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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Patel, Tanisha Jadav, Nilesh Kumar Rathod, Tejal Tanwar, Sudeep Garg, Deepak Shahinzadeh, Hossein Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat Ahmedabad India SR University School of Computer Science and Artificial Intelligence Warangal506371 India Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350349412

This paper discusses the importance of securing modern society's critical infrastructure in the face of physical and digital threats. It emphasizes the vulnerabilities introduced by programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems that highlight the potential of digital twins to enhance security. The paper presents a research contribution aimed at improving the integrity of data exchange among PLCs equipped with digital twins in critical infrastructure systems. It leverages artificial intelligence (AI) algorithms to detect and mitigate different security attacks, such as denial of service (DoS), injection attacks, and data tampering attacks. The proposed framework efficiently classifies the malicious and non-malicious data of digital twin-based critical infrastructure. Further, the trained AI model is deployed on a critical infrastructure's intrusion detection system that continuously monitors network traffic and system logs in real time, identifying unusual patterns or behaviors that may indicate an intrusion. Further, the performance of the proposed framework is evaluated using accuracy, log loss, and validation curves. © 2023 IEEE.

关键词： Critical infrastructures

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Vulnerability Analysis of Deep Learning Model for OCTA Image Classification 5th

Vulnerability Analysis of Deep Learning Model for OCTA Ima...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Trivedi, Munesh Chandra Bharadwaj, Siddhant Department of Computer Science and Engineering Pandit Sunderlal Sharma Central Institute of Vocational Education Bhopal India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal India

ISBN: (纸本)9789819761029

The prevalence of Deep Learning in healthcare has revolutionized medical diagnostics, yet the vulnerability of these models to adversarial attacks threatens their security. This study investigates the susceptibility of a sophisticated deep learning model, trained to classify OCTA images as healthy or diabetic, to adversarial perturbations. Despite achieving high accuracy on unperturbed data, the model remains vulnerable to adversarial noise. The study employs techniques like Project Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) to generate adversarial examples and tests their efficacy against the model. Results show that even minor perturbations can lead to misclassification, emphasizing the need for adversarial robustness in healthcare models. As healthcare decisions are critical, incorporating adversarial training is must to mitigate the impact of adversarial vulnerabilities in deep learning-based medical diagnoses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Dynamic modelling and optimal operation of the photovoltaic/thermal systems under various weather conditions

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2024年第6期22卷 82-86页

作者： Sredenšek, K. Simonič, E. Seme, S. Faculty of Energy Technology University of Maribor Hočevarjev trg 1 Krško8270 Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia

This paper aims to introduce a dynamic model for a photovoltaic/thermal system, highlighting an approach and synthesis of existing dynamic models to depict the full photovoltaic/thermal system. The model was validated using data from an experimental setup at the Institute of Energy Technology, Faculty of Energy Technology, University of Maribor, under different weather conditions. The model's accuracy was assessed through normalized Root Mean Square Error, normalized Mean Bias Error, Correlation Coefficient, and the coefficient of determination error metrics, focusing on the temperature and electrical output power of the photovoltaic/thermal modules, as well as the temperature of the thermal energy storage tank. Furthermore, the paper also focuses on the optimal operation of the photovoltaic/thermal system under different mass flow rates of the working medium. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： dynamic modelling electrical and thermal energy experimental system the photovoltaic/thermal system

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Extrinsic and Temporal Calibration of Automotive Radar and 3-D LiDAR in Factory and On-Road Calibration Settings

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2023年 4卷 708-719页

作者： Lee, Chia-Le Hou, Chun-Yu Wang, Chieh-Chih Lin, Wen-Chieh Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu31040 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan

While automotive radars are widely used in ADAS and autonomous driving, extrinsic and temporal calibration of automotive radars with other sensors is still daunting due to the sparsity, uncertainty, and missing elevation angles of automotive radar measurements. We propose a target-based calibration approach of 3D automotive radar and 3D LiDAR that performs extrinsic and temporal calibration in both factory and on-road settings. In factory calibration settings, a map is built with precise target poses;target trajectories are estimated based on map-based target localization in which the accuracy of both nearby and faraway target pose estimates can be ensured. The spatial and temporal relationships between radar and LiDAR measurements are established with target trajectories to accomplish extrinsic and temporal calibration. The proposed data collection procedure provides sufficient motion for analyzing time delay between sensors and can significantly reduce the data collection effort and time. There is 52.3% distance error reduction after time delay compensation in the experiment, which shows the improvements of temporal calibration. In on-road calibration settings, the metal objects with semantic labels, such as traffic signs, are selected as calibration targets. Although there could be insufficient correspondences to infer the missing dimension of planar radar for six DoF extrinsic calibration as demonstrated in factory calibration settings, the three extrinsic parameters and the time delay are shown still to be accurate. We validated the proposed method using the nuScenes datasets, which provide sensor measurements, poses, and HD map. With twenty-two data logs, each has over 1000 correspondences, the result of extrinsic parameters reaches centimeter-level accuracy compared with the offered benchmark. The time delay compensation improves 1 meter error for radar tracking in a 20 m/s vehicle case and improves mapping quality in real world data. © 2020 IEEE.

关键词： Calibration

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Recent Advances in Single-Atom Catalysts for Photoelectrocatalytic Water Splitting

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Carbon Energy 2025年第4期7卷 114-142页

作者： Jiao Yang Xiaoyang Zheng Syed Shoaib Ahmad Shah Chao Wang Xueyao Li Zhishuo Yan Lishan Peng Key Laboratory of Rare Earths Ganjiang Innovation Academy Chinese Academy of Sciences Institute of Applied Physics and Materials Engineering University of Macau Department of Mechanical Engineering Graduate School of Engineering The University of Tokyo Department of Chemistry School of Natural Sciences National University of Sciences and Technology Department of Chemistry University of Connecticut School of Rare Earths University of Science and Technology of China Department of Electrical and Computer Engineering North Dakota State University

Hydrogen is a highly promising energy carrier because of its renewable and clean qualities. Among the different methods for H2production, photoelectrocatalysis（PEC） water splitting has garnered significant interest, thanks to the abundant and perennial solar energy. Single-atom catalysts（SACs）, which feature well-distributed atoms anchored on supports, have gained great attention in PEC water splitting for their unique advantages in overcoming the limitations of conventional PEC ***, we comprehensively review SAC-incorporated photoelectrocatalysts for efficient PEC water splitting. We begin by highlighting the benefits of SACs in improving charge transfer, catalytic selectivity, and catalytic activity, which address the limitations of conventional PEC reactions. Next, we provide a comprehensive overview of established synthetic techniques for optimizing the properties of SACs, along with modern characterization methods to confirm their unique structures. Finally, we discuss the challenges and future directions in basic research and advancements, providing insights and guidance for this developing field.

关键词： hydrogen production photoelectrocatalysis single-atom catalysts water splitting

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A Novel DoA Algorithm for Coherent Signals with Asymmetric Multipath Phase Delays Based on a Modified Toeplitz Matrix Reconstruction

A Novel DoA Algorithm for Coherent Signals with Asymmetric M...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Dawod, Firas Slewa Roy, Sayan Dept. of Electrical Engineering and Computer Science South Dakota Mines Rapid CitySD57701 United States

ISBN: (纸本)9781665442282

This paper presents a new direction of arrival (DoA) method based on a modified Toeplitz matrix reconstruction (NMTMR) algorithm. The proposed method overcomes one of the primary challenges of the traditional Toeplitz DoA algorithm, the asymmetric multipath phase delay scenarios. Unlike the conventional Toeplitz algorithm, the NMTMR algorithm can maintain the diagonality of the signal covariance matrix (Rss) and hence can retain the full rank of the array covariance matrix (Rc) under different multipath phase delay scenarios. Specifically, the proposed NMTMR algorithm incorporates some pre- and post-signal processing and reconstruction steps to omit the elements that contributed to the deterioration of the diagonality of the signal covariance matrix (Rss). A successful demonstration of the proposed algorithm has also been presented using MATLAB simulation to validate the viability of the algorithm, albeit with the cost of increasing the number of the required antennas. © 2023 IEEE.

关键词： Direction of arrival

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A Traffic Sign Detection Technique Using Road Scene Images and GPS/GIS Information

A Traffic Sign Detection Technique Using Road Scene Images a...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yang, Sung-Chi Lin, Huei-Yung Fan, Yu-Hsiang Wei, Shih-Han National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

ISBN: (纸本)9781665410205

With the advancement of computational intelligence, autonomous driving has become the future development trend of the automotive industry. Since the safety is commonly considered as the first priority of self-driving and driver assistance systems, the understanding of transportation infrastructure is an essential problem. In this paper, a technique for traffic sign detection and recognition is proposed. Different from the general image-based methods, we also incorporate the vehicle position and geographic information to improve the accuracy. Based on the approximate location of a traffic sign obtained from GPS and GIS, it can be used to increase the confidence level of network detection results. In the experiments, the training datasets are derived from Google Street View images and collected with an in-vehicle camera. The performance evaluation compared to the image-only methods has demonstrated the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Automotive industry

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Constrained Dynamical Neural ODE for Time Series Modelling: A Case Study on Continuous Emotion Prediction 48

Constrained Dynamical Neural ODE for Time Series Modelling: ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Dang, Ting Dimitriadis, Antoni Wu, Jingyao Sethu, Vidhyasaharan Ambikairajah, Eliathamby University of Cambridge Department of Computer Science and Technology United Kingdom Unsw School of Electrical Engineering and Telecommunications Sydney Australia

ISBN: (纸本)9781728163277

weA number of machine learning applications involve time series prediction, and in some cases additional information about dynamical constraints on the target time series may be available. For instance, it might be known that the desired quantity cannot change faster than some rate or that the rate is dependent on some known factors. However, incorporating these constraints into deep learning models, such as recurrent neural networks, is not straightforward. In this paper, we propose constrained dynamical neural ordinary differential equation (CD-NODE) models, which treat the desired time series as a dynamic process that can be described by an ODE. CD-NODEs model the rate of change of the time series as a function of both itself and the current input features, parameterised as a neural network. We explore the effect of constraining the dynamics of the model by placing explicit restrictions on the rate of change. The proposed model is evaluated on speech-based continuous emotion prediction, where such dynamical constraints are expected, using the publicly available RECOLA dataset. Results suggest that the model achieves performances comparable with the state-of-the-art despite using significantly fewer parameters. Additional analyses reveal that imposing these constraints on the model leads to faster convergence and better performance, especially with smaller training data sets. © 2023 IEEE.

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The impact of different prosumer configurations on achieving savings due to the increase in electricity prices 4

The impact of different prosumer configurations on achieving...

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Simic, Zvonimir Misljenovic, Nemanja Dubravac, Marina Topic, Danijel Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350323597

This paper investigates the consumption, and the cost of electricity for different configurations of prosumers in Croatia and additionally considers the current increase in electricity prices. In April 2022, electricity prices for households increased by 15%. Furthermore, from October 2022, electricity prices for households with annual electricity consumption above 5000 kWh are 50% higher than for households with annual electricity consumption below 5000 kWh. The load shifting from a period with a higher price to a period with a lower price can reduce electricity costs but cannot reduce the total amount of electricity imported from the distribution grid. Therefore, there is a possibility to install a PV power plant to meet consumers' own needs for electricity and to reduce the total amount of electricity imported from the distribution grid. Prosumers with different configurations regarding a PV power plant, battery energy storage, and optimal load shifting are simulated, and the total amount of electricity imported from the grid and total electricity costs are compared. According to simulation results, a prosumer with a PV power plant can reduce electricity consumption below 5000 kWh. On the other side, a prosumer with a PV power plant, battery energy storage, and optimal load shifting can significantly reduce total electricity costs. © 2023 IEEE.

关键词： Electric batteries

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