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Energy management of solid oxide fuel cell/lithium battery hybrid system

CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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AN APPROXIMATION SCHEME FOR SOLVING QUASI-VARIATIONAL INCLUSIONS AND QUASIMONOTONE VARIATIONAL INEQUALITIES

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第3期84卷 41-52页

作者： Yu, Youli Chen, Kun Zhu, Li-Jun School of Electronics and Information Engineering Taizhou University Linhai317000 China Computer Center Taizhou Hospital of Zhejiang Province Linhai China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China

In this article, we discuss iterative schemes for solving quasi-variational inclusions and quasimonotone variational inequalities in Hilbert spaces. An iterative scheme is presented which consists of resolvent technique, extragradient-type algorithm and selfadaptive linear search rule. Under several appropriate conditions, we prove the convergence of the investigated scheme. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Variational techniques

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Image Matching Algorithm Under Adverse Conditions Based on SIFT 14

Image Matching Algorithm Under Adverse Conditions Based on S...

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14th International Conference on information Technology in Medicine and Education, ITME 2024

作者： Chen, Hao Cao, Xinrong Li, Zuoyong Lin, Lihui Fuzhou University College of Computer and Big Data Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Fujian354300 China

ISBN: (纸本)9798350356670

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance. © 2024 IEEE.

关键词： Image matching

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A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Zhu, Zhirong Liu, Yiwen Gao, Yanxia Wen, Wenkan Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the rise of data storage computing and IoT technology. The increase in data volume and user demand, the accurate delivery of data and low latency during transmission become important factors that affect the end-user experience. To address this issue, previous authors have proposed the concept of edge computings. In the general environment of edge computing, reasonable scheduling of edge caches can largely achieve low latency and high efficiency, thus improving user experience. In this paper, based on existing research, we propose a combination of a joint learning framework for cache prediction based on region popularity and an edge collaborative cache value optimization method to further improve cache hit rate and cache utilization efficiency. The method obtains excellent expected results through simulation experiments. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

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Event-triggered H∞ Consensus of Leader-follower Multi-agent Systems

Event-triggered H∞ Consensus of Leader-follower Multi-agent...

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第43届中国控制会议

作者： Cheng Gong Chenguang Ai Xuemin Zuo Zhuang Ma W.Dai Key Lab of Intelligent Data Information Processing and Control of Hebei Province Key Lab of Intelligent Motion Control System of Tangshan City Intelligence and Information Engineering College Tangshan University Hebei Jianzhi Casting Group Ltd. College of Electrical Engineering North China University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper discusses the H consensus problem of leader-follower multi-agent systems. The controller for each agent is crafted to utilize comprehensive information from all connected agents, while an innovative event-triggered strategy is incorporated to minimize communication costs. Based on the Lyapunov function theory, an H controller is proposed that can ensure both system stability and a prescribed disturbance attenuation level γ. Finally, a numerical example validated the effectiveness of the proposed method.

关键词： Leader-follower Multi-agent systems Event-triggered H_∞ consensus

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Propelling Pedagogical Paradigms: A Study of Advanced Human-Computer Interaction Learning in data Structure and Algorithms Based on Virtual Reality Technology 18th

Propelling Pedagogical Paradigms: A Study of Advanced Human-...

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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Yahui, Yang Yanxia, Gao Ling, Peng Yingxi, Xiang Huan, He Yiwen, Liu School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China School of Fine Arts and Design Huaihua University Hunan Huaihua418000 China

ISBN: (纸本)9789819707362

data Structure and Algorithms (DSA) is a course with strong practice and abstract theoretical knowledge. In view of the complexity of the knowledge points of the course and the difficulty of understanding, we design and implement a virtual simulation experiment teaching system of the course--Virtual simulation experiment platform. In this platform, DSA through simulation practice into animation effects, and combined with the unity engine and C# code to achieve user interaction, to provide users with a way to intuitively understand the principles of DSA, while increasing the user’s subjective initiative to learn, and to achieve the high level, Innovative and challenging teaching classroom, so that students in colleges and universities can feel relaxed and happy in the process of learning this course. What is more, they can better accept and understand DSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data structures

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Short-term load forecasting for buildings based on dynamic weighted edRVFL model

Short-term load forecasting for buildings based on dynamic w...

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2024 China Automation Congress, CAC 2024

作者： Xie, Junwei Yuan, Meng Xu, Zhezhuang Liu, Chi Xia, Yuxiong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798350368604

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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DMAM-ECG: A Diffusion Model with Self-Attention Module for ECG Signal Denoising

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1278-1296页

作者： Hu, Zheng-Dong Hong, Yang Huang, Jia-Yan Chen, Kai-Hong Zhao, Wan-Qi Grau, Antoni Guerra, Edmundo Wang, Chuan-Sheng Zhang, Fu-Quan New Engineering Industry College Putian University Putian351100 China School of Mathematics and Data Science Minjiang University Fuzhou350108 China The University of Sheffield SheffieldS10 2TN United Kingdom Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Electrocardiogram (ECG) is the most common and non-invasive auxiliary diagnostic technique for heart disease. However, the ECG signal is susceptible to various noises during the collection process, which will impact subsequent analysis and diagnosis. Therefore, ECG signal denoising has become the primary task after signal acquisition. ECG signal denoising aims to remove noise interference from the collected noisy signals for clarify abnormal waveforms in it, and thus diagnose diseases more accurately. In this paper, to obtain high-quality and high-fidelity denoised ECG signals, we propose a novel diffusion model with self-attention module for ECG signal denoising (DMAM-ECG). Specifically, the DMAM-ECG contains two main branches used to extract features of input noise signal observations and potential variables, respectively. Each branch consists of 6 designed Convolution-Instancenormal-LeakyReLU Filter Blocks (CIL-Filter Blocks). Meanwhile, DMAM-ECG introduces the self-Attention Module Fusion Blocks (SAM-Fusion Blocks) to further extract features for the first branch, and then fuse the output features with the second branch. Besides, we use a multi-shot reconstruction strategy to further improve signal denoising. Quantitative and qualitative results on the QT database show that the proposed DMAM-ECG performs better than the existing 5 state-of-the-art ECG signal denoising methods, especially for strong noise interference ECG signal removal. To sum up, our DMAM-ECG introduces self-attention module into the diffusion model to improve the grasp of global information and achieve high-quality information preservation for extremely noise removal of ECG signal. In particular, it uses only 3-shot reconstruction gets much better denoised results than that of the best baseline with 10-shot. The code of the proposed DMAM-ECG is available at https://***/luvletterh/DMAM-ECG. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Signal denoising

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Network Intrusion Detection Model Based on Space-time Fusion Features and Attention Mechanism 33

Network Intrusion Detection Model Based on Space-time Fusion...

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第33届中国控制与决策会议

作者： Yali Wu Liting Huang Jinjin Qi Xiaoxiao Quan School of Automation and Information Engineering Xi'an University of Technology Shaanxi ProvinceKeyLaboratory of Complex System Control and Intelligent Information Processing Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781665431293

To solve the problem of low performance of network intrusion detection,a deep learning intrusion detection model based on space-time fusion features and attention mechanism—CLT-net is *** this model,space-time fusion features are obtained by integrating convolutional neural network and long short-time memory network,and attention module is added to calculate the importance of the input features,and softmax function is used for *** a large number of simulation experiments on NSL-KDD data sets,CLT-net has significantly improved the convergence of the training set and the accuracy of the test *** with the traditional CNN model with similar structure and the space-time fusion CLSTM the accuracy of the model increased by 11.8% and 10.9%*** shows that this model has great potential in the application field of network intrusion detection.

关键词： Network Intrusion Detection Convolution Neural Network Long Short-term Memory Network Attention Mechanism CLT-net

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