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A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

Digital Communications and Networks 2022年第6期8卷 911-922页

作者： Tingyan Long Yong Ma Lei Wu Yunni Xia Ning Jiang Jianqi Li Xiaodong Fu Xiangmi You Bo Zhang College of Computer Science Chongqing UniversityChongqing400030China School of Computer and Information Engineering Jiangxi Normal UniversityJiangxi330022China School of Mathematical Sciences University of Electronic Science and Technology of ChinaChengdu611731China Mashang Consumer Finance Co. Ltd.(MSCF)Chongqing401129China Global Energy Interconnection Research Institute Co.Ltd. Beijing102209China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan650500China CISDI Research&Development CO. LTDChongqing401122China

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

关键词： Edge computing Fault tolerance DQN algorithm Primary-backup model

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Fast Analysis of Mn,Co,and Ni in the Ternary Cathode Material Mixture of Lithium Battery with EDXRF

Fast Analysis of Mn,Co,and Ni in the Ternary Cathode Materia...

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2021第六届能源，环境与地球科学国际会议

作者： Shi Jie Lai Wanchang Zhai Juan Zhou Jinge Shu Ziyao Wu Peiliang Dai Wei Gu Runqiu School of Nuclear Technology & Automation Engineering Chengdu University of Technology

The content of Mn,Co,and Ni in the ternary cathode material mixture of lithium batteries directly affects the electrochemical performance of the electrode *** rapid measurement of the content of Mn,Co,and Ni is of great significance to the production and process control of the ternary cathode *** this paper,energy dispersive X-ray fluorescence analysis(EDXRF) was used to analyze the content of Mn,Co,and Ni in the ternary cathode material mixture of lithium *** calculating the peak area,the branch ratio subtraction method is used to correct the interference of the Kβ peak of Co on the Kα peak of *** the spectrum data processing,combining the advantages of the SNIP method and the polynomial fitting method to subtract the scattering background,the two methods are combined to process the measured spectrum lines,and good results have been *** the matrix effect correction,by comparing the theoretical mass absorption coefficient and the experimental results,it is concluded that the Mn,Co,and Ni in the ternary cathode material mixture of the lithium battery are affected by the matrix effect,which is established by the multiple regression analysis in the empirical coefficient *** model to correct the absorption enhancement effect of Mn,Co and *** experimental results show that the average absolute error of Mn,Co,and Ni content in the ternary cathode material mixture of lithium battery using the empirical coefficient method is 0.09%,0.02%,0.04%,and the average relative error is 0.52%,0.12%,0.13%.Single sample analysis only needs 200 s,which meets the requirements of fast,efficient and accurate analysis.

关键词： EDXRF Lithium Battery Ternary Cathode Material Mixture Background Subtraction Absorption and Enhancement Effect

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Research on SWCNT non-contact strain sensor based on photoluminescence principle

Research on SWCNT non-contact strain sensor based on photolu...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Jie Zhang Fuzhong Zheng Junjie Du Yuting Lu Zhonghao Li Ying Wu Intelligent manufacturing and automobile school Chongqing college of electronic engineering Chongqing China School of Automation Chengdu University of Information Technology Chengdu China NO.7 Oil Production Plant PetroChina Changqing Oilfield Company Qingyang China College of mechanical engineering Sichuan university of science and engineering Chengdu China School of intelligent technology and Engineering Chongqing University of Science and Technology Chongqing China

Developing an optical full field non-contact strain sensor based on the spectral shift characteristics of single walled carbon nanotube (SWCNT), combining with the principle of photoluminescence is an emerging application field of SWCNT material that has important application prospects. The current paper analyzed the photoluminescence principle of SWCNT material, and discussed the optical path for the fluorescence excitation and acquisition of the SWCNT. Selection of the optical components in the optical path are completed. Then, based on the theory of polarize light, the fluorescence excitation of SWCNT and the controllable adjustment of polarize light in fluorescence spectra are studied. Experimental results indicate that the controllable adjustment accuracy of the polarize light is high, which conforms to Malus's law.

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Lyapunov Matrix-Based Guaranteed Cost Dynamic Positioning Control for Unmanned Marine Vehicles with Time Delay*

Lyapunov Matrix-Based Guaranteed Cost Dynamic Positioning Co...

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International Conference on Information Science and technology (ICIST)

作者： Xin Yang Li-Ying Hao Tieshan Li Yang Xiao Guoyong Liu College of Navigation Dalian Maritime University Dalian China College of Marine Electrical Engineering Dalian Maritime University Dalian China College of Automation Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science The University of Alabama Tuscaloosa USA

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

This paper presents a Lyapunov matrix-based guaranteed cost dynamic positioning controller for unmanned marine vehicles (UMVs) with time delays. A novel Lyapunov-Krasovskii functional (LKF) is introduced, which enhances the analysis of time delays and system states. The controller design leverages the LMI framework alongside Jensen's inequality to determine sufficient criteria for its feasibility, ensuring that the UMV s' state errors gradually reduce to zero and providing an adaptive $H_{\infty}$ performance guarantee. Additionally, the cost function is upperbounded, and the effectiveness of the method is demonstrated through simulation results.

关键词： Information science Costs Delay effects Simulation Control systems Robustness Linear matrix inequalities Vehicle dynamics Marine vehicles Time-varying systems

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Design of multi-Sensor Cooperative Detection Architecture Based on Analytic Hierarchy Process 6

Design of multi-Sensor Cooperative Detection Architecture Ba...

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6th International Conference on Communication engineering and technology, ICCET 2023

作者： Li, Kaiyu Cai, Xinxin Liu, Jialei Huang, Shuang Chen, Yuanbao Wang, Wenlin Yu, Yang Zhu, Quanhua Xu, Zhihui Wang, Dagui Naval University of Engineering Wuhan Second Ship Design and Research Institute Wuhan China Naval University of Engineering Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China Shenzhen Technology University Sino-German College of Intelligent Manufacturing Shenzhen China Nuclear Power Institute of China Chengdu China China Ship Scientific Research Center Wuxi China State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Shenzhen China C Anhui Jianzhu University School of Mechanical and Electrical Engineering Hefei China

ISBN: (纸本)9798350336153

Due to the diversity of information sources, the information provided by a single sensor can no longer meet the needs. Therefore, multi-sensor collaborative detection technology is a hot topic in the current research field. At present, there is no recognized, reasonable and effective method for evaluating the effectiveness of multi-sensor collaborative detection. To solve this problem, this paper will take sonar sensor, magnetic detector and infrared detector as the research object, and propose a multi- sensor collaborative detection architecture based on analytic hierarchy process to provide technical support for multi-sensor collaborative detection technology. © 2023 IEEE.

关键词： Analytic hierarchy process

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ReScConv-xLSTM: An improved xLSTM model with spatiotemporal feature extraction capability for remaining useful life prediction of Aero-engine

Results in Engineering

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Results in engineering 2025年 26卷

作者： Mingxing Huang Lanying Yang Gang Jiang Xingan Hao Hong Lu Hang Luo Peng Wang Jinyang Li School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu 610059 China The college of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 China

Remaining Useful Life (RUL) prediction is crucial for Prognostics and Health Management (PHM) of aircraft engines. Although deep learning models based on LSTM and Transformer have achieved significant results in this field, these models typically only extract temporal features, neglecting spatial features, and struggle with parallel computation, leading to a bottleneck in RUL prediction performance. To address these issues, this paper proposes an improved ReScConv-xLSTM model that integrates the characteristics of xLSTM, ScConv, and residual structures. Firstly, the model converts one-dimensional signals from multiple sensors into multi-channel two-dimensional wavelet time-frequency images through time window processing, RobustScaler normalization, and the Continuous Wavelet Transform (CWT) method, enhancing the spatiotemporal features of the data and achieving data augmentation and noise reduction. Subsequently, these spatiotemporal features are input into the ReScConv-xLSTM model for training to learn the spatiotemporal dependencies of the data, thereby obtaining accurate RUL predictions. Experiments on the C-MAPSS and N-CMAPSS datasets of aircraft turbofan engines demonstrate that the model can accurately predict RUL values, exhibiting strong accuracy, generalization capability, and practical application potential, with the average RMSE reduced to 2.07 and the average Score value reduced to 36.42.

关键词： Aero-engine prognosis Remaining useful life prediction ReScConv-xLSTM Spatial and temporal features Multi-sensor signal fusion

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Research on Data Optimization technology of Multi-Source Sensing Signal in nuclear Power Plants

SSRN

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SSRN 2023年

作者： Liu, Yongchao Li, Tong Xiao, Kai Chen, Jie Tan, Xin Cheng, Jiahao Tan, Sichao Wang, Bo He, Zhengxi Shen, Jihong Gao, Puzhen Tian, Ruifeng College of Nuclear Science and Technology Harbin Engineering University Harbin150001 China Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment Harbin Engineering University Harbin150001 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Insitute of China Chengdu610213 China College of Mathematical Sciences Harbin Engineering University Harbin150001 China

The sensor signals collected by the nuclear power plant system equipment are mixed with complex noise information. The uncertainty of sensing data directly affects the application effect of artificial intelligence on nuclear power data mining. Traditional denoising methods have poor adaptability to different noisy data, are highly dependent on researchers, and take a long time. This paper proposes a data optimization technology based on a hybrid adaptive real-time denoising model, which can realize data distribution self-adaptation and algorithm super-parameter self-optimization according to the characteristics of noisy source data. Partition denoising processing of source data. The application of examples and simulation experiments show that this technology can effectively realize the optimization of nuclear power operation data, and provide important technical support for the engineering application of artificial intelligence technology in the complex noise environment of nuclear power plants. © 2023, The Authors. All rights reserved.

关键词： nuclear energy

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Line positions,intensities,and Einstein A coefficients for 3-0 band of^(12)C^(16)O:A spectroscopy learning method

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Chinese Physics B 2021年第12期30卷 370-377页

作者： Zhi-Xiang Fan Zhi-Zhang Ni Jie-Jie He Yi-Fan Wang Qun-Chao Fan Jia Fu Yong-Gen Xu Hui-Dong Li Jie Ma Feng Xie School of Science Key Laboratory of High Performance Scientific ComputationXihua UniversityChengdu 610039China State Key Laboratory of Quantum Optics and Quantum Optics Devices Laser Spectroscopy LaboratoryCollege of Physics and Electronics EngineeringShanxi UniversityTaiyuan 030006China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor Engineering and Safety of Ministry of EducationTsinghua UniversityBeijing 100084China

Based on the model-and data-driven strategy,a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band of the ground electronic state of^(12)C^(16)*** labeled line lists such as line intensities and Einstein A coefficients quoted in HITRAN2020 are collected to enhance the *** quantified spectroscopy-learned spectroscopic constants is beneficial for improving the extrapolative accuracy beyond the *** comparisons are made for line positions,line intensities,Einstein A coefficients,which demonstrate that the model-and data-driven spectroscopy learning approach is a promising and an easy-to-implement strategy.

关键词： carbon monoxide line lists a model-and data-driven strategy spectroscopy learning

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Power Management Method for Clean Energy Ships Based on Supply Capacity Constraints

Power Management Method for Clean Energy Ships Based on Supp...

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2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Song, Jing Shan, Qihe Li, Tieshan Teng, Fei Navigation College Dalian Maritime University Dalian China College of Automation Engineering University of Electronic Science and Technology Chengdu China Marine Engineering College Dalian Maritime University Dalian China

ISBN: (纸本)9781665443227

Ships that use clean energy as power can effectively reduce environmental pollution. The power management of clean energy ships has attracted widespread attention from the industry in recent years. However, the existing research on power management issues don't comprehensively consider the security of ship navigation. If the supply capacity constraints are considered in the ship's power system to make the system operate within a safe boundary, the security of the ship will be greatly improved. This paper constructs the power system based on a chopper and proposes a multi-constrained optimization management system for new energy ships, aiming at minimizing the operation cost. The supply capacity constraints are considered in this paper so that the power system can meet the N-1 power security guidelines. After considering the N -1 power security guidelines, even if the ship's power supply equipment is damaged, the ship's safe and stable operation can still be guaranteed. Finally, the simulation results of the paper prove the effectiveness of the algorithm. © 2021 IEEE.

关键词： Ships

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A Hierarchical Optimization-Based Model for Microgrids with Energy Storage Considering Operation Cost and Net Load Mean Square Error

A Hierarchical Optimization-Based Model for Microgrids with ...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Zexiang Guan Dongsheng Cai Olusola Bamisile Jian Li Zhenyuan Zhang Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

When a high proportion of renewable energy is connected to the grid, the net load fluctuation can be suppressed by configuring energy storage. However, the current cost of energy storage is high. This paper proposes a bi-level optimization model that takes into account the dynamic allocation of shared energy storage (SES) capacity in response to electric load demand. The upper-layer objective function adopts EO optimization algorithm, which is to maximize the income of the shared energy storage operator (SESO). The lower-layer multi-objective function is to minimize the sum of the operating cost and the net load mean square error of multiple microgrids, which can be classified as the mixed integer linear programming problem. Then, the solution flow of the algorithm is given. Finally, under the constraints of the hierarchical optimization-based model, solving the problem by the CPLEX solver on the MATLAB platform. The results demonstrate that the proposed method can allocate optimal capacity and reduce operating costs for microgrids. Simultaneously, this method increases the revenue of SESO and promotes the consumption of renewable energy.

关键词： Renewable energy sources Costs Fluctuations Microgrids Mean square error methods Mathematical models Classification algorithms

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