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Impedance Based Interoperability Comparison of Active and Passive Rectification Topologies for Wireless EV Charging Systems

Impedance Based Interoperability Comparison of Active and Pa...

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Wireless Power Week (WPW)

作者： Denis Kraus Grant Covic Martin Pavlovsky Morris Kesler Oguz Atasoy Siemens AG Munich Germany Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand WiTricity Corporation Watertown MA Unites States of America

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

For the widespread adoption of Wireless Power Transfer (WPT) systems for electric vehicle (EV) charging, the development of interoperable systems and bidirectional power transfer is of key interest. While existing standardization efforts have focused on unidirectional power transfer, this paper explores the potential of incorporating an active rectification (AR) topology on the vehicle side to enable bidirectional power transfer. Specifically, new sets of electronics for the 10 kW SAE J2954 reference vehicle assemblies (VAs) are investigated and their impact on interoperability is assessed. The study demonstrated that AR offers promising advantages, including increased interoperability and efficiency, while eliminating the need for tunable matching networks.

关键词： Wireless communication Electric potential Network topology Wireless power transfer Standardization Electric vehicle charging Topology

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IsoGAN: A Gan Based Method for Isometric View Images Generation From Three Orthographic Views Contour Drawings

IsoGAN: A Gan Based Method for Isometric View Images Generat...

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Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Thao Nguyen Phuong Hidetomo Sakaino Vinh Nguyen Duy Data Solution Dept. (FCJ.ABC) FPT Consulting Japan AI-Image Group FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

Reconstructing the 2D or 3D shape of an object from several 2D drawings is a crucial problem in computer-aided design (CAD). Despite the advancement of deep neural networks, automatic isometric image generation from three orthographic views line drawings using deep learning remains unresolved. Existing image-to-image translation techniques often generate images from just one input image. In this paper, we propose a novel method for the above task using a GAN-based model, namely IsoGAN. This method takes three images of object’s front, side, and top view as input, then analyzes the spatial and geometrical relations between each view and finally generates the corresponding isometric view image of the object. Extensive experiments on SPARE3D dataset show promising results of IsoGAN on isometric view generation task, demonstrating the effectiveness of the proposed IsoGAN.

关键词： Deep learning Solid modeling Design automation Three-dimensional displays Image synthesis Shape Transforms Vectors Generators Image reconstruction

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Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation

Robot Trajectron: Trajectory Prediction-based Shared Control...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Pinhao Song Pengteng Li Erwin Aertbeliën Renaud Detry Dept. Mechanical Engineering Research Unit Robotics Automation and Mechatronics KU Leuven Leuven Belgium College of Computer Science and Software Engineering Shenzhen University Shenzhen China Flanders Make@KU Leuven Dept. Electrical Engineering Research Unit Processing Speech and Images KU Leuven Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion’s onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, by reducing the operator’s cognitive load through assistance in their anticipated direction of motion. Our novel intent estimator, dubbed the Robot Trajectron (RT), produces a probabilistic representation of the robot’s anticipated trajectory based on its recent position, velocity and acceleration history. By taking arm dynamics into account, RT can capture the operator’s intent better than other SOTA models that only use the arm’s position, making it particularly well-suited to assist in tasks where the operator’s intent is susceptible to change. We derive a novel shared-control solution that combines RT’s predictive capacity to a representation of the locations of potential reaching targets. Our experiments demonstrate RT’s effectiveness in both intent estimation and shared-control tasks. We will make the code and data supporting our experiments publicly available at https://***/detry-lab/public/robot-trajectron

关键词： Codes Dynamics Estimation Grasping Probabilistic logic Cognitive load Trajectory

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EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mo...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Beenish Khalid Ali Hassan Ehsan Ullah Munir Imran Khan Niazi Dept. of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan Dept. of Computer Science COMSATS University Islamabad Pakistan Centre of Chiropractic Research College of Chiropractic Auckland New Zealand

Electroencephalogram (EEG) signals, inherently non-stationary and non-linear, present significant challenges in their processing and interpretation. This paper presents a hybrid mode selection approach using two advanced decomposition methods: Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze these signals, targeting their application in the classification of upper limb complex movements for enhanced prosthetic limb control and rehabilitation therapy assessment. Using optimized statistical features extracted from selected modes, Intrinsic mode functions (IMFs) via EMD and modes via VMD, we seek to better distinguish neural activities in pre-movement EEG signal. Our methodology involves the following two strategies: straightforward extraction of statistical features from modes yielded by EMD and VMD; a genetic algorithm (GA) feature selection technique to select the most optimal set from these statistical features. These derived features train machine learning (ML) classifiers to differentiate limb movements. The results, derived from proprietary dataset from Aalborg University, Denmark, comprising five distinct upper limb movements, demonstrate the effectiveness of our hybrid approach. The usage of EMD and VMD significantly enhanced the discriminatory power of the extracted features, leading to improved classification performance. Furthermore, our hybrid approach yielded classification accuracies of 93.1 % and 95.6% with EMD and VMD respectively when the K-NN classifier was deployed with a 10-fold cross-validation. K-NN classifier outperformed traditional ML classifiers in terms of computational time, highlighting its potential as lightweight yet robust algorithm for classification of complex movements. The primary goal is to present and validate a hybrid mode (IMFs/modes) selection approach through EMD and VMD to analyze EEG signals associated with upper limb complex movements.

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Reliability-Based Design of Time-Varying Electricity Tariff

Reliability-Based Design of Time-Varying Electricity Tariff

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Iranian Conference on Electrical engineering (ICEE)

作者： Mohammad Rastegar Nima Sarajpoor School of Electrical and Computer Engineering Shiraz University Shiraz Iran Dept. of Electrical and Software Eng. University of Calgary Calgary Canada

Implementing price-based energy management programs to change the consumption pattern of residential consumers has become an indispensable solution to the operational challenges of smart distribution power systems. However, practical methods to design efficient time-varying prices to improve the reliability and operation of distribution systems have remained scarce. This paper proposes a bi-objective optimization problem to design time-differentiated electricity tariff for residential energy management, where a new reliability evaluation method is employed to evaluate the reliability enhancement caused by load shifting. A set of numerical studies and sensitivity analysis are conducted to investigate the effectiveness of the proposed pricing method.

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks

Identify Initial Nodes for Spreader in A New Diffusion Model...

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International Conference on Web Research (ICWR)

作者： Ehsan Khosravi Fatemeh Yazdanifar Nikoo Salimi Hamid Ahmadi Beni Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Dept. of the Electrical Electronics Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Dept. of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time.

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A Big Data Analytic and IoT-Based Approach for Addressing Water Scarcity in Jordan

A Big Data Analytic and IoT-Based Approach for Addressing Wa...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdelraouf Ishtaiwi Nuha El-Khalili May Alnashashibi Mohammad Abu Arqoub Dept. of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water scarcity critically threatens agricultural sus-tainability in Jordan, necessitating innovative technological solutions. This study presents a comprehensive framework that integrates Internet of Things (IoT) sensors with Big Data Analytics to optimize water usage in agriculture. Employing a multi-stage approach, we conducted real-time data acquisition from diverse farms, processed the data using clustering algorithms, and implemented intelligent decision-making systems. Our analysis focused on key parameters such as soil moisture, temperature, rainfall, and wind speed across various farming locations. The methodology encompassed extensive field data collection, advanced analytics, and the deployment of smart irrigation systems. Results demonstrated a significant 25 % reduction in water consumption while maintaining optimal crop yields. Additionally, clustering identified three distinct farm categories, each requiring tailored water management strategies. This research contributes to the field of smart agriculture by providing practical, scalable solutions for regions facing severe water scarcity. The findings highlight the potential of advanced technologies to enhance agricultural sustainability and address critical water management challenges.

关键词： Temperature sensors Smart agriculture Irrigation Wind speed Soil moisture Big Data Real-time systems Internet of Things Sustainable development Water resources

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Machine Learning in Agriculture and Crop Management: A Comprehensive Review 3

Machine Learning in Agriculture and Crop Management: A Compr...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Rathore, Rachna Said, Archana Ganesh Jnagra, Preeti Murari, Haritha Bansal, Saloni Singh, Harshita Jiwaji University Dept. of Mathematics Gwalior India AISSMS's Institute Of Information Technology Dept. of Computer Engineering Pune India Lingayas Vidyapeeth Dept. of Biotechnology Haryana India Spark Infotech Inc Dept. of Software Engineer Phoenix AZ United States Gla University Dept. of Computer Eng. & Applications Mathura India Chandigarh University Dept. of Cse (AIML) CSE-APEX Chandigarh India

ISBN: (纸本)9798331521691

Machine learning (ML) has transformed agriculture and crop management, revolutionizing modern farming techniques. By leveraging ML, farmers can accurately monitor and analyze agricultural data, gaining valuable insights into crop health, soil conditions, weather patterns, and pest infestations. Through the analysis of satellite imagery, sensor data, and historical records, ML models can predict optimal planting times, forecast yields, and identify potential disease outbreaks, enabling more efficient resource management. These innovations promote precision agriculture, reduce waste, and support sustainability. This paper explores the latest developments in ML applications for agriculture, emphasizing their potential to enhance food security, minimize environmental impact, and boost farm profitability through data-driven decision-making. © 2025 IEEE.

关键词： Artificial Neural Network Clustering Decision Tree Information Fuzzy Network Machine Learning

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CDP: Selective Influential Nodes Based on Cliques in the Diffusion Process in Social Networks

CDP: Selective Influential Nodes Based on Cliques in the Dif...

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International Conference on Web Research (ICWR)

作者： Fatemeh Yazdanifar Hamid Ahmadi Beni Nikoo Salimi Asgarali Bouyer Dept. of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Dept. Software Engineering Azarbijan Shahid Madani University Tabriz Iran Dept. of the Electrical Electronics Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Dept. of Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran

Influence maximization techniques emphasize selecting a set of influential nodes in order to maximize influence. Because the algorithms presented in this field ignore the topology of cliques for diffusion, there are two major challenges in influence maximization algorithms: optimal diffusion and computational overhead reduction. As a result, the CDP algorithm is presented in this article to address these issues. This algorithm first selects suitable cliques for diffusion based on their position and strategy in social networks. Furthermore, for each clique, a score is calculated based on the topological criteria of the clique, and suitable cliques are selected for diffusion by applying a threshold limit. The seed nodes are chosen from the cliques in the second step. To avoid the rich club phenomenon, only a few nodes from each clique are chosen as seed candidate nodes. Finally, the seed nodes are chosen based on the node’s topology and the strength of the node’s level one neighbor. In the experiment section, the CDP algorithm significantly outperforms the best algorithms presented in recent years in terms of influence spread rate and execution time.

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Towards Motor Imagery Classification of Finger Tapping by Both Hands for Controlling a Finger-Arm Bionic Robot

Towards Motor Imagery Classification of Finger Tapping by Bo...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Seong-Hyun Yu Young-Min Go Hyeong-Yeong Park Seo-Jin Lee Minji Lee Ji-Hoon Jeong Dept. Computer Science Chungbuk National University Cheongju Republic of Korea School of Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

In motor imagery, the greatest attention was paid to decoding signals from large muscles. However, there is a missing piece in helping people with finger issues or paralysis. This study investigates the potential advancements in neurorehabilitation by exploring the discriminative capabilities of electroencephalography (EEG) based classification in finger tapping. To improve neurorehabilitation strategies, we performed a comparative analysis of finger tapping classification during motor imagery and execution tasks. Using EEG signals, we employed machine learning and deep learning techniques, including support vector machines (SVM), linear discriminant analysis (LDA) and EEGNet. Our results indicate promising differences in EEG signatures between the two paradigms, with SVM, LDA, and EEGNet demonstrating varying degrees of efficacy in classification. These findings suggest potential implications for customized neurorehabilitation interventions. The study contributes to the affirmative possibility of effective rehabilitation strategies and is of particular relevance for patients with impaired motor function.

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