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IEEE Instrumentation and Measurement Technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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Target tracking algorithm with adaptive learning rate complementary filtering

Target tracking algorithm with adaptive learning rate comple...

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第三十九届中国控制会议

作者： Yulei Pan Yongqiang Bai Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

The Correlation filtering algorithm is not effective for fast deformation and fast movement. It is easy to lose when encountering problems such as occlusion. However, it has a good advantage of dealing with situations such as motion blur and lighting changes. A tracking algorithm based on color statistical features has a good effect on the rotation and translation of *** Staple algorithm combines the two algorithms to track using complementary fusion, but it also does not handle the occlusion and other issues well. In this paper, based on the Staple algorithm, the average peak correlation energy（APCE） and the maximum response are introduced. The value is used as the tracking confidence, and a detector using a support vector machine（SVM） is *** the tracking confidence is low, the target is blocked or moved violently. At this time, the detector works,and the search area is expanded around the original area for the target. At the same time, because the traditional tracking algorithm uses a fixed learning rate to update the template, this paper uses an adaptive tracking learning rate. When the tracking confidence is low, the update speed of the target model is reduced, which can effectively deal with the occlusion deformation in the tracking process. OTB100 benchmark experiments show that this method can solve the occlusion problem during target tracking. The degree of change is robust and stability.

关键词： Target Tracking Complementary Filtering Re-detection Adaptive Learning Rate

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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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Self-Attention based Temporal Intrinsic Reward for Reinforcement Learning

Self-Attention based Temporal Intrinsic Reward for Reinforce...

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Chinese Automation Congress (CAC)

作者： Zhuo Jiang Daiying Tian Qingkai Yang Zhihong Peng School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781665426480

This paper proposes a self-attention based temporal intrinsic reward model for reinforcement learning (RL), to synthesize the control policy for the agent constrained by the sparse reward in partially observable environments. This approach can solve the problem of temporal credit assignment to some extent and deal with the low efficiency of exploration. We first introduce a sequence-based self-attention mechanism to generate the temporary features, which can effectively capture the temporal property of the task for the agent. During the training process, the temporary features are employed in each sampled episode to elaborate the intrinsic rewards, which is combined with the extrinsic reward to help the agent learn a feasible policy. Then we use the meta-gradient to update this intrinsic reward model in order that the agent can achieve better performance. Experiments are given to demonstrate the superiority of the proposed method.

关键词： Training Automation Reinforcement learning Task analysis

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Fast Trajectory Planning for Dubins Vehicles Under Cumulative Probability of Radar Detection

SSRN

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SSRN 2022年

作者： Li, Zhuo You, keyou Sun, Jian Song, Shiji State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Automation and BNRist Tsinghua University Beijing100084 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

This paper studies a minimum-time trajectory planning problem under radar detection, where a Dubins vehicle aims to attack a target with a limited probability of being detected. Since the probability is accumulated along the vehicle's trajectory in an integral form, we have to address a non-convex constrained functional optimization problem. To this end, Pontryagin's minimum principle is first adopted to derive the optimality condition, based on which we obtain a set of parameterized trajectories that contains all the optimal ones. Then, fast algorithms are proposed to approximately compute a minimum-time trajectory among the set. Simulations are performed to validate the effectiveness and efficiency of the proposed planning algorithms. © 2022, The Authors. All rights reserved.

关键词： Probability

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Data-driven resilient predictive control under denial-of-service

arXiv

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arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method. © 2021, CC BY.

关键词： Model predictive control

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Target Assignment Optimization of Heterogeneous Sensor Networks in Urban Environment

Target Assignment Optimization of Heterogeneous Sensor Netwo...

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第三十九届中国控制会议

作者： Chen Chen Cheng Zihao Xu Wei School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

Preventing and controlling Low-Slow-Small(LSS) targets are problems to be solved due to their good concealment in urban environment. Consequently, the problem of target assignment is studied in this paper. Firstly, a target assignment model based on heterogeneous sensor networks is proposed. The characteristics of LSS targets and the influence of environmental factors such as climate and terrain are considered on this model. At the same time, the detection capabilities of different detection nodes and collaborative tracking capabilities are also considered. Secondly, an intelligent optimization algorithm GSAPSO based on PSO is proposed to maximize tracking effect of the sensors. Finally, the simulations are used to compare the GSAPSO algorithm with the traditional PSO and GAPSO algorithms. The results demonstrate that the proposed algorithm has better tracking effect.

关键词： Target assignment Urban environment Heterogeneous sensor networks PSO

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Closed-form integration of IMU error state covariance for optimization-based Visual-Inertial State Estimator

Closed-form integration of IMU error state covariance for op...

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第三十九届中国控制会议

作者： Xingbo Wang Zhihong Peng Lele Xi School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visualinertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.

关键词： visual-inertial state estimator pre-integration

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Trajectory generation for quadrotor while tracking a moving target in cluttered environment

Trajectory generation for quadrotor while tracking a moving ...

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第三十九届中国控制会议

作者： Lele Xi Zhihong Peng Lei Jiao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, we present a trajectory generation method for quadrotor while tracking a moving target with relative pattern. Compared to existing methods, safe flying zone, vehicle’s physical limits and smoothness are considered to ensure obstacles avoidance and real tracking flight. To tackle with the cluttered environment, a parallel Particle Swarm Optimization algorithm is applied to calculate the safe feasible waypoints for quadrotor with consideration of target’s predicted state and the realistic tracking pattern, as well as safety. Then, a multiple waypoints constraints motion planning method is applied and embed into a cost function for solving the problem of trajectories generation and robust tracking for quadrotor via convex optimization approach, ensuring the real-time planning, with the geometrical constraint of safe flight corridor for quadrotor. The effectiveness of proposed method is verified by numerical simulation experiments.

关键词： Target tracking Trajectory generation Waypoints constraint Convex optimization

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Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

Generative Adversarial Network for Image Deblurring Using Co...

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第32届中国控制与决策会议

作者： Ye Ji Yaping Dai Junjie Ma Kaixin Zhao Yanyan Cheng School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

The research of image deblurring plays an important role in the digital image *** order to reduce image blurring problems,a content constraint loss（CCL） function in the generative adversarial network（GAN）is *** SSIM loss and the perceptual loss constitute the CCL function,which makes the trained generative model *** CCL function as the content constraint loss component and the adversarial loss component constitute the total *** total loss is optimized by the iterative training to further improve the stability of the network model,and the image blurring will be *** the test experiment of the open source image dataset MNIST,CIFAR10/100 and CELEBA,the CCL function is used as the content constraint loss component of the generative adversarial network,the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.

关键词： Image deblurring Generative adversarial network Content constraint loss Adversarial loss

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