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Comprehensive analysis of BLDC motor faults in electric vehicle transportation

International Journal of Power Electronics 2023年第1期18卷 40-64页

作者： Usman, Adil Rajpurohit, Bharat Singh School of Computing and Electrical Engineering Indian Institute of Technology HP Mandi India Electrical and Computer Engineering Department University of California Santa CruzCA United States

The faults prospective are higher for any BLDC motor, driven under a constant and continuous operating period. The possibility of getting susceptible to winding short-circuit faults and rotor demagnetisation effects, is more frequently discerned. The winding insulation breakdown introduces stator inter-turn fault (SITF) conditions in the machine which has consequent detrimental impact on the rotor permanent magnets (PMs). The cumulative effect of fault stimulates rotor demagnetisation, intensifying the effect of faults. This paper aims in classifying the motor faults employed in electric vehicle (EV) transportation. Depending upon the machine’s characteristic performance in terms of electrical signatures like, motor current signature (MCS) and back-EMF (BEMF), and mechanical signatures like frequency and vibration spectrum;a unique classification to fault is provided. The proposed research models investigate the performance of a machine drive under both individual and combined fault conditions, to give a significant outcome to the research findings. The closed loop motor drive operation is performed using an advanced field remodelling (AFR)-based technique which employs fuzzy logic controller (FLC). The developed drive-controller-system minimises the steady state error under motor operating conditions and has a clear discrepancy in terms of machine quantities during faults. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Numerical methods

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Analysis of Encryption Techniques for Secure Data in Cloud Environment 1

Analysis of Encryption Techniques for Secure Data in Cloud E...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 IEEE.

关键词： Cloud security

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Robust Control for Uncertain Discrete-Time Linear Systems Using Reinforcement Learning With Discount Factor

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2783-2791页

作者： Ding, Yuntian Yang, Yuxiao Yan, Zhilian Tai, Weipeng Research Institute of Information Technology Anhui University of Technology Ma’anshan243032 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215000 China School of Electrical & Information Engineering Anhui University of Technology Ma’anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China

This paper focuses on robust control for uncertain discrete-time linear systems. We transform the robust control issure into an optimal control issure for an auxiliary system. The desired control gains are obtained by solving coupled algebraic Riccati equations. We propose a model-free off-policy reinforcement learning algorithm that incorporates a discount factor to get an approximate solution to the equation, using only measured data and eliminating the need for information about system dynamics. This algorithm requires adding probing noise to control inputs to maintain persistent excitation condition. We show that the probing noise does not introduce bias in the solution of the Bellman equation. The effectiveness of the proposed algorithm is verified through a simulation example, which analyzes the impact of the discount factor and the probing noise. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Riccati equations

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Optimized deep transfer learning techniques for spine fracture detection using CT scan images

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Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part

关键词： Computerized tomography

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Effective anomaly detection in hybrid wireless IoT environment through machine learning model: a survey

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International Journal of Mobile Network Design and Innovation 2023年第4期10卷 175-181页

作者： Shanmuganathan, V. Annamalai, Suresh Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India

Wireless hybrid environment has seamlessly integrated inside all smart homes and in smart environments. Assaults and Anomalies are probably going to happen in open platform of IoT frameworks and which can result easily by sending fake alarms and the inability to appropriately identify basic occasions. To address that, IoT frameworks must be outfitted with peculiarity recognition preparing notwithstanding the necessary occasion identification capacity. This is a key component that empowers unwavering quality and productivity in IoT. An effective assault and oddity ready framework are a lot of important in IoT based climate, since these are power starving gadgets and convey extremely delicate information's. A proficient K-nearest neighbour (KNN) and Random Forest algorithm-based sensor information inconsistency recognition is anticipated in an edge gadget in this paper. The sensor hardware failure and software failures are identified in the IoT based environment and effective identification was achieved through KNN and Random Forest algorithm. © 2023 Inderscience Enterprises Ltd.

关键词： Anomaly detection

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Multimodal attention-driven visual question answering for Malayalam

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Neural computing and Applications 2024年第24期36卷 14691-14708页

作者： Kovath, Abhishek Gopinath Nayyar, Anand Sikha, O.K. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Visual question answering is a challenging task that necessitates for sophisticated reasoning over the visual elements to provide an accurate answer to a question. Majority of the state-of-the-art VQA models are only applicable to English questions. However, applications such as visual assistance and tourism necessitate the incorporation of multilingual VQA systems. This paper presents an effective deep learning framework for Malayalam visual question answering (MVQA), which can answer a specific natural language question about an image in Malayalam. As there is no available dataset in English–Malayalam VQA, a MVQA dataset was created by translating English question–answer pairs from the visual genome dataset. The paper proposes an attention-driven MVQA model on the developed dataset. The proposed MVQA model uses a deep learning-based co-attention mechanism to jointly learn the attention for images and Malayalam questions. A second-order multimodal factorized high-order pooling is used for multi modal feature fusion. Different VQA models using combinations of classical CNNs and RNNs were experimented on the developed MVQA dataset, and the performance was compared against the proposed attention-driven model. Experimental results show that the proposed attention-driven MVQA model achieves state-of-the-art results as compared to other models for MVQA on the custom Malayalam VQA dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Four Quadrant Operation of Switched Reluctance Motor with Online Turn ON Angle Control 11

Four Quadrant Operation of Switched Reluctance Motor with On...

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11th National Power Electronics Conference, NPEC 2023

作者： Ingle, Piyush Misra, Himanshu Indian Institute of Technology School of Computing and Electrical Engineering HP Mandi India

ISBN: (纸本)9798350311648

This paper describes four quadrant speed control of SRM with online turn ON angle control. A speed PI controller and a current PI controller are used for the purpose. Online turn ON control has also been implemented that adjusts the turn ON angle according to the torque demand and speed, it uses reference current, speed, and DC link voltage to do so. A logic has been developed to determine in which quadrant the motor should be running based on actual speed and reference speed, and it is used to decide whether the winding should be excited on increasing slope or decreasing slope. With the help of this control strategy the motor can track the speed reference in all four quadrants. This method is experimentally verified on 8/6 SRM using an asymmetrical bridge converter to power the SRM and dSPACE controller board. This technique can be used for low performance applications. © 2023 IEEE.

关键词： Controllers

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Optimized Consensus for Blockchain in Internet of Things Networks via Reinforcement Learning

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Tsinghua Science and technology 2023年第6期28卷 1009-1022页

作者： Yifei Zou Zongjing Jin Yanwei Zheng Dongxiao Yu Tian Lan Institute of Intelligent Computing School of Computer Science and TechnologyShandong UniversityQingdao 266237China Department of Electrical and Computer Engineering George Washington UniversityWashingtonDC 20052USA

Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded ***,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless ***,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement ***,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain *** this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective *** results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.

关键词： consensus in blockchain Proof-of-Communication(PoC) MultiAgent Reinforcement Learning(MARL) Internet of Things(IoT)networks

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A Dual-Band, Polarization Insensitive Rectifying Surface for RF Energy Harvesting

A Dual-Band, Polarization Insensitive Rectifying Surface for...

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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Shah, Prashant Dutta, Debarati Reddy, G. Shrikanth Indian Institute of Technology Mandi School of Computing and Electrical Engineering Mandi India

ISBN: (纸本)9798350328264

A dual-band polarization-insensitive metasurface array-based RF energy harvester is presented in this paper. The unit cell of the metasurface consists of an X-shaped and four Dshaped slotted resonators. 4 Schottky diodes (BAT15-03WL) are embedded in the rectifying surface. A simultaneous impedance matching between free space and rectifying surface (metasurface + rectifier diodes) is achieved at 3.035 GHz and 6.885 GHz. The unit cells are connected with thin highly inductive microstrip lines to collect DC voltage. The proposed RF energy harvester offers more than 95% absorption efficiency for different angles of polarization and up to 80% absorption efficiency for wide angles of incidence at both the designated bands. The proposed harvester can harvest energy from the 3.035 GHz and 6.885 GHz frequency bands. © 2023 IEEE.

关键词： dual-band Metasuface polarization insensitive. rectifying surface

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