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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Aixin Wang, Lin Chen, Guanrong Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China City University of Hong Kong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798350316339

This study investigates the impact of multi-bit function perturbations (MFPs) on the steady-state distribution of Boolean networks (BNs), with a focus on robust stability. First, the algebraic formulation of BNs under MFPs is introduced. Then, a novel stability criterion is proposed, demonstrating that monotonically decreasing perturbations can effectively ensure the stability of BNs against MFPs. A significant advantage of this approach is that it enables robust stability assessment without remodeling the perturbed BNs. Applying this methodology to a simplified Boolean model of the lactose operon in Escherichia coli reveals that MFPs can serve as strategic tools to promote stability within BNs, rather than being merely disruptive. © 2024 IEEE.

关键词： Stability criteria

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital Department of Pathology Wuhan University Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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Reinforcement Learning Behavioral control for Nonlinear Autonomous System

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1561-1573页

作者： Zhenyi Zhang Zhibin Mo Yutao Chen Jie Huang the College of Electrical Engineering and Automation Fuzhou UniversityFuzhou 350108 the Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian ProvinceFuzhou 350108 G+Industrial Internet Institute Fuzhou UniversityFuzhou 350108China

Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear *** this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error ***,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission *** with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural *** the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control *** with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and *** error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.

关键词： Behavioral control mission supervisor nonlinear autonomous system reinforcement learning

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks 57

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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57th Annual Conference on information Sciences and systems, CISS 2023

作者： Wang, Fei Zhang, Xi Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9781665451819

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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7th International Symposium on Autonomous systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G THz Mobile Wireless Networks

IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G ...

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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Language Reasoning in Vision-Language-Action Model for Robotic Grasping

Language Reasoning in Vision-Language-Action Model for Robot...

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2024 China Automation Congress, CAC 2024

作者： Fan, Lingling Chen, Kang Xu, Zhezhuang Yuan, Meng Huang, Ping Huang, Weibing College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350368604

Grasping tasks are crucial for the interaction between the robot and environment, requiring the robot to make autonomous decisions based on environmental conditions and instructions. Recently, multimodal Vision Language Models (VLMs) have demonstrated significant potential for robotic grasping. However, previous work has often been confined to high-level planning due to the gap between the text output of VLMs and the numerical predictions of robot actions, as well as the challenges of transferring rich semantic information from large models to the prediction of grasping actions. In this paper, we co-fine-tune a pre-trained VLM using the mix of large-scale visual language data and robotic grasping trajectory data, integrating model into an end-to-end robot control system to construct a Vision Language Action Model (VLA) capable of generating the robot’s underlying actions. Simultaneously, to fully leverage the prior knowledge and reasoning capabilities of the pre-trained model, we insert a language reasoning structure to bridge the mapping from high-level tasks to low-level actions. This method first deduces the relevant information regarding the target object and the end-effector motions, followed by predicting the grasping actions value with finer granularity based on the language reasoning, thereby enhancing the accuracy of action predictions. A series of grasping experiments demonstrate that our method achieves a high grasping success rate under flexible language instructions and exhibits strong generalization capabilities for unseen objects and scenes. © 2024 IEEE.

关键词： Visual languages

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Transformer-Based Sequence Modeling with Action Discretization for Robotic Grasping

Transformer-Based Sequence Modeling with Action Discretizati...

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2024 China Automation Congress, CAC 2024

作者： Hou, Yaokun Zheng, Song Xu, Zhezhuang Yuan, Meng Huang, Ping Huang, Weibing College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350368604

The powerful representational and sequential modeling capabilities of Transformer have been validated in various fields such as natural language processing. However, how to better address sequential decision-making problems like robotic grasping through the Transformer architecture and achieve autonomous control of robotic grasping remains a longstanding challenge. Models that directly output continuous decision actions for robotic arms often rely on mean squared error (MSE) loss for training, which is unable to effectively handle the randomness and diversity of actions. This limitation leads to the generation of relatively singular actions in certain grasping scenarios, reducing the success rate of grasping. To address this issue, this paper proposes a Transformer-based method for discretizing robotic arm actions, where actions are discretized by dimension to enable autoregressive modeling. Experimental results demonstrate that this method effectively improves the grasping performance of robotic arms in simulation environments. © 2024 IEEE.

关键词： Mean square error

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A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults 13

A Cloud-Edge-Based Multi-Label Learning and Fast Clustering ...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Liu, Qi Yang, Bo Liu, Yuxiang Ma, Kai Guan, Xinping Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： K-means clustering

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