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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Leo, Hendrik Saddami, Khairun Roslidar, Roslidar Muharar, Rusdha Munadi, Khairul Arnia, Fitri Universitas Syiah Kuala Postgraduate School of Engineering Banda Aceh Indonesia Universitas Syiah Kuala Telematic Research Center Department of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350382914

Obesity is a global health concern associated with severe complications, including heart disease, diabetes, and cancer. Its prevalence has seen a consistent rise, demanding early detection and intervention. The thermal imaging technique has recently been particularly promising in monitoring and early detection of obesity-related diseases. To enable research on obesity detection technology using thermal imaging, a high-quality dataset is essential. Currently, comprehensive thermogram datasets for obesity research are limited. Hence, creating an obesity thermogram dataset is critical to advancing our understanding of obesity and its treatment. In this article, we improved our previous dataset and present an open-access obesity thermogram dataset that supports the development of obesity detection systems. We describe the thermal image acquisition procedures, participant selection, room protocol configuration, and thermal camera setups. These thermal images undergo a pre-processing phase involving color mapping configuration and temperature scaling, ensuring their comparability and usability. The obesity thermogram dataset is made available for non-commercial research, fostering transparency and collaboration in the scientific community. researchers and medical practitioners can access the dataset to train and test artificial intelligence-based obesity recognition algorithms, contributing to developing advanced medical technologies. © 2023 IEEE.

关键词： Diseases

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Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Unsupervised High Impedance Fault Detection Using Autoencode...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Liu, Yingxiang Razeghi-Jahromi, Mohammad Stoupis, James University of Southern California Department of Electrical and Computer Engineering Los Angeles United States Abb Us Research Center Principal Scientist at Electrical Systems Department Raleigh United States Abb Us Research Center Electrical Systems Department Manager Raleigh United States

ISBN: (纸本)9798350320169

Detection of high impedance faults (HIFs) in power distribution networks remains a challenging task. Due to the low current magnitude and diverse characteristics of HIFs, conventional over-current relays are ineffective in detecting such faults. Machine learning-based methods have emerged as a promising solution, but most rely on supervised learning techniques to detect HIF by performing classifications that require a significant amount of HIF data that are difficult to acquire in the real world. As a result, the reliability and generalization of these models are limited when the load profiles and faults are not present in the training data. In this paper, we propose an unsupervised HIF detection framework that employs autoencoder and principal component analysis-based monitoring techniques to identify changes in the correlation structure within the current waveforms that deviate from normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate the superiority of our proposed method over the commercially available HIF detection technique while ensuring high security by avoiding false detections during normal load conditions. © 2023 IEEE.

关键词： Principal component analysis

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Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 348-361页

作者： Tsai, Bo-Yu Diddi, Sandeep Vara Sankar Ko, Li-Wei Wang, Shuu-Jiun Chang, Chi-Yuan Jung, Tzyy-Ping Institute of Biomedical Engineering and the Brain Research Center National Yang Ming Chiao Tung University Hsinchu300 Taiwan Hsinchu300 Taiwan College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering Institute of Biomedical Engineering Brain Research Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan Kaohsiung Medical University Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung807 Taiwan Taipei Veterans General Hospital Taipei11217 Taiwan National Yang Ming Chiao Tung University Brain Research Center School of Medicine Taipei112 Taiwan University of California San Diego Swartz Center for Computational Neuroscience San DiegoCA92093 United States

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c =1-10 for activity-specific BCI applications and a moderat

关键词： Electroencephalography

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Robust Control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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2024 China automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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Performance Analysis of Model Predictive Controllers for Active Cell Balancing of Electric Vehicle Battery 21

Performance Analysis of Model Predictive Controllers for Act...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Ahmed, Afaq Uppal, Ali Arshad Ahmed, Qadeer Rizwan Azam, Muhammad COMSATS University Islamabad Department of Electrical and Computer Engineering Islamabad44000 Pakistan The Ohio State University Center for Automotive Research ColumbusOH43210 United States

ISBN: (纸本)9798331516680

One of the functionalities of a battery management system (BMS) in an Electric Vehicle (EV) is to ensure that charge among cells is equally distributed, potentially leading to enhanced performance and reliability. In this context, Model Predictive Control (MPC) has emerged as a suitable candidate. In this work, a battery pack consisting of two serially connected cells along with the bi-directional buck-boost converter is employed to simulate the balancing phenomena. Moreover, to calculate the mean balancing currents and power losses of active cell balancing architecture (ACBN), a high-fidelity model is considered. The purpose is to rigorously investigate the impact of selecting different cost functions in MPC formulation on the closed-loop performance of ACBN in terms of balancing time tb and average power losses PL. To this end, a nonlinear model predictive control (NMPC) is formulated to perform active balancing, and the problem is subsequently solved in CasADi framework employing interior point optimizer (Ipopt) algorithm. To mimic the realistic conditions, a US06 City current profile is taken as in input and the simulation's results demonstrates a noteworthy 38.9 % disparity in tb and a 4.5 % difference in PL across distinct cost functions, underscoring the need for selecting the optimal cost function within the active cell balancing domain. © 2024 IEEE.

关键词： Predictive control systems

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Surface-dominated conductance scaling in Weyl semimetal NbAs

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npj Computational Materials 2024年第1期10卷 2370-2378页

作者： Sushant Kumar Yi-Hsin Tu Sheng Luo Nicholas A.Lanzillo Tay-Rong Chang Gengchiau Liang Ravishankar Sundararaman Hsin Lin Ching-Tzu Chen Department of Materials Science&Engineering Rensselaer Polytechnic Institute1108th StTroyNY 12180USA IBM Thomas J.Watson Research Center 1101 Kitchawan RoadYorktown HeightsNY 10598USA Department of Physics Cheng Kung UniversityTainan CityTaipei Department of Electrical and Computer Engineering College of Design and EngineeringNational University of SingaporeSingaporeSingapore IBM Research 257 Fuller RoadAlbanyNY 12203USA Institute of Physics Academia SinicaTaipeiTaipei

Protected surface states arising fromnon-trivial bandstructure topology in semimetals can potentially enable advanced device functionalities in compute,memory,interconnect,sensing,and *** necessitates a fundamental understanding of surface-state transport in nanoscale topological ***,we investigate quantum transport in a prototypical topological semimetal NbAs to evaluate the potential of this class of materials for beyond-Cu interconnects in highly-scaled integrated *** density functional theory(DFT)coupled with non-equilibrium Green’s function(NEGF)calculations,we show that the resistance-areaRAproduct in NbAs films decreases with decreasing thickness at the nanometer scale,in contrast to a nearly constant RA product in ideal Cu *** anomalous scaling originates from the disproportionately large number of surface conduction states which dominate the ballistic conductance by up to 70%in NbAs thin *** also show that this favorable RA scaling persists even in the presence of surface defects,in contrast to RA sharply increasing with reducing thickness for films of conventional metals,such as Cu,in the presence of surface *** results underscore the potential of topological semimetals as future back-end-of-line(BEOL)interconnect metals.

关键词： scaling topological conductance

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Prediction of Adsorption and Desorption Isotherms for Atmospheric Water Harvesting 21

Prediction of Adsorption and Desorption Isotherms for Atmosp...

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21st International Learning and Technology Conference, L and T 2024

作者： Kabbaj, Narjisse El-Amin, Mohamed F. Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Nat. Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model's superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies. © 2024 IEEE.

关键词： Water conservation

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Fault-tolerant Safe Control for Water Networks: An Interconnected Switched System Approach ⁎

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IFAC-PapersOnLine 2024年第4期58卷 294-299页

作者： Qingkai Meng Stelios Vrachimis Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Cyprus

This paper investigates the fault-tolerant safe control problem in water network systems in the presence of process and actuator faults. Firstly, based on the established watersafe benchmark, the studied water network is modeled as an interconnected switched system, and a fault-tolerant regulation problem is proposed while ensuring the states of the system remain within safe regions. Secondly, based on the stability theories for interconnected and switched systems, considering the worst cases of leakage and pump faults, a proper state-dependent safety-guaranteed switching law and distributed control policies are designed. Thirdly, the stability and safety of the proposed approach are proved in theory. Finally, simulations are conducted to validate the effectiveness of the proposed approach on a benchmark water transport system.

关键词： water network fault-tolerant control interconnected switched system safe control

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Powering Inaccessible IoT Devices Through a WPT-enabled Sustainable UAV Network

Powering Inaccessible IoT Devices Through a WPT-enabled Sust...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Mekikis, Prodromos-Vasileios Bouzinis, Pavlos Mitsiou, Nikos Tegos, Sotiris Papanikolaou, Vasilis Tyrovolas, Dimitrios Diamantoulakis, Panagiotis Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Cyber Security Systems and Applied AI Research Center Lebanon

ISBN: (纸本)9781665494274

Powering Internet of Things (IoT) devices in hard to reach or hazardous locations could be prohibitive in terms of cost and safety. In this work, we demonstrate a thorough solution that tackles this problem using a network of unmanned aerial vehicles (UAVs) with wireless power transfer (WPT) capabilities. Given the large-scale IoT deployments of the future and the energy needs of the UAVs, we consider an infrastructure of charging stations for the UAVs that allow an uninterrupted and flexible UAV deployment based on the current energy needs of the IoT devices. Then, we exhibit an orchestrator that communicates with the entire infrastructure and handles the UAV traffic and energy decisions, as well as the digital representation and interaction of the network with the user. © 2023 IEEE.

关键词： User interfaces

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Adaptive Autopilot: Constrained Drl for Diverse Driving Behaviors 27

Adaptive Autopilot: Constrained Drl for Diverse Driving Beha...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Selvaraj, Dinesh Cyril Vitale, Christian Panayiotou, Tania Kolios, Panayiotis Chiasserini, Carla Fabiana Ellinas, Georgios University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus Kios Research and Innovation Center of Excellence Department of Computer Science Nicosia Cyprus CARS@Polito and Politecnico di Torino Torino Italy Chalmers University of Technology Sweden

ISBN: (纸本)9798331505929

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier;(ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles;and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles. © 2024 IEEE.

关键词： Deep neural networks

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