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作者： Mengmeng Zhang Shuanghui Yi Menglong Lin Honghui Chen Science and Technology on Information Systems Engineering Laboratory National Key Laboratory for Complex System Simulation

Currently,with the deepening of development and open of China's border,issues of border joint defense become increasingly *** means of cooperation and coordination are far from being able to align requirements;information systems of different joint defense units are difficult to interconnect and interoperate;the previous results of joint defense informatization are difficult to *** main reason is the lack of border joint defense information construction with a top-level design *** the lens of top-level design,this paper establishes a multi-viewpoint architecture framework and transformation process,develops the as-is and to-be joint defense architecture,and finally analyzes transformation principles from the as-is architecture to to-be *** paper mainly explores the architecture transformation mechanisms under the border joint defense scenario.

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WebGL-based Network Multi-view Visualization Interactive Analysis Method

WebGL-based Network Multi-view Visualization Interactive Ana...

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Computer Information science and Artificial Intelligence (CISAI), International Conference on

作者： Ronghuan Yu Shenhao An Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665406932

The space-ground integrated network is one of the important information infrastructures of the country in the future. In order to display its network structure in all directions and from multiple angles, a multi-view visualization interactive analysis method is proposed. First introduces the relevant basic technology, and then constructs the network dynamic model, which is mainly divided into three parts: link visibility analysis, relative motion analysis and visualization model construction. Finally, the digital earth technology is based on a unified coordinate system and data source. Establish a multi-dimensional view on it and provide a variety of interactive operations. Experimental results show that this method can more comprehensively show the structure of the space-ground integrated network, and help users understand its evolutionary law.

关键词： Earth Solid modeling Analytical models Visualization Information science Three-dimensional displays Soft sensors

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ETM fusion algorithm based on log classification

ETM fusion algorithm based on log classification

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Wenxiang Lin Desheng Liu Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665428781

With the improvement of computer computing power, the shortcomings of the computational inefficiency of the genetic mining algorithm are compensated, and the genetic mining become a research hotspot. In this paper, for the Evolutionary Tree Miner (ETM) algorithm in noise processing can not find the low-frequency effective path and remove the shortage of noise, an ETM fusion algorithm based on log classification is proposed. The algorithm performs process model mining for high-frequency and low-frequency logs separately, and get model fusion based on alignment ideas, Eventually mining the complete process model. Through the simulation of event log data verification, the improved algorithm achieves more complete process model mining than the original ETM algorithm.

关键词： Solid modeling Analytical models Machine learning algorithms Heuristic algorithms Petri nets Genetics Mathematical models

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A DQN-based hyperheuristic algorithm for emergency scheduling of Earth observation satellites

A DQN-based hyperheuristic algorithm for emergency schedulin...

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International Conference on Electronics, Communications and Information technology (CECIT)

作者： Zheng Liu Wei Xiong Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665437585

With the surge of emergency task demand, large-scale Earth observation satellite emergency scheduling has become a practical problem which is urgent to be solved. In view of this situation, this paper analyzes the constrains and conflict relationship in this problem, and proposes a DQN-based hyperheuristic algorithm. The algorithm includes encoding and decoding, deep Q-network and several low-level heuristic operators. A Markov decision model of this problem is established and 7 low-level heuristic operators are designed according the feature of this problem. After training, DQN behaves well when selecting operators for the scheduling scheme. Finally, several groups of comparative experiments verify the algorithm's ability in solving large-scale satellite emergency scheduling problems.

关键词： Training Earth Satellites Heuristic algorithms Markov processes Scheduling Information and communication technology

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A Rapid Evaluation Method of Radar Tracking Accuracy 20

A Rapid Evaluation Method of Radar Tracking Accuracy

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Proceedings of the 2020 4th International Conference on Electronic Information technology and Computer Engineering

作者： Zhongwen Zhao Xu Zhang Guodong Li Science and Technology on Complex Electronics System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781450387811

In order to solve the problem that the accuracy evaluation method based on error grouping is complex and can only be applied to post-processing, a fast judgment analysis method of radar tracking accuracy is proposed. This method decouples the two main processes of accuracy evaluation, namely error estimation and error statistics, and achieves fast analysis of radar tracking accuracy through real-time estimation of local accuracy and fast statistics of average accuracy of whole track. At the same time, this method avoids the discrimination of abnormal errors by applying robust median and median absolute difference estimation, further simplifies the error estimation process, so that it is more suitable for real-time data analysis.

关键词： Accuracy Evaluation Error Grouping Radar Robust Rapid Evaluation

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Recognition of Electromagnetic Signals Based on the Spiking Convolutional Neural Network

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Wireless Communications and Mobile Computing 2022年第0期2022卷

作者： Tao, Shifei Xiao, Shuai Gong, Shuaige Wang, Huifu Ding, Hao Wang, Hao State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang471003 China School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Engineering Training Center Nanjing University of Science and Technology Nanjing210094 China

Feature extraction and recognition of signals are the bases of a cognitive radio. Traditional manual extraction for signals' features becomes difficult in the complex electromagnetic environment. Although convolutional neural networks (CNNs) can extract signal features automatically, they have low accuracy in recognizing electromagnetic signals at low signal-to-noise ratios (SNRs) due to the agility of signals. Considering the great potential of spiking neural networks (SNNs) in classification, a spiking convolution neural network (SCNN) for the recognition of electromagnetic signals is proposed in this paper. The SCNN effectively integrates the extraction ability of spatial features in CNNs and temporal features in SNNs. Since the SCNN is difficult to train, the strategy of surrogate gradient is proposed to train it. By taking the 2-dimensional time-frequency distribution of 6 signals as input, the SCNN can effectively identify different signals at low SNRs. The method proposed in this paper contributes to promote the research and application of SNNs in the recognition of electromagnetic signals. © 2022 Shifei Tao et al.

关键词： Extraction

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A GAN-LSTM Architecture for ECG Anomaly Detection

A GAN-LSTM Architecture for ECG Anomaly Detection

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Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Soumia OUBADI Naçima MELLAL Rachida BOUCHOUAREB Soumia ZERTAL dept. Computer science Research Laboratory on Computer Science's and Complex System University of Oum El Bouaghi Algeria dept. Networks and telecommunication Research Laboratory of Artificial Intelligence and Autonomous Objects Institute of Applied Science & Technology University of Oum El Bouaghi Algeria dept. Computer science Research Laboratory of Artificial Intelligence and Autonomous Objects University of Oum El Bouaghi Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Cardiovascular diseases (CVDs) continue to be a primary cause of global mortality, requiring prompt detection and care to avert serious consequences. Electrocardiograms (ECGs) are vital diagnostic instruments for cardiovascular disorders, yet traditional diagnostic methods encounter considerable obstacles, such as class imbalance and substantial heterogeneity in the available data. This study introduces a novel ECG classification technique utilizing GAN and LSTM. Generative Adversarial Networks (GANs) are employed to produce additional synthetic ECG samples, addressing class imbalance in the training dataset. The suggested design integrates LSTM units into the GAN generator to collect and maintain temporal signal patterns, while anomaly detection is performed using a CNN-LSTM model. Experimental findings indicate a significant enhancement in classification performance, achieving an accuracy of up to 94.74%. The findings indicate that the proposed GAN-LSTM model significantly improves ECG anomaly detection and classification, offering substantial potential for the rapid and precise identification of arrhythmias.

关键词： Training Instruments Mortality Computer architecture Electrocardiography Generative adversarial networks Real-time systems Convolutional neural networks Anomaly detection Long short term memory

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Aircraft Autonomous Positioning Based on a Coarse to Fine Multi-modal Image Registration Scheme

Aircraft Autonomous Positioning Based on a Coarse to Fine Mu...

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2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021

作者： Sun, Fangde Fu, Wenxing Lu, Xiaohui Dong, Jing Han, Songlai Central South University School of Aeronautics and Astronautics Changsha China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing China People's Liberation Army 91039th Unit Beijing China National University of Defense Technology Aerospace Science and Engineering Changsha China

ISBN: (纸本)9781728190037

In this paper, an autonomous positioning method based on coarse-to-fine multi-modal image matching is proposed for UAV navigation in GPS denied environment. Coarse image matching refers to roughly determining the approximate position of the real-time image in the reference image. Fine image matching refers to iterative matching around the position obtained by coarse image matching to achieve more accurate matching results. Besides, RANSAC is adopted to eliminate the outliers of image matching. After getting the result of image matching, we use PNP to estimate the position of the aircraft. The proposed position method has the advantages of high accuracy and reliability. The experimental results show that the average error of the position of the proposed method is 59%. © 2021 IEEE.

关键词： Image matching

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Can Digital Intelligence and Cyber-Physical-Social systems Achieve Global Food Security and Sustainability?

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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