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Congress on Evolutionary Computation

作者： Hao Hao Aimin Zhou Hu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China

Preselection is an important strategy to improve evolutionary algorithms’ performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a pre-selection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.

关键词： Filtering Computational modeling Sociology Training data Evolutionary computation Approximation algorithms Linear programming

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Multi-source dynamic time series data processing method based on comprehensive weighted fusion

Multi-source dynamic time series data processing method base...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)

作者： H. Geng F. Yuan B. Suo Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing People's Republic of China School of Mathematics Southwest Jiaotong University Chengdu People's Republic of China School of Information Engineering Southwest University of Science and Technology Mianyang People's Republic of China

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data.

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Distributed square-root cubature Kalman filter for underwater cyber-physical systems with multiple fading measurements

Distributed square-root cubature Kalman filter for underwate...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Ye Chen Pengpeng Ye Yuan Liang Lin Meng Artificial Intelligence Research Institute of Industrial Technology Nanjing Institute of Technology Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory BeijingChina School of Automotive and Rail Transit Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9781665426220

In this article, we focus on the problem of the distributed nonlinear state estimation for a class of underwater cyber-physical systems with fading measurements. In this article, we propose a modified square-root cubature Kalman filter to tackle with the nonlinear system model. And the diffusion strategy is introduced in this paper to improve the system's estimation performance. To ease the internode communication cost, we propose an event-triggered mechanism to avoid the unnecessary information transmission. The corresponding esti-mation algorithm is also derived in this article. The simulation results show that the proposed algorithm can estimate the target's state accurately with less communication resources.

关键词： Fading channels Costs Simulation Information processing Cyber-physical systems Filtering algorithms Social intelligence

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Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

arXiv

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arXiv 2022年

作者： Liu, Tianying Zhang, Lu Wang, Yang Guan, Jihong Fu, Yanwei Zhao, Jiajia Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai201804 China The Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200438 China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Shanghai200433 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Research on Performance Evaluation Method of UAV Multi-Source Data Fusion Based on Credibility

Research on Performance Evaluation Method of UAV Multi-Sourc...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Hua-pin Geng Jia-hui Tong Beijing Electromechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Based on the analysis of aircraft multi-source test data and the characteristics of common data fusion methods, this paper puts forward a multi-source data fusion performance evaluation process model based on credibility, and studies the key points of test data pre-processing and compatibility test Methods, credibility analysis methods and multi-source test data fusion methods based on credibility, focusing on the credibility analysis method of multi-source data based on evidence distance and the credibility analysis method based on consistency analysis, from the perspective of reliability, objectively measure the weight of multi-source data in fusion, and carry out typical Validation to test the effectiveness of the multi-source test data fusion performance evaluation method based on credibility.

关键词： Quality function deployment Xenon Rail to rail outputs

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Aircraft control Method Based on Deep Reinforcement Learning

Aircraft Control Method Based on Deep Reinforcement Learning

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Data Driven control and Learning systems (DDCLS)

作者： Yan Zhen Mingrui Hao Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

To address the failure of precise overload tracking and anti-interference caused by the difficulty of accurate modeling of a complex aircraft, the controller designing method based on deep reinforcement learning is studied. This paper trained the control network based on the Proximal Policy Optimization (PPO), studied the tracking control problem of the aircraft, and accurately tracked the typical command signals. Fixed-point simulation of the aircraft is performed, with results showing that, in presence of aircraft model parameter variation and external disturbance, the controller based on deep reinforcement learning can achieve accurate tracking of overload commands.

关键词： Aircraft Reinforcement learning Aerospace control Atmospheric modeling Aerodynamics Mathematical model Heuristic algorithms

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UAV Decision-making system Based on the Rough Set theory and the Optimal Genetic Algorithm

UAV Decision-making System Based on the Rough Set theory and...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Wendi Sun Mingrui Hao Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Air combat decision-making is a critical issue in Unmanned Air Vehicle automatic combat. Precise and efficient maneuvering strategies are extremely important for the final victory. Quantitative research has been conducted on the maneuver strategy. In this study, a knowledge-based maneuver action library for air combat was established by the Rough Set Theory, which can help quick response to the battlefield situation. Since not all influence factors of battlefield situation are that significant and the computing source is finite, the rough set model was simplified to increase reaction rate. This paper introduced an advanced genetic algorithm to reduce the attributes of rough sets, taking the purity of each attribute into account, so as to make the condition attribute as close to 1 or 0 as possible. As a result, the reducing accuracy of the whole decision-making system is improved. The simulation results show that the rough set model built in this paper is efficient, feasible and reasonable, and the algorithm can support the air combat maneuver strategy decision system.

关键词： intelligent agents Conferences complex systems systems simulation Rough sets Power system dynamics intelligent control

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Robust Output Synchronization of Discrete-Time Linear-Time-Invariant Multi-agent systems Using Phase Tool

Robust Output Synchronization of Discrete-Time Linear-Time-I...

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European control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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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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