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第40届中国控制会议

作者： ShiLong Cheng Yule Hu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Faculty of Engineering China University of Geosciences

Measurement while drilling(MWD) is a widely used signal transmission technology in the drilling *** present,the domestic pulse identification method is relatively simple,using the basic threshold method to identify,only set the threshold which will leading a number of missing detection mud pulse in the complex working *** this paper,a novel data processing algorithm is proposed for measurement while drilling *** method designed in this paper identifies the start time of data frame by using correlation coefficient and synchronization head model,then,based on the principle that the amplitude of mud pulse signal is larger than that of noise pulse signal,the maximum pulse signal in the length of a group of mud pulse series is extracted as mud pulse *** simulation is executed using the data from the actual drilling process and the results show the effectiveness of the proposed method.

关键词： Measurement while drilling Pulse recognition Correlation coefficient Sliding time window

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Stabilization of discrete time stochastic linear systems with time delay via event-triggered control

Stabilization of discrete time stochastic linear systems wit...

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Chinese automation Congress (CAC)

作者： Jian Dong Xiaofeng Zong School of Automation( China University of Geosciences) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan People’s Republic of China

ISBN: (纸本)9781665426480

This paper presents the stabilization problem of a class of discrete-time stochastic linear systems with time delay using an event-triggered control(ETC) strategy. For the general linear systems, we propose a new event triggering mechanism and deduce sufficient conditions for mean square exponentially stabilizability(MSES) and almost sure exponentially stabilizability(ASES) via ETC. The results show that the stability of the system depends on the system parameters (time delay, noise, event trigger threshold parameter, and unstable characteristic value of the original system). When the feedback control is disturbed by low-intensity noise, the unstable system can be stable in the mean square. Finally, the validity of the theoretical results is illustrated through a simulation example.

关键词： Linear systems Sufficient conditions automation Delay effects Stability analysis Feedback control

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Motion Transfer based on Front and Back View for Rehabilitative Exercise

Motion Transfer based on Front and Back View for Rehabilitat...

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第40届中国控制会议

作者： Yuting Ge Jinhua She Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Engineering Tokyo University of Technology

As a part of rehabilitation training,rehabilitation gymnastics plays an important role in restoring the patients’ bodily *** facilitate the evaluation of rehabilitation exercises,in this work we propose multi-view on a motion transfer framework which is based on GANs and parameterized human *** we can generate virtual exercise images sampled from the front and back view of one person with the appearance *** transfer methods based on 2D information can hardly deal with the ambiguity of human pose and lack of appearance ***,this work uses a 3D parametric human model to represent the human body and poses,sampling,and combining pixels from pictures token from both front and back view of a source character,to generate target images under given *** whole framework we used can be divided into three stages.1) In the first stage the 3D human mesh will be reconstructed.2) In the second stage,transformation maps of multi-view will be calculated,which can show the coordinates of required pixels for sampling and placing.3) In the third stage,a generated adversarial network is employed to optimize the coarse result from the second stage and make it more *** our experiments,the whole framework achieves good results,especially it is proved that sampling features from both the front and back view can preserve more details of appearance by comparison experiment.

关键词： Motion transfer Rehabilitation exercises Deep learning Generative Adversarial Network

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Transient Electromagnetic Transmitter Circuit with Speedy Shutoff Based on Passive Constant Voltage Clamping

Transient Electromagnetic Transmitter Circuit with Speedy Sh...

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第40届中国控制会议

作者： Guangjun Wang Chengxiang Ding Lan Cui Zhaohui Wang Hui Jiang Xinzhao Qi Deshun Min School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System School of Law Zhongnan University of Economics and Law

The parasitic inductance of transmitting coil in transient electromagnetic transmitter causes some serious problems like the overlong falling edge time, overshoot and oscillation of the emission current. In order to solve these above problems, this paper proposes a design scheme of passive constant voltage clamping circuit of highly speeding shutoff based on TVS（Transient Voltage Suppressor） and switch. In this design scheme, resistance, TVS and switch are configured in parallel and then connected into the main launch bridge circuit. During the earlier stage of the shutoff of the emission current, TVS is utilized to form the high voltage clamping and thus realize the highly speeding shutoff of the emission current. At the later stage of the emission current decline, the energy of the load inductance is set free by the resistance to prevent the overshoot and oscillation of the emission current. In this paper, the operating procedure and principle of the circuit, the influence of the parameters of core devices on the circuit performance, and the effectiveness of this design scheme is verified by simulation and experiment. The results of simulation and experiment show that this circuit is effective to reduce the shutoff time of the emission current, restrain the overshoot and oscillation meanwhile, and hence improve the wave quality of the emission current.

关键词： transient electromagnetic TVS passive constant voltage clamping speedy shutoff

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Smart Infrared Temperature Measurement System Based On Elastic Compute Service Platform

Smart Infrared Temperature Measurement System Based On Elast...

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Chinese automation Congress (CAC)

作者： Yongbo Li Li Yuan School of Automation( China University of Geosciences) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan People’s Republic of China

ISBN: (纸本)9781665426480

In the fight against the novel coronavirus, this paper designs a smart infrared temperature measurement system based on the Elastic Compute Service platform. In the perceptual layer part of the Internet of Things(IoT), the system uses infrared temperature sensors to quickly collect the temperature of the forehead or arm of the human body and Uses the MCU to automatically transmits the measured data to the Elastic Compute Service through the WiFi module. In the network layer part of the Internet of Things,the data written into the database of the Elastic Compute Service through the program deployed on the Elastic Compute Service. In the application layer of the Internet of Things, the remote management terminal monitors the collected body temperature data in real-time and provides quick warnings. Finally, the User can log in to the applet on his mobile phone to quickly and easily obtain personal information.

关键词： Temperature measurement Temperature sensors Temperature distribution Forehead Databases Real-time systems Mobile handsets

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Hybrid Electromagnetic Modelling of Tubular Permanent Magnet Linear Motors Based on Transfer Learning Physics-Informed Neural Networks

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IET Electric Power Applications 2025年第1期19卷

作者： Jiale Guo Tao Wu Xinmei Wang Xiongbo Wan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Due to the inherent nonlinearity and saturation in the magnetic circuits of tubular permanent magnet linear motors, the analytical method (AM), while computationally efficient, often fails to capture complex electromagnetic behaviours accurately. In contrast, the finite element analysis (FEA) offers high precision but is time consuming. The nonlinearity of magnetic materials introduces strong input–output coupling, while saturation leads to localised deviations in field distributions, both of which reduce the effectiveness and generalisability of conventional modelling approaches. To overcome these challenges, a physics-informed, data-driven modelling approach is proposed. Initially, a novel hybrid modelling framework based on physics-informed neural networks (PINNs) is introduced. In this framework, AM is incorporated into both the input-output layers and the relevant variables, thereby enabling the direct embedding of physical constraints into the loss function. Consequently, the network's training process is rigorously guided in accordance with established physical principles. To further enhance prediction accuracy and generalisation, a transfer learning framework is integrated into PINN, utilising pre-trained datasets from AM and fine-tuning the model using high-accuracy datasets derived from FEA. Additionally, to optimise the physical information-related hyperparameters that impact model accuracy, functional analysis of variance is employed to quantitatively assess their importance and determine the optimal hyperparameter values. Experimental results show that, with training sample sizes representing only 5% of the FEA data, TL-PINN achieves significant improvements over DNN, including a 74.25% reduction in (1 − R 2 ), a 49.51% reduction in RMSE, and a 50.46% reduction in MAE. These findings demonstrate that TL-PINN delivers superior accuracy while utilising substantially fewer FEA datasets.

关键词： design learning (artificial intelligence) neural nets optimisation permanent magnet motors

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A Collision-Free Person-Following Approach Based on Path Planning 17

A Collision-Free Person-Following Approach Based on Path Pla...

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17th IEEE International Conference on Mechatronics and automation, ICMA 2020

作者： Pang, Lei Cao, Zhiqiang Yu, Junzhi Zhang, Weimin Chen, Xuechao Institute of Automation State Key Laboratory of Management and Control for Complex Systems China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems China

ISBN: (纸本)9781728164151

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach. © 2020 IEEE.

关键词： Mobile robots

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A Novel Coiled Cable-Conduit-Driven Hyper-Redundant Manipulator for Remote Operating in Narrow Spaces

A Novel Coiled Cable-Conduit-Driven Hyper-Redundant Manipula...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Mingrui Luo Yunong Tian En Li Minghao Chen Cunfeng Kang Guodong Yang Min Tan School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Laboratory of Industrial Vision and Intelligent Equipment Technology Chinese Academy of Sciences Beijing China Beijing-University of Technology Beijing China

Operating in narrow spaces is an important challenge in the development of robots. Redundant manipulators are one way to solve this problem, but their mechanism design and control method still have much room for improvement. In this paper, we propose a coiled cable-conduit-driven hyper-redundant manipulator (C-CDHRM) with great slenderness and flexibility. In terms of mechanism design, it considers both compactness and operability. By imitating the structure and behavior of a constricting snake, it can be uncoiled sequentially from a coiled storage state, led by the head. In terms of control methods, we propose a multi-layer control system that can make remote operations more accurate and reliable. On the one hand, guiding, segmenting, and following the path overcome the planning ambiguity caused by redundancy. On the other hand, conduit transmission modeling and cable length correction overcome the nonlinear mapping of cable-driven joints and were verified in experiments. Through tests, the mobile integrated system composed of C-CDHRM has an excellent performance in operation precision and accuracy, ensuring safety and accessibility in narrow spaces. Finally, in field experiments, the inspection and cleaning of various types of electrical equipment have been successfully completed, showing excellent application prospects.

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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2022 Chinese automation Congress, CAC 2022

作者： Zhao, Xiaoxuan Wang, Haoyu Wang, Xiujuan Lewlomphaisarl, Udom Li, Dong Hua, Jing Kang, Mengzhen The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

ISBN: (纸本)9781665465335

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration. © 2022 IEEE.

关键词： Greenhouses

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Speech Emotion Recognition Based on Deep Learning Network with Non-local Networks

Speech Emotion Recognition Based on Deep Learning Network wi...

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Chinese automation Congress (CAC)

作者： M.T. Han Z.T. Liu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Convolutional Neural Networks (CNN) based Speech Emotion Recognition (SER) has the problem of learning bias towards stronger emotions while giving less weight to relatively faint emotions. This lack of subtleness causes the misclassification of emotions that have similar arousal such as anger and joy. To solve this problem, a novel speaker-independent SER methods consist of CNN with non-local networks and attention-based Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The non-local networks can capture the dependence between any two points globally to enhance the correlation of emotional features at the utterance level. The extracted trichannel Log-Mel spectrograms features include the raw of LogMel spectrums, the Log-Mel spectrums’ deltas and delta-deltas respectively. Finally long-term contextual dependencies, local information and non-local information which is obtained by nonlocal networks is contained in the extracted feature. It was tested on the Interactive Emotional Dyadic Motion Capture Database (IEMOCAP) dataset. The experimental results show that the WAR of SER is 66.85% and the UAR of SER is 61.01%. Compared with several newly proposed SER methods, the effectiveness of the proposed model is demonstrated.

关键词： Deep learning Emotion recognition Databases Speech recognition Feature extraction Data models Convolutional neural networks

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