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Key indexes identifying approach of weapon equipment system-of-systems effectiveness integrating Bayes method and dynamic grey incidence analysis model

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Journal of systems engineering and Electronics 2024年第6期35卷 1482-1490页

作者： ZHANG Jingru FANG Zhigeng YE Feng CHEN Ding College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing 210006China National Key Laboratory for Complex Systems Simulation Institute of System EngineeringAcademy of Military ScienceBeijing 100020China Shanghai Electro-Mechanical Engineering Institute Shanghai 201109China

Aiming at the characteristics of multi-stage and(extremely)small samples of the identification problem of key effectiveness indexes of weapon equipment system-of-systems(WESoS),a Bayesian intelligent identification and inference model for system effectiveness assessment indexes based on dynamic grey incidence is *** method uses multi-layer Bayesian techniques,makes full use of historical statistics and empirical information,and determines the Bayesian estima-tion of the incidence degree of indexes,which effectively solves the difficulties of small sample size of effectiveness indexes and difficulty in obtaining incidence rules between ***-ondly,The method quantifies the incidence relationship between evaluation indexes and combat effectiveness based on Bayesian posterior grey incidence,and then identifies key system effec-tiveness evaluation ***,the proposed method is applied to a case of screening key effectiveness indexes of a missile defensive system,and the analysis results show that the proposed method can fuse multi-moment information and extract multi-stage key indexes,and has good data extraction capability in the case of small samples.

关键词： weapon equipment system-of-systems(WESoS) effectiveness index system effectiveness key index Bayes theo-rem grey incidence analysis (extremely)small samples

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Psychological Counseling with Integration of Knowledge Graph and Multi-Agent Collaboration

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Journal of Shanghai Jiaotong University (Science) 2025年 1-13页

作者： Liu, Xi Zheng, Xiaohu Cui, Weiwei College of Information Science and Technology Donghua University Shanghai201620 China Institute of artificial Intelligence Donghua University Shanghai201620 China Shanghai Engineering Center of Industrial Big Data and Intelligent System Shanghai201620 China Management College Wuzhou University Guangxi Wuzhou543003 China

Applying large language models (LLMs) to psychological counseling can reduce costs and lower barriers to access. However, issues inherent to LLMs, such as output hallucinations, knowledge lags, and response biases, limit the quality of counseling services. To address these challenges, we propose a psychological counseling method that integrates a knowledge graph (KG) and multi-agent collaboration (MAC). First, we design an ontology model and use an improved convolution residual network (CRN) to extract relational triples for constructing a psychological medicine KG. Second, we perform low-rank adaptation (LoRA) on Qwen1.5-7B-chat using the Text2Cypher training set. Finally, we design MAC prompts based on the characteristics of the retrieval-based question-answering (QA) process to facilitate the collaborative implementation of psychological medicine knowledge QA and updates. The experiments showed that the improved CRN achieved an extraction accuracy of 80%. The mean squared error of the general-domain capability of the LLM before and after LoRA was only 0.738, with a Text2Cypher score of 99.8 after LoRA. The results generated after prompting received a 70.44% approval rate compared to other methods. Thus, the proposed method can effectively enhance the intelligence, accuracy, and controllability of psychological counseling. © Shanghai Jiao Tong University 2024.

关键词： Knowledge graph

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A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

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Computers, Materials & Continua 2024年第9期80卷 4497-4512页

作者： Tianqing Yuan Na Li Hao Sun Sen Tan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Department of Electrical Engineering Northeast Electric Power UniversityJilin132012China College of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China Center for Research on Microgrids Department of EnergyAalborg UniversityAalborg9220Denmark

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting ***,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data ***,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on ***,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available *** show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

关键词： Lithium-ion batteries state of charge state of health cyber-attacks genetic algorithm back propagation neural network

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Modeling of the resilient supply chain system from a perspective of production design changes

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Frontiers of engineering management 2023年第1期10卷 96-106页

作者： Shuangshuang ZHANG Hongfeng WANG Guo LI Junwei WANG College of Information Science and Engineering Northeastern UniversityShenyang 110819China School of Management and Economics Beijing Institute of TechnologyBeijing 100081China Department of Industrial and Manufacturing System Engineering The University of Hong KongHong KongChina

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.

关键词： resilient supply chain supply chain disruption domain modeling product design changes

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Data Correction-Based Model-Driven 3D Radiation Directional Pattern Construction for 5G Base Stations 100

Data Correction-Based Model-Driven 3D Radiation Directional ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Ye, Qiancheng Li, He Lin, Zhipeng Zhu, Qiuming Gao, Qianhao Cao, Baohua Liu, Tao Zhou, Jun Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing China States Key Laboratory of Air Traffic Management System Nanjing China Nanjing Jiexi Technology Company Nanjing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (纸本)9798331517786

Accurately measuring the radiation directional pattern of base stations is crucial for performance evaluation of antenna radiation in fifth generation (5G) wireless communications. However, existing methods construct antenna directional pattern typically rely on the E-plane and H-plane data, limiting their applicability. This paper presents a new model-driven three-dimensional (3D) radiation directional pattern construction method based on data correction for 5G base stations. This method utilizes an unmanned aerial vehicle (UAV) to collect radiation characteristic data of the base station. We first propose a position correction method based on the 3D space transformation, and refine the radiation characteristic data accuracy according to a flat earth two-ray (FE2R) model. A improved close-in (CI) model driven inverse distance weighting (IDW) algorithm is then designed to accurately sense and depict the directional pattern of the base station. Experimental results demonstrate that the proposed radiation pattern construction method can achieve highly accurate radiation pattern of base stations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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Optimized Genetic Algorithm-Based Multi-UAV Cooperative TDOA Localization for Complex Multipath Scenarios 24

Optimized Genetic Algorithm-Based Multi-UAV Cooperative TDOA...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Chen, Feng Li, He Lin, Zhipeng Zhu, Qiuming Zhong, Weizhi Chen, Xiaomin Zhou, Jun Li, Hongyu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China States Key Laboratory of Air Traffic Management System Nanjing210023 China Shanghai Institute of Satellite Engineering Shanghai201108 China China Research Institute of Radiowave Propagation Qingdao266107 China

ISBN: (纸本)9798350363760

Radiation source positioning is vitally important in electromagnetic environment monitoring activities. However, most current positioning methods cannot achieve high accuracy complex multipath scenarios. In this paper, we propose an optimized genetic algorithm based time difference of arriva (GA-TDOA) multiple unmanned aerial vehicle (UAV) cooperative positioning method. We first propose a multipath signal identification algorithm based on path loss analysis. This algorithm can eliminate the effect of non-line-of-sight (NLOS) signals, thus, improving the positioning accuracy in multipath scenarios. Then, we design an optimized GA to select the UAV nodes that are little influenced by noises. The optimized GA has fast convergence and low complexity. By using the selected UAV nodes to locate radiation sources, the robustness and positioning accuracy of the positioning method can be improved. Experimental results show that the GA-TDOA positioning method can effectively identify LOS signals in complex multipath scenarios, and the positioning accuracy is increased by about 40% compared with the existing positioning methods. © 2024 IEEE.

关键词： Antennas

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Enhanced dominance of soil moisture stress on vegetation growth in Eurasian drylands

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National Science Review 2023年第8期10卷 135-147页

作者： Yu Zhang Yangjian Zhang Xu Lian Zhoutao Zheng Guang Zhao Tao Zhang Minjie Xu Ke Huang Ning Chen Ji Li Shilong Piao Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University Department of Earth and Environmental Engineering Columbia University College of Agronomy Shenyang Agricultural University Department of Geosciences and Natural Resource Management University of Copenhagen Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Department of Geography School of Geography and Information EngineeringChina University of Geosciences State Key Laboratory of Tibetan Plateau Earth System Resources and Environment Institute of Tibetan Plateau ResearchChinese Academy of Sciences

Despite the mounting attention being paid to vegetation growth and their driving forces for water-limited ecosystems, the relative contributions of atmospheric and soil moisture dryness stress on vegetation growth are an ongoing debate. Here we comprehensively compare the impacts of high vapor pressure deficit(VPD) and low soil water content(SWC) on vegetation growth in Eurasian drylands during 1982–*** analysis indicates a gradual decoupling between atmospheric dryness and soil dryness over this period,as the former has expanded faster than the latter. Moreover, the VPD–SWC relation and VPD–greenness relation are both non-linear, while the SWC–greenness relation is near-linear. The loosened coupling between VPD and SWC, the non-linear correlations among VPD–SWC-greenness and the expanded area extent in which SWC acts as the dominant stress factor all provide compelling evidence that SWC is a more influential stressor than VPD on vegetation growth in Eurasian drylands. In addition, a set of 11 Earth system models projected a continuously growing constraint of SWC stress on vegetation growth towards 2100. Our results are vital to dryland ecosystems management and drought mitigation in Eurasia.

关键词： soil water content vapor pressure deficit vegetation growth decoupling non-linear relation

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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A Grad-CAM and capsule network hybrid method for remote sensing image scene classification

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Frontiers of Earth Science 2024年第3期18卷 538-553页

作者： Zhan HE Chunju ZHANG Shu WANG Jianwei HUANG Xiaoyun ZHENG Weijie JIANG Jiachen BO Yucheng YANG Shenzhen Data Management Center of Planning and Natural Resources Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources)Shenzhen 518000China Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-restoration(Ministry of Natural Resources) Hefei 230088China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China School of Civil Engineering Hefei University of TechnologyHefei 230009China

Remote sensing image scene classification and remote sensing technology applications are hot research *** CNN-based models have reached high average accuracy,some classes are still misclassified,such as“freeway,”“spare residential,”and“commercial_area.”These classes contain typical decisive features,spatial-relation features,and mixed decisive and spatial-relation features,which limit high-quality image scene *** address this issue,this paper proposes a Grad-CAM and capsule network hybrid method for image scene *** Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features,*** using a pre-trained model,hybrid structure,and structure adjustment,the proposed model can recognize both decisive and spatial-relation features.A group of experiments is designed on three popular data sets with increasing classification *** the most advanced experiment,92.67%average accuracy is ***,83%,75%,and 86%accuracies are obtained in the classes of“church,”“palace,”and“commercial_area,”*** research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation ***,Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.

关键词： image scene classification CNN Grad-CAM CapsNet DenseNet

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