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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

ISBN: (纸本)9781665490719

Despite using Community Detection Algorithms (CDA) in various network partitioning real-world applications, these algorithms tend to fail to partition complex weighted functional networks such as power grids. When contingency events occur, appropriate power grid partitioning enables an effective distributed control strategy to detect system faults and cyberattacks and accelerate electrical assets recovery through fast remedial control. This paper addresses the CDA by considering the functional structure of the power grid. When comparing the topological and functional structures of the power grid network, electrical Coupling Strength (ECS) reflects both direct and indirect power flows, unlike a conventional adjacency matrix, which only considers an unweighted direct connection between buses. The proposed Improved Louvain Algorithm (ILA) employs the ECS patterns to represent the network connection. Here, the quality function is modified as electrical modularity Qe to assess the goodness of the produced clusters. Large Qe indicates that the power transmission within the partition is dense compared to power transmission with other partitions. The algorithm has been tested on IEEE 39-bus and 118-bus systems. Using different case scenarios, the simulation results demonstrate the high effectiveness of the proposed ILA to the traditional Louvain algorithm in partitioning the complex networked electrical grid into adequate subsystems. © 2023 IEEE.

关键词： Intrusion detection

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IoT-Based Car Safety System With Airbag Notification for Emergency Assistance 2

IoT-Based Car Safety System With Airbag Notification for Eme...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Ramalingam, L. Muthumarilakshmi, S. Venkatesh, M. Jambulingam, Umamagewaran Malathi, N. School of Computer Science and Engineering Sriram Engineering College Perumalpattu Tamil Nadu Chennai India Department of Computer Science and Engineering S.A. Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The development of Internet of Things (IoT) technology has provided the potential for creative uses in many fields, including car safety systems. This research presents an IoT-based automotive safety system that intends to improve the safety of passengers during accidents by providing quick airbag deployment notification and assistance in an emergency. The system uses a network of in-car communication modules, actuators, and sensors. These sensors continually monitor several parameters, including impact intensity, deceleration, and acceleration. The technology senses the force of a collision and activates the airbags, significantly lowering the potential for serious injury. Additionally, the IoT-based system instantly notifies emergency response agencies of the collision, including the vehicle's position, the collision's severity, and if it is occupants. This makes it possible for first responders to respond swiftly and provide the occupants with quick assistance. The technology uses Wi-Fi communication protocols to send data between the car and emergency response services, ensuring a reliable and real-time connection. The collected data is securely transmitted and stored on a cloud-based platform, enabling effective analysis and historical accident tracking. Reduced response times, increased occupant safety, and better accident data collecting for further research and preventive measures are just several advantages of the proposed IoT-based automobile safety system with airbag notification for emergency help. IoT technology in automobile safety systems brings new benefits for enhancing general road safety and emergency response capabilities. © 2023 IEEE.

关键词： Airbag deployment Airbag notification Car safety system Internet of Things Sensors

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Regression based Prediction of Rainfall for Energy Management in a Rural Islanded Micro-Hydro Grid in Kerala

Regression based Prediction of Rainfall for Energy Managemen...

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2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022

作者： Valsan, Vipina Sai, A.M. Abhishek Devidas, Aryadevi Remanidevi Ramesh, Maneesha Vinodini Department of Electrical and Electronics Engineering Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9781665493659

ISBN: (纸本)9781665493659

Rural development is one of the primary goals in developing nations like India since more than 70 percent of the population lives in rural villages. This paper aims to develop and implement an effective energy management strategy for a stand-alone Amrita Micro-Hydro Power Plant(AMHPP) set up in a remote tribal community in Kerala. The Energy management strategy is implemented by the development of regression-based machine learning models for the prediction of rainfall, thereby deriving the water flow rate and power generation at AMHPP. Five regression-based machine learning models were compared to find the optimal model to increase the asset utilization of the hydro-micro grid system. The performance of prediction models based on daily and monthly data is assessed using the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). In terms of the various types of regression, including linear, lasso, ridge, XGBoost, and elastic net, the machine learning technique XGBoost regression produced a comparably accurate prediction model. The proposed energy management strategy based on the XGBoost regression model can increase the micro-hydro system's real-time power utilization efficiency w $\mathbf{h}$ ile balancing the available hyd $\mathbf{r}$ oenergy and load management for the micro-grid system. © 2022 IEEE.

关键词： Forecasting

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Layout Optimization of Shortcuts in the Sortation Conveyor Systems

Layout Optimization of Shortcuts in the Sortation Conveyor S...

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2023 International Automatic Control Conference, CACS 2023

作者： Horng, Shih-Cheng Lin, Shieh-Shing Huang, Wei-Hao Chaoyang University of Technology Department of Computer Science & Information Engineering Taichung Taiwan St. John's University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Sortation conveyor systems are used across various industries to help streamline the direction of objects from one conveyor line to another. Sortation conveyor systems are best suited for high throughput operations that generally receive mixed unit loads and discharge them to outfeed conveyors or designated locations. A shortcut provides extra paths to quickly sort parcels to destinations without long deliveries on the main conveyor. The main conveyor of the loop is unidirectional, while the shortcuts' conveyor is bidirectional. In this paper, an algorithm compounding ordinal optimization (OO) and binary seagull optimization (BSO) is presented to solve the layout optimization of shortcuts in sortation conveyor systems. BSO is a nature inspired algorithm that imitates the habits of food-hunting seagulls. The developed algorithm is used to find the layout optimization of shortcuts such that the capacity of the sortation conveyor systems is maximized. Simulation results reveal the remarkable advantages of the proposed algorithm in reaching diverse optimal designs and improving convergence speed. © 2023 IEEE.

关键词： Conveyors

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Real Time Flower Identification by Artificial Intelligence 4

Real Time Flower Identification by Artificial Intelligence

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4th International Conference on Advances in electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Joy, Taushik Ahmed Akter, Samia Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Bashundhara Dhaka1229 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty of these flowers often remains unexplored due to our limited knowledge about their names and attributes. To address this, a project was initiated to close this gap and acquaint people with these unfamiliar yet frequently encountered blooms. Our endeavor involves an innovative mobile application that employs real-time camera recognition to identify flowers, powered by neural networks, particularly the Tensorflow-based image classifier on the Android platform. Machine learning's expansive applications in computer science have propelled our interest in this arena, specifically focusing on Convolutional Neural Networks (CNN) and Tensorflow for image classification. While our current application marks the inception, aspiration to further enrich and refine our system for the future. Our ultimate aim is to share the benefits of our work, enabling individuals to gain profound insights into the enchanting floral world that envelops them daily. © 2024 IEEE.

关键词： Artificial Intelligence Data science Deep Learning Machine Learning Monitoring Smartphone Sensors

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Line-Start Permanent Magnet Machines for Low-Power Applications

Line-Start Permanent Magnet Machines for Low-Power Applicati...

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2024 International Conference on electrical Machines, ICEM 2024

作者： Barta, Jan Toman, Marek Losak, Petr Aarniovuori, Lassi Vitek, Ondrej Brno University of Technology Department of Power Electrical and Electronic Engineering Brno Czech Republic Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Electrical Engineering LUT University Lahti Finland

ISBN: (纸本)9798350370607

The improvement of efficiency is one of the main areas of research in electrical machines today. Three-phase induction machines are widely used in industry, motivating researchers to explore methods to increase their efficiency or replace them with more efficient alternatives. A line-start synchronous machine is a promising machine design for achieving high-efficiency classes even for low-power applications. The main contribution of this paper is to show the achievable performance of the LSPMSM by simply replacing the rotor of an induction machine. In this work, two different geometries suitable for low-power machines are optimized and studied in terms of electromagnetic parameters. Finally, the results of the start-up simulations for both machine designs are presented. The presented method shows that by replacing the induction machine rotor with a line-start permanent magnet rotor, it is possible to significantly enhance machine efficiency, even for low-power applications. © 2024 IEEE.

关键词： Permanent magnets

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Version Age-Optimal Cached Status Updates in a Gossiping Network with Energy Harvesting Sensor 21

Version Age-Optimal Cached Status Updates in a Gossiping Net...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Delfani, Erfan Pappas, Nikolaos Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer and Information Science Linköping University Linköping Sweden

ISBN: (纸本)9783903176553

In this work, we consider a real-time IoT monitoring system in which an energy harvesting sensor with a finite-size battery measures a physical process and transmits the status updates to an aggregator. The aggregator, equipped with caching capabilities, can serve the external requests of a destination network with either a stored update or a fresh update from the sensor. We assume that the destination network act as a gossiping network in which the update packets are forwarded among the nodes in a randomized setting. We utilize the Markov Decision Process framework to model and optimize the network’s average Version Age of Information (AoI) and obtain the optimal policy at the aggregator. The structure of the optimal policy is analytically demonstrated and numerically verified. Numerical results highlight the effect of the system parameters on the average Version AoI. The simulations reveal the superior performance of the optimal policy compared to a set of baseline policies. © 2023 IFIP.

关键词： Energy harvesting

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Deep learning-based few-shot person re-identification from top-view RGB and depth images

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Neural Computing and Applications 2024年第31期36卷 19365-19382页

作者： Abed, Almustafa Akrout, Belhassen Amous, Ikram Miracl Laboratory Enet’com University of Sfax National School of Electronics and Telecommunications of Sfax Road Tunis City El Ons Sfax3018 Tunisia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Sfax University Sfax3021 Tunisia

Person re-identification (re-id) attempts to match a person from the images of different time steps. Existing deep learning approaches either use appearance or geometry features for re-id which does not provide the required robustness because of higher intra-class similarity. Existing supervised re-id approaches utilize Convolutional Neural Networks (CNNs) and identity-labeled images to train, where the person images are taken by the sensors from a horizontal view. The horizontal view exposes the privacy of the people because of their facial appearance in the image. Moreover, person re-id includes new unseen people;however, CNN does not have the ability to identify the new unseen people because of a lack of continual learning. Privacy-preserved computer vision-assisted person re-id systems can benefit from visual appearance and geometry features extracted from top-view RGB and depth input. This paper presents the privacy-preserved person top-view re-id few-shot network which uses the appearance and geometry features. The EfficientNet is used for appearance-based features from RGB input, while PointNet is used to extract the geometry features from the point cloud which is made from the RGB-D image registration. Concatenated features from EfficientNet and PointNet are fed to the two-layer Bi-LSTM network for person identification. Finally, the whole network is converted into a few-shot network to achieve continual learning by removing the output layer and joining the similarity measurement unit. This approach is based on CNN and fine-tunes a TVPR/2 dataset acquired by using a top-view arrangement that is publicly available. The experimental results on TVPR/2 and GODPR datasets show that the proposed re-id network outperforms other state-of-the-art networks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Retail stores

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Optimal Deployment of Heterogeneous Visual Sensor Networks for Target Coverage and Feature Aggregation with Lifetime Guarantee 21

Optimal Deployment of Heterogeneous Visual Sensor Networks f...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Zhu, Xiaojian Zhou, MengChu School of Computer Science and Engineering Changshu Institute of Technology Changshu215500 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

Due to their need to handle a huge size of image data and limited battery power at each node, visual sensor networks (VSNs) suffer from short network lifetime and long network delay. Consequently, how to reduce network traffic is very important in their operation. The deployment of processing nodes for feature extraction is an effective way to reduce it. Deploying multiple types of camera nodes should help do so as well. Therefore, in this work we investigate how to deploy a heterogeneous VSN for target coverage and feature aggregation with lifetime guarantee at the minimum cost. Since this problem is NP-hard, we propose two heuristics, i.e., separate and joint node deployment algorithms, to solve it. A large number of experimental results expose that the latter has higher success rate and can obtain lower network deployment cost than the former at the expense of extra computation. © 2024 IEEE.

关键词： Sensor nodes

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