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Impact of the Increasing Integration of Photovoltaic Systems and Charging Stations for Electric Vehicles with Vehicle-To-Grid Concept on Voltage Stability of the Distribution Grid 20

Impact of the Increasing Integration of Photovoltaic Systems...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Kljajic, Ruzica Misljenovic, Nemanja Maric, Predrag Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9781665463973

The increasing integration of renewable energy sources (RES) and variable loads can stress the operation of the distribution grid during certain times of the day. In another way, electric vehicles, which are assumed to take an increasing share in the overall transportation structure, are considered as variable loads with the ability to store energy and thus support stability of the distribution grid. This paper focuses on a model of a prosumer-rich distribution grid with increasing integration of photovoltaic (PV) systems, electric vehicle charging stations (EVCS) as well as vehicle-to-grid (V2G) in the foreseeable future. The impact of integration on voltage stability was analyzed for the different operating scenarios considering voltage profiles and characteristic P-V curves. © 2023 IEEE.

关键词： Vehicle-to-grid

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AI-based Secure Intrusion Detection Framework for Digital Twin-enabled Critical Infrastructure 14

AI-based Secure Intrusion Detection Framework for Digital Tw...

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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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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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Fault Classification in Photovoltaic Arrays Using Convolutional Neural Networks with L2 Regularization 2

Fault Classification in Photovoltaic Arrays Using Convolutio...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Pradeep Kumar, S. Umasankar, L. Almusawi, Muntather Surekha, Pothireddy Aluvala, Srinivas Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India The Islamic University Najaf Iraq Griet Department of Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382952

Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of output accuracy of the detection method in complex situations such as PS and high impedance faults. This paper proposes a method for diagnosing PV failures based on the combination of Hybrid Transformer- Convolutional Neural Network (CNN) Architecture framework that is used for the purpose. Proposed method implies an extension of transformers that is capable to capture long-range dependencies of images, and CNNs to extract spatial features. This is because it performs in a higher way which is to achieve better fault classification performance. A contrast with the extant techniques that deal with limited few scenarios of faults, we have faced up to five different faulty scenarios, PS included and have devised the maximum power point tracking (MPPT). The results of this experimental work proved that the implemented hybrid method is much better than the previous methods regarding fault detection, and succeeded in having the accuracy of 88.4% compared to the other methods;and it is a promising one for betterment of the fault diagnosis in the photovoltaic systems. © 2024 IEEE.

关键词： Convolutional neural networks

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A Vision-Based People Identification System Using Gait Recognition for Industrial Environments 22

A Vision-Based People Identification System Using Gait Recog...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Peyron, Jason Ramotsoela, Daniel Hancke, Gerhard Department of Electrical Engineering University of Cape Town Cape Town South Africa Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9798331527471

This paper presents a vision-based people identification system designed for industrial environments, focusing on continuous user identification through gait recognition. Leveraging the CASIA-B gait dataset, a renowned resource in gait recognition research, our system adopts an enhanced spatiotemporal representation known as the Gait Energy Image (GEI), a binary silhouette-based gait representation, as its primary feature. The gait recognition model is built upon a ResNet architecture, integrating all essential components for effective identification. Our system demonstrates superior or comparable performance to existing algorithms in the literature for normal walking styles, with opportunities for further enhancements in the bag sequence identification. © 2024 IEEE.

关键词： Machine vision

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Development of a Smart IOT Enabled Arduino Glove Supporting Sign Language Communication 26

Development of a Smart IOT Enabled Arduino Glove Supporting ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Zaman, Muhammad Shahriar Victor Biswas, Richard Faculty of Science and Technology Department of Computer Science and Engineering Dhaka1229 Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering 408/1 Kuratoli Dhaka1229 Bangladesh

ISBN: (纸本)9798350359015

Deafness is an unfortunate issue of our society impairing many people. Around 3 million people in Bangladesh suffer from partial or complete deafness. One key challenge for these individuals is communicating with regular people. Various sign languages are available but very few people are knowledgeable about it. Keeping this issue in consideration this project has been developed. The smart Arduino glove allows the user to express their ideas by translating their fingerspelling as well as the gestures into comprehensible messages. The system also allows identification of common sentences by identifying simple gestures. The suggested system if applied in daily life can bring a positive change in the lives of deaf-mute individuals. © 2023 IEEE.

关键词： Accelerometer Alphabets Deaf Digits Fingerspelling Flex sensor Gesture Recognition I2C MPU-6050 Mute PAJ7620 Sign language

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Dual-Branch Retrieval Network for Satellite Cloud Image Classification Based on Multilevel Semantic Information

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Lv, Jiezhi Wu, Nan Jin, Wei Fu, Randi Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

The weather system has a profound impact on human activities. Conducting research on satellite cloud image classification can provide critical parameters for weather forecasting, climate analysis, and severe weather detection. However, conventional satellite cloud image classification methods typically neglect higher level semantic constraints and rarely incorporate decision-level adaptive calibration, resulting in confusion among visually similar categories and restricting interpretable, content-based inference. Here, we propose a dual-branch retrieval network with multilevel semantic information (DBR-MSI) to address these gaps. DBR-MSI jointly optimizes high-level semantics (e.g., broad meteorological and surface categories) and low-level semantics (e.g., specific cloud or surface attributes), and we explicitly highlight critical semantic content via a gradient-based attention sharing module. Moreover, a retrieval-based inference approach driven by high-level semantic guidance supports interpretable content reasoning and adaptive decision calibration, which in turn allows the proposed method to deliver enhanced robustness and efficient integration of additional data. Experimental results on two satellite cloud image datasets confirm that DBR-MSI exhibits stronger interpretability and achieves overall accuracy (OA) gains of 1.06% and 0.39% over the best competing methods. © 2004-2012 IEEE.

关键词： Content based retrieval

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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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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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