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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-pulse Power Supply for Electrolytic Hydrogen

IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (GZ) Guangzhou China Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India College of Electrical Engineering Sichuan University Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel Asymmetric Current Injection Units (ACIU). The ACIU is installed on the DC side of the AC-DC converter, generating a specific current at the DC link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a Total Harmonic Distortion (THD) of 3.2% and an output current with a ripple rate (RR) of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2 kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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Machine Learning-Enhanced Voice Interaction: Revolutionizing Windows Desktop Applications 1

Machine Learning-Enhanced Voice Interaction: Revolutionizing...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Vora, Raj Chakrawarti, Rajesh Kumar Raghuwanshi, Namrata Sushila Devi Bansal College of Technology Computer Science and Engineering Indore India

ISBN: (纸本)9798331518578

The use of voice interaction is changing the way human being communicate with devices as it offers a simple and effective way of interaction. To implement voice interaction within desktop applications and make it more effective as it is integrated with the operating system, there is a various need models, to particularly improve Convolutional its neural quality. Networks This (CNNs), paper Recurrent considers Neural how Networks (RNNs), and transformers, can enhance the effectiveness of voice interaction in the context of Windows desktop platforms. The research focuses on several major issues: enhancing the speech recognition performance in the presence of background noise, handling the differences in user's accent and speech manner, and real-time operation with reduced delay. It also examines issues of privacy relating to handling of sensitive voice information and ways of reducing these vulnerabilities. Using and enhanced user machine intent learning and results operating encompass connecting detection strategies, the indicate system the in this user's and that voice possibility developing voice research experience provides the interaction the of recognition in is an application with next increasing systems. Different designed better of other these generation the practical user to machine current models of multilingual applications experience. Improve learning and are desktop capabilities, including the enhances future assessed apps. Implementing productivity current The AI from the tools, limited paper effectiveness solutions, the ability accessibility capabilities also and thus perspective to features of provides quality stating of recognize and contextual recommendations of that performance, users' intelligent understanding for voice flexibility emotions, assistants. Future control is and the research, commands the which on key them to Windows. © 2025 IEEE.

关键词： Speech recognition

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SADAK: Simple, and Automatic Detection of Accidents on roads using Kolmogorov-Arnold Networks

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Dutta, Anurag Kumar, Pijush Kanti Lakshmanan, K. Ghosh, Soubhik Department of Computer Science Government College of Engineering & Textile Technology Calcutta Serampore India Department of Information Technology Government College of Engineering & Textile Technology Calcutta Serampore India Department of Mathematics Kuwait American School of Education Hawalli Salmiya Kuwait Department of Computer Science Bengal College of Engineering and Technology Calcutta Durgapur India

Road accidents are one of the major contributors to mortality in India with nearly 1.68 lakh deaths in 2023 accounting for nearly 19 deaths per hour. While road accidents aren’t always “fatal”, delayed detection leads to the loss of many lives. Deep Learning deliberately showed terrific results for a lot of tasks, but for a long time, the Deep Learning models utilized the standard Multi-Layered Perceptron (MLP) architecture as its’ skeleton. Recently, a state-of-the-art architecture—Kolmogorov Arnold Network (KAN) showed up, with variable activation functions in each edge of the graphical representation of the network. Several works have inherited the KAN architecture and have shown better results in many instances. This research proposes a class of accident detection models, SADAK (Simple, and Automatic Detection of Accidents on roads using Kolmogorov–Arnold Networks) for detection of road accidents inheriting KAN architecture. To benchmark its efficiency, several well-known MLP based architectures have been considered, alongside some state-of-the-art models, which result in accuracies as high as 89% using the MLP (Vision Transformer), and 97% using the KAN architecture (KAN-Convolutional-MLP). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Kolmogorov Arnold network Multi-layered perceptron Road accidents

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Prediction of banana leaf disease by intelligent algorithm through digital image using neural network technique

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Multimedia Tools and Applications 2025年 1-17页

作者： Nagu, Bharathiraja Kaur, Gaganpreet Shanmugavelu, Murugesan M.R.M, Veeramanickam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Information Technology Tagore Engineering College Tamil Nadu Chennai India Apex Institute of Technology-Computer Science and Engineering Chandigarh University Punjab India

A lot of research shows that there could be several reasons why the duality of agricultural products has been reduced. Plant diseases make up one of the most important components of this quality. Therefore, the reduction of plant diseases allows for much higher product quality. The automated diagnosis of plant diseases using CNN techniques is presented in this article. The simple approach alters the 25 color channels and 24H histograms. The Banana Leaf Disease Detection Technique (BLDDT), which uses two attention mechanisms to assess, identify and categorize traits, was also suggested in this research. In the analysis phase, the testing carried out using the reference information reveals that the disease, detection system may be reasonably more appropriate than the current techniques because it both identifies and diagnoses the diseased/infected areas. The researchers were able to identify Esca, black decay, and isariopsis ––––with a 99.93% accuracy level using the suggested disease detector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Voice Spoofing Detection using Feature Adaptive One-Class Classification Based on Graph Neural Network 24

Voice Spoofing Detection using Feature Adaptive One-Class Cl...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Lan, Haiyan Li, Yike Wang, Yuhua Dong, Linfan Han, Zhiying Yang, Kai College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710834

To address the problems of complex real voice feature distribution, easy overfitting by learning only one classification boundary, and poor generalization ability of existing voice spoofing detection methods for unknown attacks, this paper combines the representation ability of graph neural networks with the learning objective of one-class classification, and proposes a feature adaptive one-class voice detection framework based on graph neural networks. The core idea of the feature adaptive one-class classification loss (FAOC) in the framework is to cluster the real voices affected by similar factors more closely, learn several classification boundaries of the real voices, and optimize the distance of the real voices with different distributions, so that the model can obtain more general features of the real voices. At the same time, the relationship between different frequency bands or time segments of the voice is modeled by using graph neural networks, and the graph embedding features with strong recognition ability are obtained, which can better exploit the performance advantages of FAOC. A series of experiments are conducted on the ASVspoof2019 LA and ASVspoof2021 LA datasets, and the results on the ASVspoof2021 LA show that the EER and min-tDCF are improved by 45.8% and 48.2% compared to the baseline. The results show that the proposed method has good performance for voice synthesis attack detection. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Blockchain for Patient-Centric Solutions Using Distributed Ledger technology 2

Blockchain for Patient-Centric Solutions Using Distributed L...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sandhya, G. Vaishnavi, T. Subashree, M. Naveen Kumar, A. Sri Krishna College of Technology Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Blockchain technology, with its decentralized and tamper-resistant nature, has the potential to revolutionize healthcare by improving data security, reliability, and interoperability. By leveraging encryption methods such as Attribute-Based Encryption (ABE), patient records can be securely stored and protected from unauthorized modifications or breaches. Additionally, the integration of smart contracts and standardized data formats enables seamless communication across diverse healthcare systems, fostering a more connected and efficient ecosystem. This patient-focused model enhances data privacy while granting individuals greater control over their medical information. However, the adoption of blockchain in healthcare encounters several challenges, including regulatory hurdles, scalability issues, implementation costs, and acceptance by users. Moreover, integrating blockchain with existing infrastructures and ensuring cooperation among stakeholders adds further complexity. Despite these obstacles, research underscores blockchain's potential to redefine healthcare data management. Addressing these challenges can pave the way for a more secure, interoperable, and patientdriven healthcare system in the future. © 2025 IEEE.

关键词： Information management

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Research on Fault Detection of Traction System Based on Multi-task Learning

Research on Fault Detection of Traction System Based on Mult...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Guo, Xingang Rao, Guibin Cheng, Chao College of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9789819639601

Under the background of high-speed train production performance degradation, aiming at the problem of insufficient sensitivity of high-speed train traction system for early fault detection, we propose an early fault detection method based on multi-task learning. By using the multi-task network structure, this method obtains the probability density function of the data with the help of kernel density estimation and detects the fault from the data and the probability density function value. By comparing and analyzing the detection results of two fault detection indexes, Hotelling’s T2 statistic and Kullback-Leibler divergence statistic, the identifiability of fault indexes is enhanced. In addition, the adopted multi-task learning network structure enables the networks between two different tasks to share information effectively so that the performance of the overall network for fault detection is improved. Finally, the traction drive control system platform developed by Central South University is used to verify the effectiveness of the proposed method. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Railroads

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A Speech Spoofing Detection Method Based on Feature Fusion and Residual Attention 24

A Speech Spoofing Detection Method Based on Feature Fusion a...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lan, Haiyan Dong, Linfan Wang, Yuhua Li, Yike Jiang, Dongxu Han, Zhiying College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710865

The emergence of various types of deception attacks presents a substantial risk to Automatic Speaker Verification (ASV) systems, and an increasing number of experts and scholars are focusing on the research of voice deception detection. This paper introduces an innovative approach for detecting voice deception that addresses the accuracy and generalization limitations of traditional methods. The introduced method is based on spectral-temporal feature fusion, which complements the acoustic features captured by different channels to obtain more comprehensive speaker information. It then utilizes multi-channel fused spectrogram features, applying a residual attention learning mechanism to assign varying levels of attention to the spectrogram features from different channels. The method further leverages an improved ResNet network to learn more refined acoustic information for classification. The method attained an EER of 3.60 and a t-DCF score of 0.0931 on the LA logical access corpus of ASVspoof 2019, which represent improvements of 55.5% and 56.0%, respectively, over the best performing baseline system. © 2024 Copyright held by the owner/author(s).

关键词： Spectrographs

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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