Search results for: neural style transfer for edge

Authors: Adriana Martínez-Arias, John Josephraj Selvaraj, Luis Octavio González-Salcedo

Abstract:

In the Colombian Pacific, Dorado fish (Coryphaena hippurus L.) fisheries is of great commercial interest. However, its habitat and fisheries may be affected by climatic change especially by the actual increase in sea surface temperature. Hence, it is of interest to study the dynamics of these species fishing zones. In this study, we developed Artificial Neural Networks (ANN) models to predict Catch per Unit Effort (CPUE) as an indicator of species abundance. The model was based on four oceanographic variables (Chlorophyll a, Sea Surface Temperature, Sea Level Anomaly and Bathymetry) derived from satellite data. CPUE datasets for model training and cross-validation were obtained from logbooks of commercial fishing vessel. Sea surface Temperature for Colombian Pacific were projected under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 using Coupled Model Intercomparison Project Phase 5 (CMIP5) and CPUE maps were created. Our results indicated that an increase in sea surface temperature reduces the potential fishing zones of this species in the Colombian Pacific. We conclude that ANN is a reliable tool for simulation of climate change effects on the potential fishing zones. This research opens a future agenda for other species that have been affected by climate change.

Keywords: climatic change, artificial neural networks, dorado fish, CPUE

Procedia PDF Downloads 232

Authors: Simon Bambey, Edward Lim, Kai Corley-Jory, Pooya Taheri

Abstract:

The main goal of this paper is to propose a simple and low-cost microcontroller-based laser communication link. To demonstrate that laser communication is a viable and efficient means for transmitting data, a transceiver capable of transfer rates of approximately 0.7 kB/s is prototyped. The hardware used for the transceiver consists of Commercial Off-The-Shelf (COTS) lasers, photodiodes, and the Arduino Mega 2560 which is an open-source and easy-to-use microcontroller-based platform intended for making interactive projects. A graphic user interface utilizing the Meteor framework is developed to facilitate the communication between the user and transceiver. The developed transceiver prototype is capable of receiving and transmitting data at significant ranges with no loss of information. Furthermore, stable and secure communication is achieved through several mechanisms developed to manage simultaneous sending and receiving, in addition to detecting physical interruptions during transmission. The design setup is scalable and with further development can be transformed into a fiber-optic transmission system. Due to its nature, laser communication is very secure and can provide a safe and private communication link. Overall, this paper demonstrates how laser communication can be an economical, durable, and effective means of information transfer.

Keywords: Arduino microcontrollers, laser applications, user interfaces, wireless communication

Procedia PDF Downloads 291

Authors: Yu-ding Du, Qi-lian Bao, Nassim Bessaad, Lin Liu

Abstract:

The Global Navigation Satellite System (GNSS) is regarded as an effective approach for the purpose of replacing the large amount used track-side balises in modern train localization systems. This paper describes a method based on the data fusion of a GNSS receiver sensor and an odometer sensor that can significantly improve the positioning accuracy. A digital track map is needed as another sensor to project two-dimensional GNSS position to one-dimensional along-track distance due to the fact that the train’s position can only be constrained on the track. A model trained by BP neural network is used to estimate the trend positioning error which is related to the specific location and proximate processing of the digital track map. Considering that in some conditions the satellite signal failure will lead to the increase of GNSS positioning error, a detection step for GNSS signal is applied. An adaptive weighted fusion algorithm is presented to reduce the standard deviation of train speed measurement. Finally an Extended Kalman Filter (EKF) is used for the fusion of the projected 1-D GNSS positioning data and the 1-D train speed data to get the estimate position. Experimental results suggest that the proposed method performs well, which can reduce positioning error notably.

Keywords: multi-sensor data fusion, train positioning, GNSS, odometer, digital track map, map matching, BP neural network, adaptive weighted fusion, Kalman filter

Procedia PDF Downloads 239

Authors: Asadullah I. Qazi, Mansoor Ahsan, Zahir Ashraf, Uzair Ahmad

Abstract:

System identification of an Unmanned Aerial Vehicle (UAV), to acquire its mathematical model, is a significant step in the process of aircraft flight automation. The need for reliable mathematical model is an established requirement for autopilot design, flight simulator development, aircraft performance appraisal, analysis of aircraft modifications, preflight testing of prototype aircraft and investigation of fatigue life and stress distribution etc.  This research is aimed at system identification of a fixed wing UAV by means of specifically designed flight experiment. The purposely designed flight maneuvers were performed on the UAV and aircraft states were recorded during these flights. Acquired data were preprocessed for noise filtering and bias removal followed by parameter estimation of longitudinal dynamics transfer functions using MATLAB system identification toolbox. Black box identification based transfer function models, in response to elevator and throttle inputs, were estimated using least square error   technique. The identification results show a high confidence level and goodness of fit between the estimated model and actual aircraft response.

Keywords: fixed wing UAV, system identification, black box modeling, longitudinal dynamics, least square error

Procedia PDF Downloads 313

Authors: Sadia Siddiqa, Z. Khan, M. A. Hossain

Abstract:

In this article, the effect of thermal radiation on mixed convection boundary layer flow of a viscous fluid along a highly heated vertical flat plate is considered with varying density and volumetric expansion coefficient. The density of the fluid is assumed to vary exponentially with temperature, however; volumetric expansion coefficient depends linearly on temperature. Boundary layer equations are transformed into convenient form by introducing primitive variable formulations. Solutions of transformed system of equations are obtained numerically through implicit finite difference method along with Gaussian elimination technique. Results are discussed in view of various parameters, like thermal radiation parameter, volumetric expansion parameter and density variation parameter on the wall shear stress and heat transfer rate. It is concluded from the present investigation that increase in volumetric expansion parameter decreases wall shear stress and enhances heat transfer rate.

Keywords: thermal radiation, mixed convection, variable density, variable volumetric expansion coefficient

Procedia PDF Downloads 355

Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

Abstract:

Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

Procedia PDF Downloads 328

Authors: Tim Steinhaus, Christian Beidl

Abstract:

Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modeling the exact interaction remains challenging. In this paper, a novel approach for the determination of the emission-immission interaction on the basis of neural network modeling for traffic induced NO2-immission load within a near-traffic hot-spot scenario is presented. In a detailed sensitivity analysis, the significance of relevant influencing variables on the prevailing NO2 concentration is initially analyzed. Based on this, the generation process of the model is described, in which not only environmental influences but also the vehicle fleet composition including its associated segment- and certification-specific real driving emission factors are derived and used as input quantities. The validity of this approach, which has been presented in the past, is re-examined in this paper using updated data on vehicle emissions and recent immission measurement data. Within the framework of a final scenario analysis, the future development of the immission load is forecast for different developments in the vehicle fleet composition. It is shown that immission levels of less than half of today’s yearly average limit values are technically feasible in hot-spot situations.

Keywords: air quality, emission, emission-immission-interaction, immission, NO2, zero impact

Procedia PDF Downloads 117

Authors: M. Gobinath, S. Balaji

Abstract:

Assumptions in model inputs: Grade of concrete=40 N/mm2 (for slab), Grade of concrete=40 N/mm2 (for shear wall), Grade of Structural steel (plate girder)=350 N/mm2 (yield strength), Ultimate strength of structural steel=490 N/mm2, Grade of rebar=500 N/mm2 (yield strength), Applied Load=1716 kN (un-factored). Following assumptions are made for the mathematical modelling of RCC with steel embedment: (1) The bond between the structural steel and concrete is neglected. (2) The stiffener is provided with shear studs to transfer the shear force. Hence nodal connectivity is established between solid nodes (concrete) and shell elements (stiffener) at those locations. (3) As the end reinforcements transfer either tension/compression, it is modeled as line element and connected to solid nodes. (4) In order to capture the bearing of bottom flange on to the concrete, the line element of plan size of solid equal to the cross section of line elements is connected between solid and shell elements below for bottom flange and above for top flange. (5) As the concrete cannot resist tension at the interface (i.e., between structural steel and RCC), the tensile stiffness is assigned as zero and only compressive stiffness is enabled to take. Hence, non-linear static analysis option is invoked.

Keywords: structure, construction, signature tower, embedment design concept

Procedia PDF Downloads 288

Authors: Parker Murray, Marci Johnson, Tyson S. Burnham, Alina K. Fong, Mark D. Allen, Bruce McIff

Abstract:

Purpose: Pathological dysregulation of Neurovascular Coupling (NVC) caused by mild traumatic brain injury (mTBI) is the predominant source of chronic post-concussion syndrome (PCS) symptomology. fNCI has the ability to localize dysregulation in NVC by measuring blood-oxygen-level-dependent (BOLD) signaling during the performance of fMRI-adapted neuropsychological evaluations. With fNCI, 57 brain areas consistently affected by concussion were identified as PCS neural markers, which were validated on large samples of concussion patients and healthy controls. These neuromarkers provide the basis for a computation of PCS severity which is referred to as the Severity Index Score (SIS). The SIS has proven valuable in making pre-treatment decisions, monitoring treatment efficiency, and assessing long-term stability of outcomes. Methods and Materials: After being scanned while performing various cognitive tasks, 476 concussed patients received an SIS score based on the neural dysregulation of the 57 previously identified brain regions. These scans provide an objective measurement of attentional, subcortical, visual processing, language processing, and executive functioning abilities, which were used as biomarkers for post-concussive neural dysregulation. Initial SIS scores were used to develop individualized therapy incorporating cognitive, occupational, and neuromuscular modalities. These scores were also used to establish pre-treatment benchmarks and measure post-treatment improvement. Results: Changes in SIS were calculated in percent change from pre- to post-treatment. Patients showed a mean improvement of 76.5 percent (σ= 23.3), and 75.7 percent of patients showed at least 60 percent improvement. Longitudinal reassessment of 24 of the patients, measured an average of 7.6 months post-treatment, shows that SIS improvement is maintained and improved, with an average of 90.6 percent improvement from their original scan. Conclusions: fNCI provides a reliable measurement of NVC allowing for identification of concussion pathology. Additionally, fNCI derived SIS scores direct tailored therapy to restore NVC, subsequently resolving chronic PCS resulting from mTBI.

Keywords: concussion, functional magnetic resonance imaging (fMRI), neurovascular coupling (NVC), post-concussion syndrome (PCS)

Procedia PDF Downloads 332

Authors: Shanshan Zhu, Mohammad Nasim

Abstract:

Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

Procedia PDF Downloads 4

Authors: Atitallah Sofien, Bouyahia Olfa, Romdhani Meriam, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

Abstract:

Introduction: Spondylothoracic dysostosis, also known as Jarcho-Levin syndrome, is defined by a shortened neck and thorax, a protruding abdomen, inguinal and umbilical hernias, atypical spinal structure and rib fusion, leading to restricted chest movement or difficulty in breathing, along with urinary tract abnormalities and, potentially severe scoliosis. Aim: This is the case of a patient diagnosed with Jarcho-Levin syndrome, aiming to detail the range of abnormalities observed in this syndrome, the observed complications, and the therapeutic approaches employed. Results: A three-month-old male infant, born of a consanguineous marriage, delivered at full term by cesarean section, was admitted to the pediatric department for severe acute bronchiolitis. In his prenatal history, morphological ultrasound revealed macrosomia, a shortened spine, irregular vertebrae with thickened skin, normal fetal cardiac ultrasound, and the absence of the right kidney. His perinatal history included respiratory distress, requiring ventilatory support for five days. Upon physical examination, he had stunted growth, scoliosis, a short neck and trunk, longer upper limbs compared to lower limbs, varus equinus in the right foot, a neural tube defect, a low hairline, and low-set ears. Spondylothoracic dysostosis was suspected, leading to further investigations, including a normal transfontaneous ultrasound, a spinal cord ultrasound revealing a lipomyelocele-type closed dysraphism with a low-attached cord, an abdominal ultrasound indicating a single left kidney, and a cardiac ultrasound identifying Kommerell syndrome. Due to a lack of resources, genetic testing could not be performed, and the diagnosis was based on clinical criteria. Conclusion: Jarcho-Levin syndrome can result in a mortality rate of about 50%, primarily due to respiratory complications associated with thoracic insufficiency syndrome. Other complications, like heart and neural tube defects, can also lead to premature mortality. Therefore, early diagnosis and comprehensive treatment involving various specialists are essential.

Keywords: Jarcho-Levin syndrome, congenital disorder, scoliosis, spondylothoracic dysostosis, neural tube defect

Procedia PDF Downloads 47

Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

Abstract:

Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

Procedia PDF Downloads 182

Authors: Alexslis N. Maindze

Abstract:

Knowledge management (KM) has become an imperative to modern business growth, competitive edge, and sustainability. Though there has been extensive research in the field, this literature overview showcases massive gaps that exist on the coverage of the field’s rich and fascinating history. Particularly, accounts of the history of KM are inconsistent and fragmentary in breadth and depth. This paper presents new insights into the history of KM from the early 70s when the actual coinage ‘knowledge management’ entered the literature. It reveals how knowledge over the years was shrouded in secrecy and subsumed by technology. It makes a clear distinction between the histories of the debate around knowledge and that of KM. The paper also finds a history of KM filled with skepticisms and engulfed by an ‘intellectual paradox’.

Keywords: knowledge management history, secrecy, skepticism, intellectual paradox

Procedia PDF Downloads 206

Authors: M. Maache, R. Bessaih

Abstract:

In this study, the effect of the magnetic field direction on the free convection heat transfer in a vertical cylinder filled with an Al₂O₃ nanofluid is investigated numerically. The external magnetic field is applied in either direction axial and radial on a cylinder having an aspect ratio H/R0=5, bounded by the top and the bottom disks at temperatures Tc and Th and by an adiabatic side wall. The equations of continuity, Navier Stocks and energy are non-dimensionalized and then discretized by the finite volume method. A computer program based on the SIMPLER algorithm is developed and compared with the numerical results found in the literature. The numerical investigation is carried out for different governing parameters namely: The Hartmann number (Ha=0, 5, 10, …, 40), nanoparticles volume fraction (ϕ=0, 0.025, …,0.1) and Rayleigh number (Ra=103, Ra=104 and Ra=105). The behavior of average Nusselt number, streamlines and temperature contours are illustrated. The results revel that the average Nusselt number increases with an increase of the Rayleigh number but it decreases with an increase in the Hartmann number. Depending on the magnetic field direction and on the values of Hartmann and Rayleigh numbers, an increase of the solid volume fraction may result enhancement or deterioration of the heat transfer performance in the nanofluid.

Keywords: natural convection, nanofluid, magnetic field, vertical cylinder

Procedia PDF Downloads 307

Authors: Chao He, Shunhua Zhou, Peijun Guo

Abstract:

The vast majority of existing underground railway lines consist of twin tunnels. In this paper, the dynamic interaction between two neighboring tunnels in a layered half-space is investigated by an analytical model. The two tunnels are modelled as cylindrical thin shells, while the soil in the form of a layered half-space with two cylindrical cavities is simulated by the elastic continuum theory. The transfer matrix method is first used to derive the relationship between the plane wave vectors in arbitrary layers and the source layer. Thereafter, the wave translation and transformation are introduced to determine the plane and cylindrical wave vectors in the source layer. The solution for the dynamic interaction between twin tunnels in a layered half-space is obtained by means of the compatibility of displacements and equilibrium of stresses on the two tunnel–soil interfaces. By coupling the proposed model with a fully track model, the train-induced vibrations from twin tunnels in a multi-layered half-space are investigated. The numerical results demonstrate that the existence of a neighboring tunnel has a significant effect on ground vibrations.

Keywords: underground railway, twin tunnels, wave translation and transformation, transfer matrix method

Procedia PDF Downloads 102

Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

Abstract:

Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

Procedia PDF Downloads 126

Authors: Lakhdar Rahmani, Benhanifia Kada, Brahim Mebarki

Abstract:

The present paper aims to investigate the natural convection of nanofluid (water-cu) inside a hexagonal enclosure shape embedded with a square obstacle in the presence of hot and cold side walls. The governing equations were solved in a non-uniform unstructured grid by employing the Galerkin finite element method using the software COMSOL Multiphysics. The objective of this study is to analyze the influence of Rayleigh number (103 < Ra < 105), the position of the obstacle, which is located in three different positions (center, bottom, and top side ), and the effect of Nanoparticles volume concentration (0 < Ø < 0.2) on the thermal behavior inside the enclosure, The results are reported as contours of isotherms, streamlines, and average Nusselt numbers. The obtained results illustrate that the increase in the Rayleigh number (Ra) and the Nanoparticles concentration ( Ø ) leads to an increase in the Nusselt number (Nu average ) that signifies the rate of heat transfer in the studied enclosure, in addition to the best performance observed with the position of obstacle that is located at the middle of the enclosure, where has a high effect in improving the heat transfer along the enclosure comparatively with the rest different positions.

Keywords: natural convection, nanofluid (water-Cu), hexagonal enclosure, Nusselt numbers, Rayleigh number

Procedia PDF Downloads 81

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

Abstract:

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

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

Procedia PDF Downloads 276

Authors: Srinivasan Chandrasekaran, R. Nagavinothini

Abstract:

Offshore platforms for ultra-deep waters are form-dominant by design; hybrid systems with large flexibility in horizontal plane and high rigidity in vertical plane are preferred due to functional complexities. Offshore triceratops is relatively a new-generation offshore platform, whose deck is partially isolated from the supporting buoyant legs by ball joints. They allow transfer of partial displacements of buoyant legs to the deck but restrain transfer of rotational response. Buoyant legs are in turn taut-moored to the sea bed using pre-tension tethers. Present study will discuss detailed dynamic analysis and preliminary design of the chosen geometric, which is necessary as a proof of validation for such design applications. A detailed numeric analysis of triceratops at 2400 m water depth under random waves is presented. Preliminary design confirms member-level design requirements under various modes of failure. Tether configuration, proposed in the study confirms no pull-out of tethers as stress variation is comparatively lesser than the yield value. Presented study shall aid offshore engineers and contractors to understand suitability of triceratops, in terms of design and dynamic response behaviour.

Keywords: offshore structures, triceratops, random waves, buoyant legs, preliminary design, dynamic analysis

Procedia PDF Downloads 196

Authors: Farhad Salek, Shahaboddin Resalati

Abstract:

The production of electric vehicles (EVs) featuring lithium-ion battery technology has substantially escalated over the past decade, demonstrating a steady and persistent upward trajectory. The imminent retirement of electric vehicle (EV) batteries after approximately eight years underscores the critical need for their redirection towards recycling, a task complicated by the current inadequacy of recycling infrastructures globally. A potential solution for such concerns involves extending the operational lifespan of electric vehicle (EV) batteries through their utilization in stationary energy storage systems during secondary applications. Such adoptions, however, require addressing the safety concerns associated with batteries’ knee points and thermal runaways. This paper develops an accurate mathematical model representative of the second-life battery packs from a cell-to-pack scale using an equivalent circuit model (ECM) methodology. Neural network algorithms are employed to forecast the degradation parameters based on the EV batteries' aging history to develop a degradation model. The degradation model is integrated with the ECM to reflect the impacts of the cycle aging mechanism on battery parameters during operation. The developed model is tested under real-life load profiles to evaluate the life span of the batteries in various operating conditions. The methodology and the algorithms introduced in this paper can be considered the basis for Battery Management System (BMS) design and techno-economic analysis of such technologies.

Keywords: second life battery, electric vehicles, degradation, neural network

Procedia PDF Downloads 44

Authors: Vishal Kumar Singh

Abstract:

Bioprocess development is a time-constrained activity aimed at harnessing the full potential of culture performance in an ambience that is not natural to cells. Even with the use of chemically defined media and feeds, a significant amount of time is devoted in identifying the apt operating parameters. In addition, the scale-up of these processes is often accompanied by loss of antibody titer and product quality, which further delays the commercialization of the drug product. In such a scenario, the investigation of this disparity of culture performance is done by further experimentation at a smaller scale that is representative of at-scale production bioreactors. These scale-down model developments are also time-intensive. In this study, a computation fluid dynamics-based multi-objective scaling approach has been illustrated to speed up the process transfer. For the implementation of this approach, a transient multiphase water-air system has been studied in Ansys CFX to visualize the air bubble distribution and volumetric mass transfer coefficient (kLa) profiles, followed by the design of experiment based parametric optimization approach to define the operational space. The proposed approach is completely in silico and requires minimum experimentation, thereby rendering a high throughput to the overall process development.

Keywords: bioprocess development, scale up, scale down, computation fluid dynamics, multi-objective, Ansys CFX, design of experiment

Procedia PDF Downloads 70

Authors: Ashkan Vatani, Peter Woodfield, Nam-Trung Nguyen, Dzung Dao

Abstract:

Thermomagnetic convection of a ferrofluid flow induced by the non-uniform magnetic field around a current-carrying wire was theoretically analyzed and experimentally tested. To show this phenomenon, the temperature rise of a hot wire, immersed in DIW and Ferrofluid, as a result of joule heating has been measured using a transient hot-wire technique. When current is applied to the wire, a temperature gradient is imposed on the magnetic fluid resulting in non-uniform magnetic susceptibility of the ferrofluid that results in a non-uniform magnetic body force which makes the ferrofluid flow as a bulk suspension. For the case of the wire immersed in DIW, free convection is the only means of cooling, while for the case of ferrofluid a combination of both free convection and thermomagnetic convection is expected to enhance the heat transfer from the wire beyond that of DIW. Experimental results at different temperatures and for a range of constant currents applied to the wire show that thermomagnetic convection becomes effective for the currents higher than 1.5A at all temperatures. It is observed that the onset of thermomagnetic convection is directly proportional to the current applied to the wire and that the thermomagnetic convection happens much faster than the free convection. Calculations show that a 35% enhancement in heat transfer can be expected for the ferrofluid compared to DIW, for a 3A current applied to the wire.

Keywords: cooling, ferrofluid, thermomagnetic convection, magnetic field

Procedia PDF Downloads 253

Authors: Tieling Xing, Jinqiu Yang, Guoqiang Chen

Abstract:

It is environmentally friendly to use horseradish peroxidase (HRP) instead of the traditional transition metal catalyst for the catalyst of atom transfer radical polymerization (ATRP). Silk fabrics were successfully grafted with hydroxyl-containing acrylate monomer to improve its crease resistance by HRP-catalyzed ATRP method. Taking grafting yield as the evaluation index, single factor tests revealed that the optimum grafting reaction condition was as follow: monomer mass fraction 120-210%(o.w.f), HRP concentration 360-480U/mL, molar ratio of HRP to NaAsc 1:150, reaction temperature 50-60℃, reaction time 24h. Raman spectra showed hydroxyl-containing acrylate monomer were successfully grafted on silk fabrics. SEM figures indicated the surface of grafted silk became rougher, and graft copolymer was distributed evenly on the surface of silk fiber. The crease-resistant recovery property of grafted silk fabric was greatly improved, especially in wet crease recovery angle. The result showed hydroxyl-containing acrylate monomer can be successfully grafted onto silk fabric based on HRP-catalyzed ATRP method.

Keywords: atom transfer radical polymerization, catalysis, horseradish peroxidase, hydroxyl-containing acrylate monomer

Procedia PDF Downloads 136

Authors: Utsav Swarnkar, Rabi Pathak, Rina Maiti

Abstract:

This study aims to investigate the thermoregulatory role of sweating by comprehensively analyzing the evaporation process and its thermal cooling impact on local skin temperature at various time intervals. Traditional experimental methods struggle to fully capture these intricate phenomena. Therefore, numerical simulations play a crucial role in assessing sweat production rates and associated thermal cooling. This research utilizes transient computational fluid dynamics (CFD) to enhance our understanding of the evaporative cooling process on human skin. We conducted a simulation employing the k-w SST turbulence model. This simulation includes a scenario where sweat evaporation occurs over the skin surface, and at particular time intervals, temperatures at different locations have been observed and its effect explained. During this study, sweat evaporation was monitored on the skin surface following the commencement of the simulation. Subsequent to the simulation, various observations were made regarding temperature fluctuations at specific points over time intervals. It was noted that points situated closer to the periphery of the droplets exhibited higher levels of heat transfer and lower temperatures, whereas points within the droplets displayed contrasting trends.

Keywords: CFD, sweat, evaporation, multiphase flow, local heat loss

Procedia PDF Downloads 47

Authors: Srinivas Bathini, Duraichelvan Raju, Simona Badilescu, Muthukumaran Packirisamy

Abstract:

A bio-sensing method, based on the plasmonic property of gold nano-islands, has been developed for detection of exosomes in a clinical setting. The position of the gold plasmon band in the UV-Visible spectrum depends on the size and shape of gold nanoparticles as well as on the surrounding environment. By adsorbing various chemical entities, or binding them, the gold plasmon band will shift toward longer wavelengths and the shift is proportional to the concentration. Exosomes transport cargoes of molecules and genetic materials to proximal and distal cells. Presently, the standard method for their isolation and quantification from body fluids is by ultracentrifugation, not a practical method to be implemented in a clinical setting. Thus, a versatile and cutting-edge platform is required to selectively detect and isolate exosomes for further analysis at clinical level. The new sensing protocol, instead of antibodies, makes use of a specially synthesized polypeptide (Vn96), to capture and quantify the exosomes from different media, by binding the heat shock proteins from exosomes. The protocol has been established and optimized by using a glass substrate, in order to facilitate the next stage, namely the transfer of the protocol to a microfluidic environment. After each step of the protocol, the UV-Vis spectrum was recorded and the position of gold Localized Surface Plasmon Resonance (LSPR) band was measured. The sensing process was modelled, taking into account the characteristics of the nano-island structure, prepared by thermal convection and annealing. The optimal molar ratios of the most important chemical entities, involved in the detection of exosomes were calculated as well. Indeed, it was found that the results of the sensing process depend on the two major steps: the molar ratios of streptavidin to biotin-PEG-Vn96 and, the final step, the capture of exosomes by the biotin-PEG-Vn96 complex. The microfluidic device designed for sensing of exosomes consists of a glass substrate, sealed by a PDMS layer that contains the channel and a collecting chamber. In the device, the solutions of linker, cross-linker, etc., are pumped over the gold nano-islands and an Ocean Optics spectrometer is used to measure the position of the Au plasmon band at each step of the sensing. The experiments have shown that the shift of the Au LSPR band is proportional to the concentration of exosomes and, thereby, exosomes can be accurately quantified. An important advantage of the method is the ability to discriminate between exosomes having different origins.

Keywords: exosomes, gold nano-islands, microfluidics, plasmonic biosensing

Procedia PDF Downloads 161

Authors: P. Charvin, R. Bourrou, F. Lemont, C. Lafon, A. Russello

Abstract:

For nuclear waste treatment and confinement, a specific IN-CAN melting module based on low-frequency induction heating have been designed. The frequency of 50Hz has been chosen to improve penetration length through metal. In this design, the liquid metal, strongly stirred by electromagnetic effects, presents shape of a dome caused by strong Laplace forces developing in the bulk of bath. Because of a lower density, the glass phase is located above the metal phase and is heated and stirred by metal through interface. Electric parameters (Intensity, frequency) give precious information about metal load and composition (resistivity of alloy) through impedance modification. Then, power supply can be adapted to energy transfer efficiency for suitable process supervision. Modeling of this system allows prediction of metal dome shape (in agreement with experimental measurement with a specific device), glass and metal velocity, heat and motion transfer through interface. MHD modeling is achieved with COMSOL and Fluent. First, a simplified model is used to obtain the shape of the metal dome. Then the shape is fixed to calculate the fluid flow and the thermal part.

Keywords: electromagnetic stirring, induction heating, interface modeling, metal load

Procedia PDF Downloads 249

Authors: Wedad Albalawi

Abstract:

The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is defined as a closed subset contains real numbers. Then the inequalities of time scales version have received a lot of attention and has had a major field in both pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on double integrals to obtain new time-scale inequalities of Copson driven by Steklov operator. They will be applied in the solution of the Cauchy problem for the wave equation. The proof can be done by introducing restriction on the operator in several cases. In addition, the obtained inequalities done by using some concepts in time scale version such as time scales calculus, theorem of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of Hardy, inequality of Coposon, Steklov operator

Procedia PDF Downloads 61

Authors: Ira Slabodar

Abstract:

Autonomous learning in English as a Foreign Language (EFL) refers to the use of target language, learner collaboration and students’ responsibility for their learning. Teachers play a vital role of mediators and facilitators in self-regulated method. Thus, their perception of self-guided practices dictates their implementation of this approach. While research has predominantly focused on inadequate administration of autonomous learning in school mostly due to lack of appropriate teacher training, this study examined whether novice teachers who were exposed to extensive autonomous practices were likely to implement this method in their teaching. Twelve novice teachers were interviewed to examine their perception of learner autonomy and their administration of this method. It was found that three-thirds of the respondents experienced a gap between familiarity with autonomous learning and a favorable attitude to this approach and their deficient integration of self-directed learning. Although learner-related and institution-oriented factors played a role in this gap, it was mostly caused by the respondents’ not being genuinely autonomous. This may be due to indirect exposure rather than explicit introduction of the learner autonomy approach. The insights of this research may assist curriculum designers and heads of teacher training programs to rethink course composition to guarantee the transfer of methodologies into EFL classes.

Keywords: learner autonomy, teacher training, english as a foreign language (efl), genuinely autonomous teachers, explicit instruction, self-determination theory

Procedia PDF Downloads 45

Authors: Behnam Habibi Dargah

Abstract:

The economics of cost-benefit theory seeks to monitor claims and determine their final price. The cost of litigation is important because it is a measure of the efficiency of the justice system. From an economic point of view, the cost of litigation is considered to be the point of equilibrium of litigation, whereby litigation is regarded as a high-risk investment and is initiated when the costs are less than the probable and expected benefits. Costs are economically separated into private and social costs. Private cost includes material (direct and indirect) and spiritual costs. The social costs of litigation are also subsidized-centric due to the public and governmental nature of litigation and cover both types of bureaucratic bureaucracy and the costs of judicial misconduct. Macroeconomic policy in the economics of justice is the reverse engineering of controlling the social costs of litigation by employing selective litigation and working on the judicial culture to achieve rationality in the monopoly system. Procedures for controlling and managing court costs are also circumscribed to economic patterns in the field. Rational cost allocation model and cost transfer model. The rational allocation model deals with cost-tolerance systems, and the transfer model also considers three models of transferability, including legal, judicial and contractual transferability, which will be described and explored in the present article in a comparative manner.

Keywords: cost of litigation, economics of litigation, private cost, social cost, cost of litigation

Procedia PDF Downloads 116

Authors: J. I. Corcoles, J. D. Moya-Rico, A. Molina, J. F. Belmonte, J. A. Almendros-Ibanez

Abstract:

One of the most common heat exchangers technology in engineering processes is the use of double-pipe heat exchangers (DPHx), mainly in the food industry. To improve the heat transfer performance, several passive geometrical devices can be used, such as the wall corrugation of tubes, which increases the wet perimeter maintaining a constant cross-section area, increasing consequently the convective surface area. It contributes to enhance heat transfer in forced convection, promoting secondary recirculating flows. One of the most extended tools to analyse heat exchangers' efficiency is the use of computational fluid dynamic techniques (CFD), a complementary activity to the experimental studies as well as a previous step for the design of heat exchangers. In this study, a double pipe heat exchanger behaviour with two different inner tubes, smooth and spirally corrugated tube, have been analysed. Hence, experimental analysis and steady 3-D numerical simulations using the commercial code ANSYS Workbench v. 17.0 are carried out to analyse the influence of geometrical parameters for spirally corrugated tubes at turbulent flow. To validate the numerical results, an experimental setup has been used. To heat up or cool down the cold fluid as it passes through the heat exchanger, the installation includes heating and cooling loops served by an electric boiler with a heating capacity of 72 kW and a chiller, with a cooling capacity of 48 kW. Two tests have been carried out for the smooth tube and for the corrugated one. In all the tests, the hot fluid has a constant flowrate of 50 l/min and inlet temperature of 59.5°C. For the cold fluid, the flowrate range from 25 l/min (Test 1) and 30 l/min (Test 2) with an inlet temperature of 22.1°C. The heat exchanger is made of stainless steel, with an external diameter of 35 mm and wall thickness of 1.5 mm. Both inner tubes have an external diameter of 24 mm and 1 mm thickness of stainless steel with a length of 2.8 m. The corrugated tube has a corrugation height (H) of 1.1 mm and helical pitch (P) of 25 mm. It is characterized using three non-dimensional parameters, the ratio of the corrugation shape and the diameter (H/D), the helical pitch (P/D) and the severity index (SI = H²/P x D). The results showed good agreement between the numerical and the experimental results. Hence, the lowest differences were shown for the fluid temperatures. In all the analysed tests and for both analysed tubes, the temperature obtained numerically was slightly higher than the experimental results, with values ranged between 0.1% and 0.7%. Regarding the pressure drop, the maximum differences between the values obtained numerically, and the experimental values were close to 16%. Based on the experimental and the numerical results, for the corrugated tube, it can be highlighted that the temperature difference between the inlet and the outlet of the cold fluid is 42%, higher than the smooth tube.

Keywords: corrugated tube, heat exchanger, heat transfer, numerical simulation

Procedia PDF Downloads 132