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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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Refined recognition and intelligent smart interference of radar signal

Refined recognition and intelligent smart interference of ra...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Jianquan Yuan Yunong Bu Shuyuan Yang Qingxi Chi Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China Xidian University Xi’an China

ISBN: (纸本)9781728137933

Facing the complex electromagnetic environment on the battlefield, how to effectively identify important radar signals by jammers and how to implement strong and effective electromagnetic interference to the enemy's huge radar detection system under the condition of limited jamming resources will be the difficult problems that need to be solved continuously in the electronic warfare field. The solution of these problems will be the premise of effective interference for wartime jammers. In this paper, the technical gap of traditional jammers is analyzed. For radar signal perception, a refined perception and recognition method is proposed, forming the algorithm idea of multi-scale feature extraction and recognition based on radar fingerprint and multi-channel integrated multi-label recognition and classification based on radar radiation source. For interference implementation, the idea of smart interference based on deep GAN is put forward, laying the ground for further research on cognitive electronic warfare.

关键词： Cognitive electronic warfare Smart interference Wartime application RADAR SIGNALS jammers Recognition (Psychology) Electromagnetic environments Electromagnetic fields electronic warfare PROBLEM SOLUTION PREMISES interference interference Battlefields

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Regularized densely-connected pyramid network for salient instance segmentation

arXiv

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arXiv 2020年

作者： Wu, Yu-Huan Liu, Yun Zhang, Le Gao, Wang Cheng, Ming-Ming TKLNDST College of Computer Science Nankai University Tianjin300350 China ETH Zurich Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the side predictions, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids. A novel multi-level RoIAlign based decoder is introduced to adaptively aggregate multi-level features for better mask predictions. Such strategies can be well-encapsulated into the Mask R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing state-of-the-art competitors by 6.3% (58.6% vs. 52.3%) in terms of the AP metric. The code is available at https://***/yuhuan-wu/RDPNet. © 2020, CC BY-NC-ND.

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Differential evolution with adaptive population size combining lifetime and extinction mechanisms

Differential evolution with adaptive population size combini...

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作者： Zhang, Chunmei Chen, Jie Xin, Bin Cai, Tao Chen, Chen School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9788995605646

Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extinction which regulate DE's population size in an adaptive manner. The population size is adjusted according to the online progress of fitness improvement. Two schemes of inserting new individuals are proposed to match different mechanisms respectively. The performance of these innovations is examined through the optimization of benchmark problems. The results show that the proposed adaptive population sizing strategy is efficient for improving the convergence and efficiency of the DE. © 2011 Asian control Association.

关键词： Optimization

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A fault isolation method using kernel canonical variate analysis-based contribution

A fault isolation method using kernel canonical variate anal...

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第35届中国过程控制会议

作者： Hongxin Wang Lingxia Mu Nan Feng Han Liu Ding Liu Department of Information and Control Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology School of Intelligence Science and Technology University of Science and Technology Beijing

Fault diagnosis and isolation is important for industrial system. In this paper, a kernel canonical variate analysis(KCVA) is proposed for fault isolation. KCVA is originally used as a data dimension reduction technique which can account for nonlinearity and correlations in the industrial dynamical process data. But there are some difficulties using KCVA in the construction of the contribution for the fault isolation. On the one hand, it is difficult to compute the contributions of individual variables because it is scarcely possible to find an inverse mapping from the feature space to the original space. On the other hand, a smearing effect is hardly avoided. To solve the problem, a KCVA-based contributions is proposed using the state subspace and the residual subspace which can isolate the faulty variables effectively. Simulations are conducted on the Tennessee Eastman process to verify the performance of the proposed method.

关键词： Kernel canonical variate analysis contribution plot fault isolation

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

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

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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Distributed simulation system hierarchical design model based on quotient space granular computation

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Zidonghua Xuebao/Acta Automatica Sinica 2010年第7期36卷 923-930页

作者： Chen, Jie Wu, Di Zhang, Juan Department of Automation Beijing Institute of Technology Beijing 100081 China Education Ministry Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China VALORIA Laboratory Department of Computer Science and Technology University of South Brittany Vannes Cedex 56017 France

To answer the problem of ambiguous design levels for large-scale distributed simulation systems, this paper proposes a hierarchical system model based on the quotient space theory. This model consists of a system global design level, an architecture level, and a execution structure level to solve the problem of diverse levels on system design. Furthermore, we deeply analyze the relationship between the granules of different levels in both horizontal and vertical directions, and integratedly consider simulation tasks, resources and services. Finally, the model is verified based on a real simulation system design example, and the results illustrate that the flexibility and reusability of the system can be improved with the application of the hierarchical design model. Copyright © 2010 Acta Automatica Sinica. All rights reserved.

关键词： systems analysis

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Network Parameter Generation for One-Shot Object Detection

Network Parameter Generation for One-Shot Object Detection

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IEEE International Conference on Unmanned systems (ICUS)

作者： Kang Wang Jiajia Zhao Yang Wang Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineer Institute Beijing China School of Information Engineering Nanchang University China

ISBN: (纸本)9781728137933

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection.

关键词： Object detection Detectors Head Training Feature extraction Task analysis Training data

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A Cooperative Route Planning Method for Multi-UAVs Based-on the Fusion of Artificial Potential Field and B-spline Interpolation

A Cooperative Route Planning Method for Multi-UAVs Based-on ...

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Chinese control Conference (CCC)

作者： Wenbin Bai Xiande Wu Yaen Xie Yu Wang Han Zhao Kefan Chen Yong Li Yong Hao College of Aerospace and Civil Engineering Harbin Engineering University Harbin China College of Automation Harbin Engineering University Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Qian Xuesen Laboratory of Space Technology Beijing China

This paper deals with the route planning problem for multi-UAVs formation. It provides a method of improved artificial potential field by introducing a longitudinal random factor and B-spline interpolation. The longitudinal random factor is used to solve the problem of falling into local minimum, when the B-spline interpolation is used to smooth the planned route. The simulated results show that the proposed method can solve the route planning problem of multi-UAVs.

关键词： Planning Force Space missions Splines (mathematics) Interpolation Heuristic algorithms Unmanned aerial vehicles

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