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Robust flocking for non-identical second-order nonlinear multi-agent systems

Autonomous intelligent Systems 2021年第1期1卷 81-90页

作者： Xiuxian Li Housheng Su Li Li Department of Control Science and Engineering College of Electronics and Information Engineeringand Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China Institute for Advanced Study Tongji UniversityShanghai 200092China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of ChinaHuazhong University of Science and TechnologyLuoyu Road 1037Wuhan 430074China

This paper investigates the robust flocking problem for second-order nonlinear systems with a leader and external *** contrast with most of second-order systems in the literature,the intrinsic dynamics here are nonlinear and non-identical that depend not only on the velocity but also on the position,which is more ***,the interaction topology is undirected and *** that the leader’s velocity may be constant or time-varying,two distributed flocking control laws have been proposed for two cases to make the differences of the velocities between all followers and the leader approach to zero *** proposed distributed flocking control laws are both model-independent which results in the effectiveness of the controllers to cope with the different intrinsic dynamics of the followers and the leader under some assumptions on boundedness of several *** example is given to illustrate the validity of the theoretical results.

关键词： Flocking Multi-agent systems Second-order nonlinear systems Non-identical dynamics

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Deep Reinforcement Learning With a Stage Incentive Mechanism of Dense Reward for Robotic Trajectory Planning

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第6期53卷 3566-3573页

作者： Peng, Gang Yang, Jin Li, Xinde Khyam, Mohammad Omar Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430070 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710300 Peoples R China Southeast Univ Sch Automat Nanjing 210000 Peoples R China Cent Queensland Univ Sch Engn & Technol Melbourne Vic 3000 Australia

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning in random working environments, we present three dense reward functions. These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to speed up the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration. Second, a stride reward function is proposed to improve the stability of the learning process by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including a hard-stage incentive reward function and a soft-stage incentive reward function. Extensive experiments show that the soft-stage incentive reward function is able to improve the convergence rate, get higher mean reward and lower standard deviation after convergence.

关键词： Deep reinforcement learning (DRL) dense reward function stage incentive mechanism trajectory planning

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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Asynchronous spiking neural P systems with local synchronization of rules

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INFORMATION SCIENCES 2022年 588卷 1-12页

作者： Wu, Tingfang Zhang, Luping Lyu, Qiang Jin, Yu Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Zhejiang Univ Sch Math Sci 866 Yuhangtang Rd Hangzhou 310058 Peoples R China

Asynchronous spiking neural P (AsynSN P) systems are a class of distributed and parallel computational models working in a non-synchronized mode, inspired by the mechanism of information processing and communication underlies biological neurons by spikes, where at each computation step, any neuron with applicable rules is not obligatory to exe-cute an applicable rule. It remains an open problem whether AsynSN P systems using stan-dard spiking rules (the execution of the rule only produces a spike) are equivalent in power to Turing machines. In this work, the control mechanism of local synchronization at the rule level is introduced into AsynSN P systems. Namely, there are some given locally syn-chronous sets of rules;if a rule in such set is executed, then all applicable rules residing in the same set should be executed simultaneously. The computational power of AsynSN P systems with local synchronization of rules is examined. It is demonstrated that with local synchronization of rules, both general and unbounded AsynSN P systems are Turing uni-versal, whereas bounded AsynSN P systems can only characterize the family of semilinear sets of numbers, using standard spiking rules. These results demonstrate the great poten-tial for the local synchronization of rules to improve the computational capability of AsynSN P systems.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Membrane computing Spiking neural P system Synchronization Local synchronization

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Evolution of controllers Under a Generalized Structure Encoding/Decoding Scheme With Application to Magnetic Levitation System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第9期69卷 9655-9666页

作者： Xin, Bin Wang, Yipeng Xue, Wenchao Cai, Tao Fan, Zhun Zhan, Jiaoyang Chen, Jie Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Shantou Univ Minist Educ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this article. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indicators, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method.

关键词： Optimization Design methodology control systems Tuning Magnetic levitation Signal processing Periodic structures Automatic design of controllers disturbance-rejection intelligent optimization algorithms magnetic levitation system (MLS) mixed-variable optimization

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Association Analysis-Based Cybersecurity Risk Assessment for Industrial control Systems

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IEEE SYSTEMS JOURNAL 2021年第1期15卷 1423-1432页

作者： Qin, Yuanqing Peng, Yuan Huang, Kaixing Zhou, Chunjie Tian, Yu-Chu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4001 Australia

With the adoption of various information and communication technologies and commercial off-the-shelf components, industrial control systems (ICSs) become highly vulnerable to cyberattacks. Dynamic cybersecurity risk assessment (CSRA) plays a vital role in the security protection of ICSs. To reduce the complexity of the modeling process in the dynamic CSRA, an association analysis-based CSRA approach is proposed in this article. It designs a three-layer association network (AN) to infer the probabilities of security incidents. The parameters of the AN are derived through mining the data of historical attack records. From a distance correlation analysis of the process data of the target system, an association matrix is obtained between the system state variables and the key security variables to quantify the cybersecurity risk of the system. A case study is conducted on a coupling tanks control system to demonstrate the effectiveness and timeliness of the proposed approach.

关键词： Risk management Computer security Adaptation models control systems Correlation Data mining Association network (AN) data driven industrial cybersecurity risk assessment

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Fast multi-parametric imaging in abdomen by B1+corrected dual-flip angle sequence with interleaved echo acquisition

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MAGNETIC RESONANCE IN MEDICINE 2022年第5期87卷 2194-2208页

作者： Peng, Hao Cheng, Chuanli Wan, Qian Jia, Sen Wang, Shuai Lv, Jianxun Liang, Dong Liu, Wenzhong Liu, Xin Zheng, Hairong Zou, Chao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Key Lab Magnet Resonance & Multimodal Imaging Gua Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

Purpose To achieve simultaneous T-1,(w)/proton density fat fraction (PDFF)/R2* mapping in abdomen within a single breadth-hold, and validate the accuracy using state-of-art measurement. Theory and Methods An optimized multiple echo gradient echo (GRE) sequence with dual flip-angle acquisition was used to realize simultaneous water T-1 (T-1,(w))/PDFF/R2* quantification. A new method, referred to as "solving the fat-water ambiguity based on their T-1 difference" (SORT), was proposed to address the fat-water separation problem. This method was based on the finding that compared to the true solution, the wrong (or aliased) solution to fat-water separation problem showed extra dependency on the applied flip angle due to the T-1 difference between fat and water. The B1+ measurement sequence was applied to correct the B1+ inhomogeneity for T-1,(w) relaxometry. The 2D parallel imaging was incorporated to enable the acquisition within a single breath-hold in abdomen. Results The multi-parametric quantification results of the proposed method were consistent with the results of reference methods in phantom experiments (PDFF quantification: R-2 = 0.993, mean error 0.73%;T-1,(w) quantification: R-2 = 0.999, mean error 4.3%;R2* quantification: R-2 = 0.949, mean error 4.07 s(-1)). For volunteer studies, robust fat-water separation was achieved without evident fat-water swaps. Based on the accurate fat-water separation, simultaneous T-1,(w)/PDFF/R2* quantification was realized for whole liver within a single breath-hold. Conclusion The proposed method accurately quantified T-1,(w)/PDFF/R2* for the whole liver within a single breath-hold. This technique serves as a quantitative tool for disease management in patients with hepatic steatosis.

关键词： B1+ inhomogeneity fat-water separation liver steatosis multi-parametric quantification

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A general end-to-end diagnosis framework for manufacturing systems

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National Science Review 2020年第2期7卷 418-429页

作者： Ye Yuan Guijun Ma Cheng Cheng Beitong Zhou Huan Zhao Hai-Tao Zhang Han Ding School of Artificial Intelligence and Automation MOE Key Lab of Intelligent Control and Image Processing Huazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven,end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts,indicating its potential use as a critical cornerstone in smart manufacturing.

关键词： manufacturing systems deep learning diagnosis and monitoring

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Distributed Adaptive Containment control for Coupled Reaction-Diffusion Neural Networks With Directed Topology

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6320-6330页

作者： Su, Housheng Qiu, Qian Chen, Xia Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

In this article, we consider the problem of distributed adaptive leader-follower coordination of partial differential systems (i.e., reaction-diffusion neural networks, RDNNs) with directed communication topology in the case of multiple leaders. Different from the dynamical networks with ordinary differential dynamics, the design of adaptive protocols is more difficult due to the existence of spatial variables and nonlinear terms in the model. Under directed networks, a novel adaptive control protocol is proposed to solve the containment control problem of RDNNs. By constructing proper Lyapunov functional and adopting some important prior knowledge, the stability of containment for coupled RDNNs is theoretically proved. Furthermore, a corollary about the leader-follower synchronization with a leader for coupled RDNNs with directed communication topology is given. In the end, two numerical examples are provided to illustrate the obtained theoretical results.

关键词： Topology Synchronization Protocols Neurons Directed graphs Biological neural networks Network topology Adaptive control containment control directed topology reaction-diffusion neural network

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Hybrid Relation Guided Set Matching for Few-shot Action Recognition

arXiv

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arXiv 2022年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Tang, Mingqian Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China

Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal alignment strategies. Nevertheless, they are limited in that (a) learning individual features without considering the entire task may lose the most relevant information in the current episode, and (b) these alignment strategies may fail in misaligned instances. To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric. The purpose of the hybrid relation module is to learn task-specific embeddings by fully exploiting associated relations within and cross videos in an episode. Built upon the task-specific features, we reformulate distance measure between query and support videos as a set matching problem and further design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. By this means, the proposed HyRSM can be highly informative and flexible to predict query categories under the few-shot settings. We evaluate HyRSM on six challenging benchmarks, and the experimental results show its superiority over the state-of-the-art methods by a convincing margin. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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