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Multi-Fault Diagnosis, Quantitative Analysis and Warning Strategy for Lithium-ion Battery System: When LSTM Meets the Fuzzy Logic Theory

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8224-8235页

作者： Chen, Hui Tian, Engang Wang, Licheng Zheng, Yuejiu University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai University of Electric Power College of Automation Engineering Shanghai200090 China University of Shanghai for Science and Technology College of Mechanical Engineering Shanghai200093 China

This paper is concerned with the multi-level secure warning problem for lithium-ion battery systems subject to multi-fault scenarios. First, the long short-term memory (LSTM) network is employed to design a series of cell voltage estimators. In comparison with the estimation of the cell voltage and its measurement, the estimation residual is constructed in terms of the Euclidean distance, which plays an essential index for the multi-fault detection issue. Furthermore, in combination of the voltage envelope relationship, a specific type of the faults is subsequently identified. Then, for the subsequent fault warning purpose, a novel fault quantitative analysis method is proposed to further determine not only the fault amplitude but also its duration. In addition, on the basis of specific characteristics of the fault such as the type, count, duration and the amplitude, a fuzzy-logic-based multi-level secure warning strategy is put forward. Finally, the effectiveness of the developed multi-level fault warning approach is substantiated through a comprehensive experimental validation. © 2015 IEEE.

关键词： Fuzzy logic

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Infrared Single Pixel Imaging Based on Generative Adversarial Network

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Journal of Shanghai Jiaotong University (science) 2023年 1-11页

作者： Jiang, Yilin Zhang, Yilong Zhang, Fangyuan College of Information and Communication Engineering Harbin Engineering University Harbin150001 China College of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

In the field of imaging, the image resolution is required to be higher. There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system. This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned. It is effective to use optical lens instead of traditional optical reflection system, which can reduce the loss in optical path transmission. At the same time, deep learning neural network is used for control. An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained. The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling, the system finally can realize the single pixel recovery imaging of the infrared image, which improves the resolution of the image while ensuring high sensitivity. © 2023, Shanghai Jiao Tong University.

关键词： Generative adversarial networks

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A Dynamic Clustering Method for Maximizing Energy Efficiency in C-V2X Metaverse 10

A Dynamic Clustering Method for Maximizing Energy Efficiency...

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10th IEEE Smart World Congress, SWC 2024

作者： Ma, Mingyang Feng, Xin Zhang, Jing Liu, Haohua Wang, Jiacheng Sun, Geng Changchun University of Science and Technology College of Computer Science and Technology Changchun China Nanyang Technological University College of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331520861

The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivity and real-time data processing capabilities. These features make it possible to create a smart transportation system that closely mirrors the real world. Dynamic clustering is an effective network management method for handling the large number of mobile vehicles within the Metaverse, particularly in emergency scenarios where base stations (BSs) may fail. However, frequent changes in network topology due to high-speed vehicles result in unstable clusters and low communication efficiency. In this study, a dynamic clustering algorithm based on motion vector similarity (DA-MVS) is proposed for constructing stable clusters, and an adaptive adjustment clusters strategy is presented based on the change rate of vehicle numbers to improve energy efficiency (EE). Furthermore, we propose an elite chaotic perturbed sparrow search algorithm (ECPSSA) to optimize the EE of the C-V2X. Compared to the multiple baseline algorithm, the successful transmission rate can be improved by up to 76.9%, and the EE is enhanced by 20.97%. © 2024 IEEE.

关键词： Information management

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi directional optimization iot devices swarm deep learning

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Intelligent surveillance support system

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Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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A Comprehensive Study of the Advanced Encryption Standard (AES) for Secure Communications 3

A Comprehensive Study of the Advanced Encryption Standard (A...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Revathy, J. Shanthalakshmi Prakash, M. Surya Lingeshwaran, K. Dhinakaran, J. Davidv Harish, V. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331533205

AES is a popular symmetric encryption algorithm and sophisticated encryption standard that is used to protect digital communications across mobile devices, cloud storage, and the Internet of Things. The SubBytes, ShiftRows, MixColumns, and AddRoundKey transformations make up the full AES structure. Additionally, there are methods for enlarging keys with all possible key sizes - 128, 192, and 256 bits. Performance analysis of AES in various computing environments emphasizes its usability in highspeed applications and very constrained environments such as those in IoT. Based on this analysis, it's confirmed that AES remains the versatile and secure option available for modern encryption needs;hence, it shows more insight into its continuous usage and implications for the next cryptographic advancements in secure communications. © 2025 IEEE.

关键词： Secure communication

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Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

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computer Systems science & engineering 2023年第1期44卷 423-440页

作者： Devakirubai Navulkumar Menakadevi Thangavelu Department of Computer Science and Engineering Jayam College of Engineering and TechnologyDharmapuri636813India Department of Electronics and Communication Engineering Adhiyaman College of EngineeringHosur635109India

Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic *** specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network ***-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network *** speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration *** the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital *** infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled *** simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

关键词： Vehicular cloud container migration cell transmission model 5G NR V2X PC5 interface

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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AVirtual Cloud Storage Architecture for Enhanced Data Security

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computer Systems science & engineering 2023年第2期44卷 1735-1747页

作者： M.Antony Joans Kumar C.Christopher Columbus E.Ben George T.Ajith Bosco Raj Department of Information Technology St.Xavier’s Catholic College of EngineeringNagercoil629003Tamil NaduIndia Department of Computer Science Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Computer Science Engineering University of Technology and Applied SciencesMuscat112Oman Department of Electronics and Communications Engineering PSN College of Engineering and TechnologyTirunelveli627152India

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and conﬁdentiality,as well as *** provide conﬁdentiality and availability,theﬁle to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured theﬁle integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***ﬁle could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisﬁes the CIA *** response time VCSA model was tested with different sizedﬁles starting from 2 to 16 *** response time for storing and retrieving a 2 MBﬁle is 4.96 and 3.77 s respectively,and for a 16 MBﬁle,the response times are 11.06 s for storage and 5.6 s for retrieval.

关键词： Erasure code regenerating code cryptography virtual cloud storage architecture circular shift chunk allocation algorithm

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Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

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computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

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