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arXiv 2022年

作者： Yin, Hui-Ping Ren, Hai-Peng Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'An710048 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

A new feature of the chaotic signal generated by chaotic shape-forming filter (CSF) is uncovered in this work. We find that, the autocorrelation function (ACF) of the transmitting signal generated by CSF keeps the same as that of the base function of CSF, no matter what information is encoded. We derive the analytical equation to describe the relation between the ACF of the received signal and the wireless channel parameters using the ACF of the transmitted signal as prior knowledge revealed by the finding in this work. This new property can be utilized together with different wireless communication systems to improve the system performance. Specially, to demonstrate the improvement, channel state information (CSI) is identified using the chaotic baseband wireless communication as a paradigm. Two significant benefits by using the new property are 1) the CSI can be identified without the probe information known to the receiver as done in the conventional wireless communication systems, which improves the bandwidth efficiency, especially in the time-varying channel;2) the correlation operation is insensitive to the channel noise, which improves the identification accuracy as compared to the commonly used methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

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Artificial intelligence enhances the performance of chaos-based wireless communication

arXiv

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arXiv 2019年

作者： Ren, Hai-Peng Zhao, Hong-Er Bai, Chao Yin, Hui-Ping Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

Some new findings for chaos-based wireless communication systems have been identified recently. First, chaos has proven to be the optimal communication waveform because chaotic signals can achieve the maximum signal to noise ratio at receiver with the simplest matched filter. Second, the information transmitted in chaotic signals is not modified by the multipath wireless channel. Third, chaos properties can be used to relief inter-symbol interference (ISI) caused by multipath propagation. Although recent work has reported the method of obtaining the optimal threshold to eliminate the ISI in chaos-based wireless communication, its practical implementation is still a challenge. By knowing the channel parameters and all symbols, especially the future symbol to be transmitted in advance, it is almost an impossible task in the practical communication systems. Owning to Artificial intelligence (AI) recent developments, Convolutional Neural Network (CNN) with deep learning structure is being proposed to predict future symbols based on the received signal, so as to further reduce ISI and obtain better bit error rate (BER) performance as compared to that used the existing sub-optimal threshold. The feature of the method involves predicting the future symbol and obtaining a better threshold suitable for time variant channel. Numerical simulation and experimental results validate our theory and the superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal to noise ratio

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A high-precision vision-based mobile robot slope detection method in unknown environment

A high-precision vision-based mobile robot slope detection m...

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Chinese Automation Congress

作者： Kenan Du Jing Xin Yaqian Shi Ding Liu Youmin Zhang CETC 20 Shaanxi key Laboratory of Integrated and Intelligent Navigation Xi'an P.R. China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi‘an University of Technology Xi'an P.R. China Department of Mechanical and Industrial Engineering Concordia University Quebec Canada

Aiming at satisfying the demand of slope perception accuracy for mobile robots in the unknown environment, a high-precision vision-based mobile robot slope detection method in unknown environment is proposed in this paper. Firstly, the RGB images of slopesin the unknown environment arecaptured by the Kinect sensor installed on the mobile robot, and edge sharpening is performed on the images of slopes. Then, the coordinates of the slope edge pixel in the image coordinate system are transformed into the camera coordinate system to obtain the real length of the slope. Finally, the single image depth estimation method based on convolution neural network (CNN) is used to estimate the depth of theslope image toobtain the projection length of the slope only using a single slope image, and the slope angle is obtained by the inverse cosine function. The experimental results show that the slope detection method proposed in this paper has higher detection accuracy and can satisfy the demand of slope sensing accuracy of mobile robot in unknown environment.

关键词： Slope detection Coordinate transformation Single image Depth estimation Convolutional neural network (CNN)

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Early Forest Fire Segmentation Based on Deep Learning

Early Forest Fire Segmentation Based on Deep Learning

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Mengna Li Youmin Zhang Jing Xin Lingxia Mu Ziquan Yu Han Liu Guo Xie Shangbin Jiao Yingmin Yi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an Shaanxi China Concordia University Montreal Quebec Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China

ISBN: (纸本)9781665401166

Fire segmentation is very important for fire rescue. It can make firefighters get the information on fire area, spread direction and so on, and then help them make quick and effective fire-fighting plan. Therefore, this paper proposes an early forest fire segmentation algorithm based on a deep learning model, named F-Unet, which mainly uses the architecture idea of Unet for reference. F-Unet consists of contraction path, feature fusion layer and expansion path. The contraction path is composed of the first 13 layers of VGG16, which is used to obtain feature maps with different scales. In order to improve the segmentation accuracy of the model, the feature fusion network proposed in this paper is added to the Unet architecture to fuse these feature maps with different scales. The expansion path is used for upsampling these feature maps to restore the size of the original input image and obtain the fire segmentation results. The experimental testing results on the FLAME dataset show that F-Unet can significantly improve the fire segmentation precision, and also prove that the proposed feature fusion network is effective to improve the performance of fire segmentation.

关键词： Deep learning Image segmentation Fuses Fault detection Fires Forestry Safety

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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IEEE International Conference on Unmanned systems (ICUS)

作者： Pengyu Yue Jing Xin Zhijie Mao Yongchang Lu Youmin Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an P.R.China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal QC Canada

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron.

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Parameter estimation of time fractional heat conduction equation for monocrystalline silicon

Parameter estimation of time fractional heat conduction equa...

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Chinese Automation Congress (CAC)

作者： Shuyan Shi Ding Liu Zhongdan Zhao Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shananxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665426480

In this paper, a time-fractional heat conduction model is established to describe the heat transfer process of monocrystalline silicon in the Czochralski method. The numerical solution of the fractional-order model is obtained by using implicit difference scheme. In addition, an improved grey wolf algorithm is proposed to estimate the parameters of the fractional heat conduction model using the experimental temperature data. The numerical experiment results show that the time-fractional heat conduction model has a higher explanatory ability, compared with the classical heat conduction model. The Improved Grey Wolf algorithm proposed in this paper is effective in solving the parameter estimation problem of the time-fractional heat conduction equation.

关键词： Heating systems Temperature distribution Parameter estimation Inverse problems Simulation Mathematical models Silicon

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Fault Diagnosis for Small Samples Based on VMR

Fault Diagnosis for Small Samples Based on VMR

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Chinese Automation Congress (CAC)

作者： Xuejin Wu Yanghu Hu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis.

关键词： Fault diagnosis Training Neural networks Rolling bearings Feature extraction Data models Signal reconstruction Convolutional neural networks Long short term memory Overfitting

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Tracking Performance of Networked systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhang, Bin Jiang, Xiaowei Sun, Li Zhang, Xianhe School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan430074 China School of Electrical Engineering and Automation Hubei Normal University Huangshi435002 China

ISBN: (纸本)9781665465335

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions. © 2022 IEEE.

关键词： Networked control systems

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Research on control Optimization Method of Mine Ventilation system Based on intelligent Optimization Algorithm

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Journal of Computers (Taiwan) 2023年第1期34卷 239-248页

作者： Lian, Wen-Li Qi, Jing-Jing Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Hebei Province Tangshan City063600 China Key Laboratory of Intelligent Motion Control System of Tangshan City Tangshan University Hebei Province Tangshan City063600 China Tangshan University Hebei Province Tangshan City063600 China

In view of the complex ventilation network in the mine, many influencing factors, and the complex establishment of the air demand model, this paper establishes an air volume optimization model with the minimum energy consumption of the entire ventilation network as the optimization objective, and adds the air volume balance law conditions, the air pressure balance law conditions, the branch air demand law conditions, the air resistance regulation law conditions, and the fan conditions as constraints to the model. Then, the intelligent algorithm is used to solve the problem. In order to make the intelligent algorithm converge faster and solve the problem optimally, the Grey Wolf algorithm is improved. Finally, the model and the improved intelligent algorithm are used to optimize the ventilation regulation of the actual operating coal mine, and the air volume control scheme is given. © 2023 Authors. All rights reserved.

关键词： Coal mines

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Anti-occlusion Object Tracking Algorithm using Response Template Fusion and Combined Features

Anti-occlusion Object Tracking Algorithm using Response Temp...

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第35届中国控制与决策会议

作者： Jing Wang Yuan Wei Yisong Liu Weichao Huang Ming Hu Faculty of Printing Packaging Engineerring And Digital Media Technology Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

The kernel correlation filtering algorithm cannot judge the occlusion by itself when the object is occluded byforeign objects,and the tracking performance is greatly *** at this problem,this paper proposes an anti-occlusion object tracking algorithm that fuses response templates and combined *** peak sidelobe ratio(PSR) is used to determine whether the object is *** the object is partially occluded,a combined feature representation object model is introduced to enhance robustness,and a multi-response template with anti-occlusion performance is constructed to improve the performance of the object when it is occluded by foreign *** problem of reducing the available information,thereby improving algorithm *** in the OTB-100 data set can prove that the success rate and accuracy of the algorithm have competitive results compared with the comparison algorithms.

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