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IEEE International Conference on Unmanned systems (ICUS)

作者： Zirui Wu Zhen Li Dong Zhu Qiuhan Liao Weiran Yao School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China Chengdu CHG Robots & Intelligent Equipment Institute Co. Ltd Chengdu China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

The task allocation for multiple robots is a critical component in the coordination of unmanned clusters. The existing heuristic algorithms are hard to achieve satisfactory results in large-scale problems, and reinforcement learning-based methods face challenges in ensuring the rationality of reward design. This paper introduces a model based on multi-head attention and graph neural networks to address the schedule-dependent multi-robot task allocation problem, trained using unsupervised learning techniques. This model can be trained with varying numbers of robots and tasks without necessitating changes to its structure or parameters. In the experiment of this paper, the model is trained under two different conditions, and the performance is evaluated across six different problem scales. Comparing the proposed model against greedy algorithms and genetic algorithms, the results demonstrate that the proposed model has sianiflcant advantages in overall performance.

关键词： Greedy algorithms Robot kinematics Heuristic algorithms Computational modeling Reinforcement learning Graph neural networks Resource management Unsupervised learning Faces Genetic algorithms

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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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A Hierarchical Graph Search Method for Path Planning of Unmanned Ground Vehicle for Freight Transportation

A Hierarchical Graph Search Method for Path Planning of Unma...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Jianing Hu Zhen Li Sen Guo Weiran Yao School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China CRRC Nanjing Puzhen Co. Ltd Nanjing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

For the problem of collision volume change when forklift unmanned vehicles work in complex storage environments, a multi-level graph search path planning method is proposed. The first level is the path planning of the Unmanned Ground Vehicle (UGV) body, which uses the grid map and the A * algorithm to plan the path of the UGV transferring in the scene; the second level is the joint path planning of the UGV and the object being carried, to avoid overcomplicating this issue, the unmanned vehicle will be limited to pushing the cargo only forwards. The third level is the path planning when the unmanned vehicle carries multiple objects, which decomposes the whole scene into multiple sub-scenes, plans the paths of the unmanned vehicle in the sub-scenes separately, and searches for the optimal combination of sub-scenes. Higher level searches will invoke lower level algorithms and provide a virtual scene and target as input. The experimental results show that this multilevel path planning method is optimal and complete in solving the forklift unmanned vehicle for handling a single object, and can give a feasible solution for handling multiple objects.

关键词： Costs Transportation Search problems Path planning Land vehicles complexity theory Autonomous vehicles

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Cooperative Collision Avoidance Method for Multi-UAV Based on Kalman Filter and Model Predictive control

Cooperative Collision Avoidance Method for Multi-UAV Based o...

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International Conference on Unmanned systems and Artificial Intelligence

作者： Hanqiao Huang Huan Zhou Meiyun Zheng Cheng Xu Xiaofeng Zhang Wei Xiong Unmanned System Research Institute Northwest Polytechnical University Xi'an China Aeronautical Engineering College Air Force Engineering University Xi'an China School of Marine Science and Technology Northwest Polytechnical University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China UAV Researc Center Army Aviation Institute Beijing China

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

Collision avoidance is the primary problem to be solved in formation flight of multiple Unmanned aerial vehicles(UAVs). Firstly, a cooperative collision avoidance architecture of multiple UAVs is designed according to the requirement of autonomous collision avoidance of single UAV. Then a new cooperative collision avoidance method of multiple UAVs based on Kalman filter and model predictive control(MPC) is proposed. In this method, extended Kalman filter(EKF) is used to estimate the state of obstacles and target points in uncertain environment space, and to predict the trajectory of obstacles and target points. At the same time, relevant performance index functions and constraints are set up. On the basis of sharing environmental information, model predictive control strategy is used to guide and make cooperative collision avoidance decisions for multiple UAVs. The simulation results show that the proposed method is effective in uncertain environment perception and UAV collision avoidance, and the cooperative mechanism has obvious advantages.

关键词： UAV cooperative decision collision avoidance Kalman filter state estimation MPC Collision avoidance Kalman filters Unmanned Aerial Vehicle State estimation Maximum Allowable Concentration drone honey bees environmental perception Formation flight

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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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WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Low-Light Enhancement

WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Lo...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Qiangqiang Wang Peiliang Huang Lei Li Longfei Han AHU-IAI AI Joint Laboratory Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Automation Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Technology and Business University Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal illumination. Currently, deep neural networks have become the mainstream approach for image processing. However, recent works have devoted considerable efforts to designing high-performance models, which often come with high computational complexity and inference time, making real-time processing unfeasible. We observed that some convolutional methods are due to the need for deep layers which results in a large number of parameters. Moreover, enhancing details and removing noise in low-light images remains an open challenge. In order to solve the above problems, we propose a lightweight baseline that combines CNN and sparse grid attention transformer blocks to enable the model to capture a global receptive field at an early stage. Specifically, we propose a High-Frequency Wavelet-aware Block(HFWB) that focuses on processing high-frequency information in the wavelet domain to refine details and suppress noise. With a processing time of only 10.6ms, the performance of our model outperforms that of the current state-of-the-art lightweight models on benchmark low-light datasets. Compared to state-of-the-art models in the LOL dataset, our model achieves a reduction in inference time of over 90% and requires only about 1% of the FLOPS.

关键词： Wavelet transforms Visualization Wavelet domain Noise Lighting Benchmark testing Transformers

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Impact Time and Angle control Guidance Based on Cubic Bézier Curves: Theory and Experiment

SSRN

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SSRN 2024年

作者： Li, Heng Lv, Xiaokang Yu, Jianglong Zhang, Kun Wang, Yanyan Dong, Xiwang Institute of Artificial Intelligence Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China School of Astronautics Beihang University Beihang University Beijing100191 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

A trajectory-shaping guidance strategy with constraints on impact time, impact angle, and maximum lateral acceleration is proposed based on a cubic Bézier curve fitting method. Firstly, an optimal guidance model is formulated, in which the Bézier curves are employed to fit the guidance trajectory, ensuring compliance with the required constraints. The manipulation transforms the optimal guidance problem into an optimal Bézier guidance trajectory design problem and a trajectory tracking problem. Then, by parameterizing the Bézier curves and analyzing the trajectory constraints in detail, the optimal Bézier guidance trajectory design is transformed into optimizing the control parameters of the Bézier curves. Moreover, a multi-layer zeroth-order Adam trajectory optimization algorithm based on general and fine tuning is introduced to determine the control parameters without relying on gradient information. Subsequently, a hyperbolic tangent vector field path tracking guidance algorithm is proposed to follow the designed trajectory and fulfill the guidance missions. Finally, numerical simulations and equivalent physical experiments verify the effectiveness of the proposed guidance strategy. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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A New Modeling Reference Direct Adaptive Sliding Mode control for Electromechanical Actuator

A New Modeling Reference Direct Adaptive Sliding Mode Contro...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Hao Li Lihua Dou Zhong Su Jie Chen School of Automation and the Key Laboratory of Complex System Intelligent Control and DecisionMinistry of EducationBeijing Institute of Technology School of Automation Beijing Information Science & Technology University

A new model reference direct adaptive sliding mode control(MRDASMC) approach for electromechanical actuator(EMA) is *** with parameter variation,external load disturbance,nonlinear structure and unmodelled dynamics in the system are treated as a lumped *** lumped uncertainty is estimated without the bounds known in advance.A switching function is employed to compensate for the estimated error of the lumped *** the issue of chattering in classical SMC is suppressed as the amplitude of the switching function is tuned with the tracking error by adaption *** adaption law contains a fading factor which can prevent saturation of the control *** stability of the control system is guaranteed by Lyapunov *** results show that the proposed controller can provide favorable performance and is robust to system uncertainties.

关键词： Model reference adaptive sliding mode control Electromechanical actuator Fading factor Saturation Chattering.

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