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IEEE International Conference on Unmanned systems (ICUS)

作者： Yan Zhen Mingrui Hao Wendi Sun Science and technology on complex system control and intelligent agent cooperation laboratory Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

The fixed-wing UAV is a non-linear and strongly coupled system. controlling UAV attitude stability is the basis for ensuring flight safety and performing tasks successfully. The non-linear characteristic of the UAV is the main reason for the difficulty of attitude stabilization. Deep reinforcement learning for the UAV attitude control is a new method to design controller. The algorithm learns the nonlinear characteristics of the system from the training data. Due to the good performance, the PPO algorithm is the mainly algorithm of reinforcement learning. The PPO algorithm interacts with the reinforcement learning training environment by gazebo, and improve attitude controller, different from the traditional PID control method, the attitude controller based on deep reinforcement learning uses the neural network to generate control signals and controls the rotation of rudder directly.

关键词： intelligent agents complex systems Reinforcement learning Training TV Research and development Phase shift keying

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The Research on Simulation technology for multi-aircraft cooperative guidance

The Research on Simulation Technology for multi-aircraft coo...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Jing, Li Chao, Shen Jiahui, Tong Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

In this paper, the composition and operation mechanism of a multi-aircraft cooperative simulation system is analyzed, with its formal architecture identified as a discrete event system differential equation system specifications (DEVDESS). Then, the operation and control problem of the cooperative simulation system is converted into the simulation control problem of a specific DEVDESS system. A coordinator-based test method for cooperative simulation is proposed and validated by tests of real-time mathematical simulation. © 2019 IEEE.

关键词： Hardware-in-the-loop simulation

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Parametric Transformation of Timed Weighted Marked Graphs:Applications in Optimal Resource Allocation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 179-188页

作者： Zhou He Ziyue Ma Zhiwu Li Alessandro Giua the College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Institute of Systems Engineering Macao University of Science and TechnologyTaipa 999078MacaoChina the Department of Electrical and Electronic Engineering University of CagliariCagliari 09124Italy

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging *** this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary *** order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent ***,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic *** analysis shows that the developed approach is superior to the heuristic approach.

关键词： Discrete event system resource allocation timed Petri net weighted marked graph

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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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Quantitative evaluation modeling of mobile Ad Hoc networkcommunication service availability under failure|

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International Journal of Modeling, Simulation, and Scientific Computing 2020年第6期11卷 250-267页

作者： Yanfang Fu Yichang Su Zan Wang Dengdeng Guo Fei Dai Lianjiong Zhong Jianing Wei Xi’an Technological University State and Provincial Joint Engineering Lab.of Advanced Network Monitoring and Control Xi’an 710021P.R.China Beijing Electromechanical Engineering Institute Beijing 100074 P.R.China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing 100074 P.R.China

The availability of network communication services is an important evaluation standard to measure the ability of the network to meet the user’s business *** current related research mostly focuses on the evaluation of network reliability,which cannot reflect the logical relationship between failure/maintenance and network performance *** Ad Hoc Network is a multi-hop,centerless and energy-constrained distributed *** to the dynamic change of communication environment and the instability of wireless link,Ad Hoc Network is facing great challenges in service avail*** this thesis,a fault-based quantitative evaluation model of traffic availability in Ad Hoc networks is *** studying the multi-state Markov fault model of communication networks and the performance analysis of delay index based on CSMA\CA protocol,the quantitative evaluation of traffic availability in Ad Hoc networks is *** the availability of tactical unit network under different typical network configuration conditions is analyzed through experiments.

关键词： Network reliability service availability fault model quantitative evaluation

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Behavior-based cooperative control method for fixed-wing UAV swarm through a virtual tube considering safety constraints

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Chinese Journal of Aeronautics 2025年

作者： Siyi YUE Duo ZHENG Mingjun WEI Zhichen CHU Defu LIN School of Astronautics Engineering Beijing Institute of Technology Beijing 100081 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Yungang Street Beijing 100074 China

Unmanned Aerial Vehicle (UAV) swarm collaboration enhances mission effectiveness. However, fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments. Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment, this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints. Considering the fixed-wing UAV swarm flight problem in complex environment, a virtual tube model based on generator curve is established. The tube keeping, centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary. On this basis, a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace. This cooperative control method eliminates the need for respective pre-planned trajectories, reduces communication requirements, and achieves a high level of intelligence. Simulation results show that the proposed behavior-based UAV swarm cooperative control method is able to make the fixed-wing UAV swarm, which is faster and unable to hover, fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes, and the spacing between the UAVs is larger than the minimum safe distance during the flight.

关键词： Unmanned aerial vehicles (UAV) UAV swarm Distributed cooperative control Swarm flight safety Behavior-based method Virtual tube airspace

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Identify Influential Nodes in complex Networks: A K-Orders Entropy-Based Method

SSRN

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SSRN 2023年

作者： Wu, Yali Dong, Ang Ren, Yuanguang Jiang, Qiaoyong Department of Information and Control Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China The School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

Identifying influential nodes is a recognized challenge for the tremendous number of nodes in complex networks. Most of proposed methods detect the influential nodes based on their degree or topological location, which only consider the local or global information of the network causing inaccuracy. In this paper, we propose a k-orders entropy-based method to identify influential nodes. The influence of node is determined by its entropy with local and global information. The entropy reflecting local information is measured by the different order neighbors' information of nodes while the entropy reflecting global information by the betweenness centrality. The experiments conducted on real-world networks demonstrate the proposed method is more accurate than other methods. © 2023, The Authors. All rights reserved.

关键词： complex networks

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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Low Frequency Wide Bandwidth Antenna Design Using the Concept of Lumped Elements Integration 9

Low Frequency Wide Bandwidth Antenna Design Using the Concep...

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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： Xia, Yinfeng Wang, Yanyan Li, Yingsong Xue, Wei Ye, Qiubo Harbin Engineering University College of Information and Communication Engineering Harbin China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China Jimei University School of Information Engineering Xiamen361021 China

ISBN: (纸本)9781728198057

In this paper, we will propose a super wide bandwidth low frequency antenna with a compact size for HF, VHF and UHF applications. The developed low frequency antenna consists of coplanar guide feeding structure, sleeve-like structure with lumped elements, reactance loading in the feeding. The antenna is investigated and evaluated in the HFSS, and the simulation results shows that the proposed antenna has a very wide fractional bandwidth of 191.9% from 10 MHz to 481 MHz for supporting the HF, VHF, UHF and P-band detection system applications in various platform like aircraft, ship and vehicles. © 2020 IEEE.

关键词： Bandwidth

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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