contrasts that do not exist on their own. This experiment uses the vowel-length distinction in Japanese, which is phonologically contrastive and co-occurs with tonal change in some cases. For speakers whose first language does not distinguish vowel length, contrastive duration is usually misperceived, e.g., Mandarin speakers. Two alternative hypotheses for how Mandarin speakers would perceive a phonological contrast that does not exist in their language make different predictions. The stress parameter model does not have a clear prediction about the impact of tonal type. Mandarin speakers will likely be not able to perceive vowel length as well as Japanese native speakers do, but the performance might not correlate to tonal type because the prosody of their language is distinctive, which requires users to encode lexical prosody and notice subtle differences in word prosody. By contrast, cue-based phonetic models predict that Mandarin speakers may rely on pitch differences, a secondary cue, to perceive vowel length. Two groups of Mandarin speakers, including naive non-Japanese speakers and beginner learners, were recruited to participate in an AX discrimination task involving two Japanese sound stimuli that contain a phonologically contrastive environment. Participants were asked to indicate whether the two stimuli containing a vowel-length contrast (e.g., maapero vs. mapero) sound the same. The experiment was bifactorial. The first factor contrasted three syllabic positions (syllable position; initial/medial/final), as it would be likely to affect the perceptual difficulty, as seen in previous studies, and the second factor contrasted two pitch types (accent type): one with accentual change that could be distinguished with the lexical tones in Mandarin (the different condition), with the other group having no tonal distinction but only differing in vowel length (the same condition). The overall results showed that a significant main effect of accent type by applying a linear mixed-effects model (β = 1.48, SE = 0.35, p < 0.05), which implies that Mandarin speakers tend to more successfully recognize vowel-length differences when the long vowel counterpart takes on a tone that exists in Mandarin. The interaction between the accent type and the syllabic position is also significant (β = 2.30, SE = 0.91, p < 0.05), showing that vowel lengths in the different conditions are more difficult to recognize in the word-final case relative to the initial condition. The second statistical model, which compares naive speakers to beginners, was conducted with logistic regression to test the effects of the participant group. A significant difference was found between the two groups (β = 1.06, 95% CI = [0.36, 2.03], p < 0.05). This study shows that: (1) Mandarin speakers are likely to use pitch cues to perceive vowel length in a non-native language, which is consistent with the cue-based approaches; (2) an exposure effect was observed: the beginner group achieved a higher accuracy for long vowel perception, which implied the exposure effect despite the short period of language learning experience.

Keywords: cue-based perception, exposure effect, prosodic perception, vowel duration

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Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

Procedia PDF Downloads 87

Authors: Stephen Bahooshy

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Co-producing services with the people that access them is considered best practice in the United Kingdom, with the Care Act 2014 arguing that people who access services and their carers should be involved in the design, commissioning and delivery of services. Co-production is a way of working with the community, breaking down barriers of access and providing meaningful opportunity for people to engage. Unfortunately, owing to a number of reported factors such as time constraints, practitioner experience and departmental budget restraints, this process is not always followed. In 2019, in a south London borough, d/Deaf people who access services were engaged in the design, commissioning and delivery of an information and advice service that would support their community to access local government services. To do this, sensory impairment social workers and commissioners collaborated to host a series of engagement events with the d/Deaf community. Interpreters were used to enable communication between the commissioners and d/Deaf participants. Initially, the community’s opinions, ideas and requirements were noted. This was then summarized and fed back to the community to ensure accuracy. Subsequently, a service specification was developed which included performance metrics, inclusive of qualitative and quantitative indicators, such as ‘I statements’, whereby participants respond on an adapted Likert scale how much they agree or disagree with a particular statement in relation to their experience of the service. The service specification was reviewed by a smaller group of d/Deaf residents and social workers, to ensure that it met the community’s requirements. The service was then tendered using the local authority’s e-tender process. Bids were evaluated and scored in two parts; part one was by commissioners and social workers and part two was a presentation by prospective providers to an evaluation panel formed of four d/Deaf residents. The internal evaluation panel formed 75% of the overall score, whilst the d/Deaf resident evaluation panel formed 25% of the overall tender score. Co-producing the evaluation panel with social workers and the d/Deaf community meant that commissioners were able to meet the requirements of this community by developing evaluation questions and tools that were easily understood and use by this community. For example, the wording of questions were reviewed and the scoring mechanism consisted of three faces to reflect the d/Deaf residents’ scores instead of traditional numbering. These faces were a happy face, a neutral face and a sad face. By making simple changes to the commissioning and tender evaluation process, d/Deaf people were able to have meaningful involvement in the design and commissioning process for a service that would benefit their community. Co-produced performance metrics means that it is incumbent on the successful provider to continue to engage with people accessing the service and ensure that the feedback is utilized. d/Deaf residents were grateful to have been involved in this process as this was not an opportunity that they had previously been afforded. In recognition of their time, each d/Deaf resident evaluator received a £40 gift voucher, bringing the total cost of this co-production to £160.

Keywords: co-production, community engagement, deaf and hearing impaired, service design

Procedia PDF Downloads 247

Authors: Aliya K. Salahova

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Scientific thinking, characterized by purposeful knowledge-seeking and the harmonization of theory and facts, holds a crucial role in preparing young minds for an increasingly complex and technologically advanced world. This abstract presents a research study aimed at fostering scientific thinking in early childhood, focusing on children aged 5 to 9 years, through experiential learning in Kids Science Labs (STEM). The study utilized a longitudinal exploration design, spanning 240 weeks from September 2018 to April 2023, to evaluate the effectiveness of the Kids Science Labs program in developing scientific thinking skills. Participants in the research comprised 72 children drawn from local schools and community organizations. Through a formative psychology-pedagogical experiment, the experimental group engaged in weekly STEM activities carefully designed to stimulate scientific thinking, while the control group participated in daily art classes for comparison. To assess the scientific thinking abilities of the participants, a registration table with evaluation criteria was developed. This table included indicators such as depth of questioning, resource utilization in research, logical reasoning in hypotheses, procedural accuracy in experiments, and reflection on research processes. The data analysis revealed dynamic fluctuations in the number of children at different levels of scientific thinking proficiency. While the development was not uniform across all participants, a main leading factor emerged, indicating that the Kids Science Labs program and formative experiment exerted a positive impact on enhancing scientific thinking skills in children within this age range. The study's findings support the hypothesis that systematic implementation of STEM activities effectively promotes and nurtures scientific thinking in children aged 5-9 years. Enriching education with a specially planned STEM program, tailoring scientific activities to children's psychological development, and implementing well-planned diagnostic and corrective measures emerged as essential pedagogical conditions for enhancing scientific thinking abilities in this age group. The results highlight the significant and positive impact of the systematic-activity approach in developing scientific thinking, leading to notable progress and growth in children's scientific thinking abilities over time. These findings have promising implications for educators and researchers, emphasizing the importance of incorporating STEM activities into educational curricula to foster scientific thinking from an early age. This study contributes valuable insights to the field of science education and underscores the potential of STEM-based interventions in shaping the future scientific minds of young children.

Keywords: Scientific thinking, education, STEM, intervention, Psychology, Pedagogy, collaborative learning, longitudinal study

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Authors: Tibor Ajtai, Noémi Utry, Máté Pintér, Tomi Smausz, Zoltán Kónya, Béla Hopp, Gábor Szabó, Zoltán Bozóki

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Characterization of spectral responses of light absorbing carbonaceous particulate matter (LAC) is of great importance in both modelling its climate effect and interpreting remote sensing measurement data. The residential or domestic combustion of coal is one of the dominant LAC constituent. According to some related assessments the residential coal burning account for roughly half of anthropogenic BC emitted from fossil fuel burning. Despite of its significance in climate the comprehensive investigation of optical properties of residential coal aerosol is really limited in the literature. There are many reason of that starting from the difficulties associated with the controlled burning conditions of the fuel, through the lack of detailed supplementary proximate and ultimate chemical analysis enforced, the interpretation of the measured optical data, ending with many analytical and methodological difficulties regarding the in-situ measurement of coal aerosol spectral responses. Since the gas matrix of ambient can significantly mask the physicochemical characteristics of the generated coal aerosol the accurate and controlled generation of residential coal particulates is one of the most actual issues in this research area. Most of the laboratory imitation of residential coal combustion is simply based on coal burning in stove with ambient air support allowing one to measure only the apparent spectral feature of the particulates. However, the recently introduced methodology based on a laser ablation of solid coal target opens up novel possibilities to model the real combustion procedure under well controlled laboratory conditions and makes the investigation of the inherent optical properties also possible. Most of the methodology for spectral characterization of LAC is based on transmission measurement made of filter accumulated aerosol or deduced indirectly from parallel measurements of scattering and extinction coefficient using free floating sampling. In the former one the accuracy while in the latter one the sensitivity are liming the applicability of this approaches. Although the scientific community are at the common platform that aerosol-phase PhotoAcoustic Spectroscopy (PAS) is the only method for precise and accurate determination of light absorption by LAC, the PAS based instrumentation for spectral characterization of absorption has only been recently introduced. In this study, the investigation of the inherent, spectral features of laser generated and chemically characterized residential coal aerosols are demonstrated. The experimental set-up and its characteristic for residential coal aerosol generation are introduced here. The optical absorption and the scattering coefficients as well as their wavelength dependency are determined by our state-of-the-art multi wavelength PAS instrument (4λ-PAS) and multi wavelength cosinus sensor (Aurora 3000). The quantified wavelength dependency (AAE and SAE) are deduced from the measured data. Finally, some correlation between the proximate and ultimate chemical as well as the measured or deduced optical parameters are also revealed.

Keywords: absorption, scattering, residential coal, aerosol generation by laser ablation

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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: Elizabeth M. Annoni, Elena G. Tolkacheva

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Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that is a known prognostic marker for stroke, heart failure and death. Reentrant mechanisms of rotor formation, which are stable electrical sources of cardiac excitation, are believed to cause AF. No existing commercial mapping systems have been demonstrated to consistently and accurately predict rotor locations outside of the pulmonary veins in patients with persistent AF. There is a clear need for robust spatio-temporal techniques that can consistently identify rotors using unique characteristics of the electrical recordings at the pivot point that can be applied to clinical intracardiac mapping. Recently, we have developed four new signal analysis approaches – Shannon entropy (SE), Kurtosis (Kt), multi-scale frequency (MSF), and multi-scale entropy (MSE) – to identify the pivot points of rotors. These proposed techniques utilize different cardiac signal characteristics (other than local activation) to uncover the intrinsic complexity of the electrical activity in the rotors, which are not taken into account in current mapping methods. We validated these techniques using high-resolution optical mapping experiments in which direct visualization and identification of rotors in ex-vivo Langendorff-perfused hearts were possible. Episodes of ventricular tachycardia (VT) were induced using burst pacing, and two examples of rotors were used showing 3-sec episodes of a single stationary rotor and figure-8 reentry with one rotor being stationary and one meandering. Movies were captured at a rate of 600 frames per second for 3 sec. with 64x64 pixel resolution. These optical mapping movies were used to evaluate the performance and robustness of SE, Kt, MSF and MSE techniques with respect to the following clinical limitations: different time of recordings, different spatial resolution, and the presence of meandering rotors. To quantitatively compare the results, SE, Kt, MSF and MSE techniques were compared to the “true” rotor(s) identified using the phase map. Accuracy was calculated for each approach as the duration of the time series and spatial resolution were reduced. The time series duration was decreased from its original length of 3 sec, down to 2, 1, and 0.5 sec. The spatial resolution of the original VT episodes was decreased from 64x64 pixels to 32x32, 16x16, and 8x8 pixels by uniformly removing pixels from the optical mapping video.. Our results demonstrate that Kt, MSF and MSE were able to accurately identify the pivot point of the rotor under all three clinical limitations. The MSE approach demonstrated the best overall performance, but Kt was the best in identifying the pivot point of the meandering rotor. Artifacts mildly affect the performance of Kt, MSF and MSE techniques, but had a strong negative impact of the performance of SE. The results of our study motivate further validation of SE, Kt, MSF and MSE techniques using intra-atrial electrograms from paroxysmal and persistent AF patients to see if these approaches can identify pivot points in a clinical setting. More accurate rotor localization could significantly increase the efficacy of catheter ablation to treat AF, resulting in a higher success rate for single procedures.

Keywords: Atrial Fibrillation, Optical Mapping, Signal Processing, Rotors

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Authors: Aleksandra Jonas, Aleksandra Gronowska, Maciej Ścigacz, Szymon Jadczak

Abstract:

Exact Data is a smart tool which helps with meaningful marketing content creation. It helps marketers achieve this by analyzing the text of an advertisement before and after its publication on social media sites like Facebook or Instagram. In our research we focus on four areas of natural language processing (NLP): grammar correction, sentiment analysis, irony detection and advertisement interpretation. Our research has identified a considerable lack of NLP tools for the Polish language, which specifically aid online marketers. In light of this, our research team has set out to create a robust and versatile NLP tool for the Polish language. The primary objective of our research is to develop a tool that can perform a range of language processing tasks in this language, such as sentiment analysis, text classification, text correction and text interpretation. Our team has been working diligently to create a tool that is accurate, reliable, and adaptable to the specific linguistic features of Polish, and that can provide valuable insights for a wide range of marketers needs. In addition to the Polish language version, we are also developing an English version of the tool, which will enable us to expand the reach and impact of our research to a wider audience. Another area of focus in our research involves tackling the challenge of the limited availability of linguistically diverse corpora for non-English languages, which presents a significant barrier in the development of NLP applications. One approach we have been pursuing is the translation of existing English corpora, which would enable us to use the wealth of linguistic resources available in English for other languages. Furthermore, we are looking into other methods, such as gathering language samples from social media platforms. By analyzing the language used in social media posts, we can collect a wide range of data that reflects the unique linguistic characteristics of specific regions and communities, which can then be used to enhance the accuracy and performance of NLP algorithms for non-English languages. In doing so, we hope to broaden the scope and capabilities of NLP applications. Our research focuses on several key NLP techniques including sentiment analysis, text classification, text interpretation and text correction. To ensure that we can achieve the best possible performance for these techniques, we are evaluating and comparing different approaches and strategies for implementing them. We are exploring a range of different methods, including transformers and convolutional neural networks (CNNs), to determine which ones are most effective for different types of NLP tasks. By analyzing the strengths and weaknesses of each approach, we can identify the most effective techniques for specific use cases, and further enhance the performance of our tool. Our research aims to create a tool, which can provide a comprehensive analysis of advertising effectiveness, allowing marketers to identify areas for improvement and optimize their advertising strategies. The results of this study suggest that a smart tool for advertisement analysis can provide valuable insights for businesses seeking to create effective advertising campaigns.

Keywords: NLP, AI, IT, language, marketing, analysis

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Authors: Gopalasingam Daisan

Abstract:

Landmines and ammunitions have been creating a significant threat to the people and animals, after the war, the landmines remain in the land and it plays a vital role in civilian’s security. Especially the Children are at the highest risk because they are curious. After all, an unexploded bomb can look like a tempting toy to an inquisitive child. The initial step of designing the UAS (Unmanned Aircraft Systems) for landmine detection is to choose an appropriate and effective sensor to locate the landmines and other unexploded ammunitions. The sensor weight and other components related to the sensor supporting device’s weight are taken as a payload weight. The mission requirement is to find the landmines in a particular area by making a proper path that will cover all the vicinity in the desired area. The weight estimation of the UAV (Unmanned Aerial Vehicle) can be estimated by various techniques discovered previously with good accuracy at the first phase of the design. The next crucial part of the design is to calculate the power requirement and the wing loading calculations. The matching plot techniques are used to determine the thrust-to-weight ratio, and this technique makes this process not only easiest but also precisely. The wing loading can be calculated easily from the stall equation. After these calculations, the wing area is determined from the wing loading equation and the required power is calculated from the thrust to weight ratio calculations. According to the power requirement, an appropriate engine can be selected from the available engine from the market. And the wing geometric parameter is chosen based on the conceptual sketch. The important steps in the wing design to choose proper aerofoil and which will ensure to create sufficient lift coefficient to satisfy the requirements. The next component is the tail; the tail area and other related parameters can be estimated or calculated to counteract the effect of the wing pitching moment. As the vertical tail design depends on many parameters, the initial sizing only can be done in this phase. The fuselage is another major component, which is selected based on the slenderness ratio, and also the shape is determined on the sensor size to fit it under the fuselage. The landing gear is one of the important components which is selected based on the controllability and stability requirements. The minimum and maximum wheel track and wheelbase can be determined based on the crosswind and overturn angle requirements. The minor components of the landing gear design and estimation are not the focus of this project. Another important task is to calculate the weight of the major components and it is going to be estimated using empirical relations and also the mass is added to each such component. The CG and moment of inertia are also determined to each component separately. The sensitivity of the weight calculation is taken into consideration to avoid extra material requirements and also reduce the cost of the design. Finally, the aircraft performance is calculated, especially the V-n (velocity and load factor) diagram for different flight conditions such as not disturbed and with gust velocity.

Keywords: landmine, UAS, matching plot, optimization

Procedia PDF Downloads 145

Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

Abstract:

Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

Procedia PDF Downloads 39

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

Procedia PDF Downloads 47