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High-adaption locomotion with stable robot body for planetary exploration robot carrying potential instruments on unstructured terrain

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Chinese Journal of Aeronautics 2021年第5期34卷 652-665页

作者： Kang XU Shoukun WANG Junzheng WANG Xiuwen WANG Zhihua CHEN Jinge SI School of Automation Beijing Institute of Technology.Beijing 100081China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of TechnologyBeijing 100081China Key Laboratory Ministry of Industry and Information Technology Beijing Institute of TechnologyBeijing 100081China

There is a strong demand for Planetary Exploration Mobile robots(PEMRs)that have the capability of the traversability,stability,efficiency and high load while tackling the specialized tasks on planet *** this paper,an electric parallel wheel-legged hexapod robot which has high-adaption locomotion on the unstructured terrain is ***,the hybrid control framework,which enables robot to stably carry the heavy loads as well as to traverse the uneven terrain by utilizing both legged and wheeled locomotion,is also *** on this framework,robot controls the multiple DOF leg for performing high-adaption locomotion to negotiate obstacles via Gait Generator(GG).Additionally,by using Whole-Body control(WBC)of framework,robot has the capability of flexibly accommodating the uneven terrain by Attitude control(AC)kinematically adjusting the length of legs like an active suspension system,and by Force/torque Balance control(FBC)equally distributing the Ground Reaction Force(GRF)to maintain a stable *** simulation and experiment are employed to validate the proposed framework with the physical system in the planetary analog ***,to smoothly demonstrate the performance of robot transporting heavy loads,the experiment of carrying 3-person load of about 240 kg is deployed.

关键词： Attitude control Gait generation High-adaption locomotion Obstacle avoidance Planetary exploration robot

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ICP algorithm point cloud registration method based on a new improved Harris Hawk algorithm

ICP algorithm point cloud registration method based on a new...

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2021 China Automation Congress, CAC 2021

作者： Wang, Chen Jiao, Shangbin Gao, Rui Li, Yuxing School of Automation and Information Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of Electronic and Electrical Engineering Baoji University of Arts and Sciences Baoji China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Baoji China

ISBN: (纸本)9781665426473

To address shortcomings of ICP algorithm, which is sensitive to the initial position and easy to fall into local optimum, we propose a Harris Hawks Optimizer (HHO) point cloud registration method combining PCA preprocessing and parallel population initialization. The PCA method is used to align the point clouds with the main axes and perform the main axes correction. Since the six input variables in the population initialization stage of HHO point cloud registration are different and independent, the initial population of HHO algorithm is initialized by the parallel initialization method. The results show that the improved algorithm is able to successfully overwhelm the fault of ICP algorithm, and its global seek performance and optimization accuracy are more advantageous, and the algorithm operates efficiently, with high registration accuracy and robustness. © 2021 IEEE

关键词： PCA HHO Parallel Computing ICP

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An online state and parameter estimation of dynamic state space model of high speed train

International Journal of Control and Automation

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International Journal of control and Automation 2016年第9期9卷 51-62页

作者： Xie, Guo Zhang, Dan Hei, Xinhong Qian, Fucai Takahashi, Sei Nakamura, Hideo Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi710048 China College of Science and Technology Nihon University 7-24-1 Narashinodai Funabashi Chiba274-8501 Japan

In this paper, the state space model of high speed train is established to describe its nonlinear dynamic characteristics, whose parameters are disturbed by noise with an arbitrary distribution, and an online state and parameter identification method is proposed based on a parameters set by employing Bayesian theory and Particle Filter (PF). Firstly, the priori probabilities of all of the possible parameters are set to be equal, and the predicted states with different parameters are estimated using PF. Then the posterior probabilities of the parameters are updated by analyzing the characteristic of the measurement noise using Bayesian theory. Finally, the system sate and parameter are estimated by weighted summing all of the parameters and predicted states. The simulation results indicate that the proposed method can estimate the states and parameters of high speed train online and adaptively. © 2016 SERSC.

关键词： State space methods

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Sentence-length informed method for active learning based resource-poor statistical machine translation 3

Sentence-length informed method for active learning based re...

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3rd CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2014

作者： Du, Jinhua Wang, Miaomiao Zhang, Meng School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an710048 China

ISBN: (纸本)9783662459232

This paper presents a simple but effective sentence-length informed method to select informative sentences for active learning (AL) based SMT. A length factor is introduced to penalize short sentences to balance the "exploration" and "exploitation" problem. The penalty is dynamically updated at each iteration of sentence selection by the ratio of the current candidate sentence length and the overall average sentence length of the monolingual corpus. Experimental results on NIST Chinese–English pair and WMT French-English pair show that the proposed sentence-length penalty based method performs best compared with the typical selection method and random selection strategy. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Iterative methods

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Redundancy reduction penalty term of loss function in deep neural network

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Knowledge-Based systems 2025年

作者： Xueheng Hu Shuhuan Wen H.K. Lam Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Qinhuangdao 066004 Hebei Province China The Department of Engineering King’s College London London WC2R 2LS United Kingdom

In this paper, we propose a novel regularization algorithm that is introduced as a penalty term to the loss function. Differing from conventional L1 and L2 regularization methods, our approach does not aim to diminish the weights of individual neurons or enforce sparsity by driving certain neurons to zero. Instead, it functions by increasing the differences between neurons and enhancing the diversity of neurons within each layer. Our method incorporates ensemble learning techniques by treating the layer weight matrix as a collective learning model, where each neuron serving as a weak learner within the layer. The proposed algorithm improves the performance of DCNN by simultaneously considering the distance between multiple filters in the same layer. This algorithm reduces the redundancy of the parameter layer filters in DCNN and enhances its robustness. The penalty term proposed by our algorithm dynamically adjusts its value in a cyclical manner, compelling the neural network to navigate away from its current gradient state. In the parameter space, different weights correspond to different locations. The proposed algorithm quantifies the distance between neurons and iteratively increases the distance between neurons during thereby encouraging greater diversity within the network. Experimental evaluations demonstrate the effectiveness of our algorithm in enhancing neural network performance without requiring adjustments to other hyper-parameters.

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Research on the weak Signal Detection Method Based on Adaptive Vibrational Resonance 35

Research on the weak Signal Detection Method Based on Adapti...

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第35届中国控制会议

作者： JIAO Shang-bin SUN Di LIU Ding HUANG Wei Chao National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

In this paper,the weak signal detection under a stable noise is investigated based on bistable vibrational resonance(VR) which is driven by a high frequency *** the one hand,the energy of the high frequency drive signal is transferred to the low frequency weak signal when VR occurs;on the other hand,the control of stochastic resonance(SR) is achieved based on VR,which transfers more noise energy into useful signal *** addition,considering the requirements of real-time detection,the amplitude and frequency of the high frequency drive signal are optimized by the knowledge-based particle swarm optimization(KPSO),which takes the mean signal-noise-ratio(MSNR) of output as the fitness function,and the property that VR system produces the best resonance effect just when the valid system parameter a(B,Ω) is greater than zero as ***,the parameter compensation is combined to achieve multi-high frequency weak signals detection with a stable ***,the method is applied to the vibration fault diagnosis of a mono-crystalline silicon furnace,and the experiment results show the effectiveness and practicability of the method.

关键词： Adaptive vibrational resonance weak signal detection knowledge-based particle swarm optimization

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A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

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Defence Technology（防务技术） 2022年第2期18卷 183-193页

作者： Yu-xing Li Shang-bin Jiao Bo Geng Qing Zhang You-min Zhang School of Automation and Information Engineering Xi’an University of TechnologyXi’an710048China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an710048China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversityMontrealQuebec H3G 1M8Canada

Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault *** this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed *** results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and *** is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.

关键词： Nonlinear dynamic Refined composite multi-scale dispersion entropy(RCMDE) Multi-scale dispersion entropy(MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy(MPE) Classification of ship-radiated noise

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A Novel Soft Switching Two-Stage Heating Power Supply Topology for Silicon Growth Furnace 35

A Novel Soft Switching Two-Stage Heating Power Supply Topolo...

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第35届中国控制会议

作者： GUO Xin REN Hai-Peng LIU Ding SUO Jun PENG Shu-Tao School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Lab.of Complex System Control and Intelligent Information Processing Shaanxi Electric Power Research Institute

ISBN: (纸本)9781509009107

Mono-crystalline silicon is widely applied in the photovoltaic and the integrated circuit *** power supply,as the key element of the mono-crystalline silicon growth furnace,plays an important role in the performance of the whole *** this paper,we propose a new kind of two-stage silicon growth furnace heating power supply topology *** first stage three phase rectifier employs the compound active clamp zero voltage soft switching(CACZVS) three-phase power factor correction(PFC) converter to achieve unity power factor and DC bus voltage regulation,and the second stage DC/DC converter employs phase shifted full bridge zero voltage and zero current soft switching converter(FB-ZVZCS) to regulate the output *** with the heating power supply of non-controlled rectifier type or three-phase power factor correction circuit using hard switching,the proposed two-stage converter structure can achieve high power factor,high efficiency and more flexibility of *** simulation results show the correctness of the analysis.

关键词： Silicon Growth Furnace Heating Power Supply CACZVS three phase PFC converter FB-ZVZCS converter

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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