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第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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SeqEnhance: A Lightweight Image Processing PipeLine for Low-Light Image Enhancement 7

SeqEnhance: A Lightweight Image Processing PipeLine for Low-...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhou, Runlin Huang, Peiliang Zhang, Dingwen Ren, Jun Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Northwestern Polytechnical University School of Automation Xi'an China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350359145

Low-light image enhancement (LLIE) is an important task in computer vision, aiming to improve the visual perception or interpretability of images captured in poorly illuminated environments. Recently, deep learning based methods have been extensively explored to address this issue. While many of these methods have achieved significant advancements in various evaluation metrics for LLIE, only a few have made progress in improving inference speed for the resulting images. As a result, achieving both high-quality enhancements and efficient inference speed remains a challenge. To tackle this challenge, we propose SeqEnhance, a fast LLIE method based on a predefined parameterized image processing pipeline. Our approach combines the inference capabilities of deep neural networks for parameter estimation and the efficient processing capabilities of image processing pipelines to generate enhanced images in an end-to-end manner. The experimental results demonstrate that the proposed method achieves competitive performance on image quality evaluation metrics such as PSNR and SSIM with a fast inference speed. © 2023 IEEE.

关键词： Image enhancement

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A Fast Covariance Intersection Tracking Algorithm Based on CDKF

A Fast Covariance Intersection Tracking Algorithm Based on C...

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第三十八届中国控制会议

作者： Hang Zhang Chuang Song Mingrui Hao Linxiu Chen School of Software and Microelectronics Northwestern Polytechnical University Science and technology on complex system control and intelligent agent cooperation laboratory

With the enhancement of the anti-jamming ability of the target, it is difficult to track and strike the target stably and accurately by single guidance. It is necessary to use a variety of detectors as sensors to provide a variety of observation data to track the target stably and achieve accurate strike. In this paper, we mainly research the distributed multi-sensor estimation problem. First, the target motion model and observation model are established. Secondly, the central differential Kalman filter(CDKF) transformation is applied to address the non-linear filtering problem without the need for computation of Jacobian matrix. Then, a FCI fusion algorithm for the multi-sensor target tracking problem is proposed. Compared to the traditional covariance intersection fusion method, FCI is more efficient in computation without the need for complex optimization process. Finally, simulations are designed and implemented and the joint CDKF-FCI fusion estimation algorithm is validated.

关键词： central differential Kalman filter fast covariance intersection fusion information fusion target tracking

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Aeromagnetic Compensation Based on Tikhonov Regularization with Limited L-Curve Parameter-Choice Algorithm 37

Aeromagnetic Compensation Based on Tikhonov Regularization w...

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37th Chinese control Conference, CCC 2018

作者： Mengyin, Fu Jie, Li Tailin, Wu Tong, Liu Meiling, Wang Kai, Wang Jiapeng, Kang Beijing Institute of Technology Beijing100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing100081 China Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9789881563941

Magnetic anomaly detection has great application value to national economic development and national defense construction, while the presence of various interference magnetic fields on the aircraft will affect the detection of magnetic anomaly. Therefore, it is very important to compensate the aircraft interference magnetic field. In order to solve the problem that the accuracy of the parameters is poor by using ridge estimation in the process of solving the parameters of aircraft interference magnetic field model, we propose a method based on Tikhonov Regularization (TR). Firstly, the optimal regularization parameter is obtained by the limited L-curve method in our work. Secondly, the aircraft interference magnetic field model parameters are obtained by using the TR method. Finally, we use the model parameters to calculate the aircraft interference magnetic field in the real flight, and complete the compensation. The effectiveness of this method is validated by simulation experiments. The results show that the method proposed in this paper has the advantages of high precision and convenient application, so it can be used to compensate the aircraft interference magnetic field effectively. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Parameter estimation

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Depth Estimation for Small Obstacles Based on Monocular Vision

Depth Estimation for Small Obstacles Based on Monocular Visi...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Wang Gao Changqing Wang Lei Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.

关键词： intelligent agents Estimation control systems Training Deep learning Computer vision complex systems

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An adaptive fault detection strategy for Cz crystal growth process

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Mu, Lingxia Feng, Nan Zhang, Zhenhua Zhang, Youmin Zhang, Jian Xian University of Technology Crystal Growth Equipment and Systems Integration National Engineering Research Center Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

The monitoring and fault detection for the Czochralski (Cz) crystal growth process is important to ensure the quality of the produced mono-crystalline silicon. The Cz process is a typical multi-modes industrial process, which makes the detection difficult in distinguish the change caused by the fault or mode switching. In this paper, an adaptive fault detection strategy for Cz crystal growth process is proposed. Firstly, quality-related variables are selected from numerous process variables, based on which the training dataset is constructed. Then, a fault detection model is built based on slowly-varying feature and statics indicator. A model switching strategy and updating algorithm are designed to handle the abnormal situation caused by the mode switching or fault occurrence. Three representative experiments are conducted to verify the performance of the proposed strategy, including numerical cases, a publicly available dataset, and Cz crystal growth process from our laboratory. © 1963-2012 IEEE.

关键词： Crystal growth from melt

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Modeling Method of Aircraft Simulation Based on Formal Description of Petri Net 18th

Modeling Method of Aircraft Simulation Based on Formal Des...

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18th Chinese intelligent systems Conference, CISC 2022

作者： Zhan, Tianming Huang, Yu Geng, Huapin Tong, Jiahui TAIJI Computer Co. Ltd Beijing100102 China Peking University Beijing100871 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100190 China Beijing Electro-Mechanical Engineering Institute Beijing102299 China

ISBN: (纸本)9789811962028

The simulation of aircraft guidance and control system has characteristics such as multiple model classification, large parameter influence,complex information interaction and so on. The traditional code-level model development method has many limitations, such as time consuming, low reusability, low modeling efficiency, and model quality can not be guaranteed. Based on the concurrency, graphic representation, mathematics and simulation characteristics of Petri net, this paper studies the aircraft simulation modeling method of formal description based on Petri net, constructs a general modeling development and integration software tool that can carry out component, parametric and graphical modeling, solves the problems of multi granularity model modeling and automatic generation of real-time simulation model code, and realizes the visual graphic modeling and comprehensive integration of different granularity models such as components, modules and meta models. The research method has been successfully applied in the simulation modeling of an aircraft. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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Estimate depth information from monocular infrared images based on deep learning 7

Estimate depth information from monocular infrared images ba...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Li, Xu Ding, Meng Wei, Donghui Wu, Xiaozhou Cao, Yunfeng Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing China Beijing Electro-.Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China

ISBN: (纸本)9781728170862

In the field of automatic driving, vision-based driving assistance system(V-DAS) is a research direction. Aiming at the demand of V-DAS for low visibility environment perception, we propose a depth estimation method based on monocular infrared images. Our method uses an end-to-end self-supervised deep learning framework, and uses the reprojection relationship between monocular video frames to construct the loss function, which makes it easy to collect dataset. In order to mitigate the influence of infrared noise, we adopt the minimum reprojection loss method. To ensure the neural network can learn infrared features better, we use transfer learning from RGB image dataset first. Qualitative experiments on FLIR datasets show that the proposed method can obtain pixel-lev el dense depth from monocular infrared images. Experiments on real roads show that the proposed method can effectively perceive the depth information of the target in the night, and the absolute error is 13.2% within 15m. This can meet the requirements of collision avoidance in most emergency situations during automatic driving. © 2020 IEEE.

关键词： Deep learning

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Channel Estimation Based on Lorentzian Adaptive Algorithm in α-stable Noise Environment

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Aleksey Cherednichenko Xinqi Huang Yanyan Wang Yingsong Li Harbin Engineering University Harbin China Intelligent Agent Cooperation Laboratory Science and Technology on Complex System Control Beijing China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198064

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel inα-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons.

关键词： Computer simulation Adaptive algorithms Channel estimation Adaptive arrays

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Motion Parallax Estimation for Ultra Low Altitude Obstacle Avoidance

Motion Parallax Estimation for Ultra Low Altitude Obstacle A...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Changqing Wang Xinkai Liang Shan Zhang Chao Shen Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137933

By making full use of the curvature of the earth and ground clutter, the ultra-low altitude flight technique can significantly increase the difficulty of detecting and intercepting unmanned aerial vehicles (UAVs), and improve their penetration probabilities. However, unpredicted static/dynamic obstacles seriously threaten the flight safety of the ultra-low altitude UAVs. Therefore, fine autonomous spatial perception capability is essential for ultra-low altitude UAVs with limited loads and computing resources to avoid obstacles. To solve the difficult problem of spatial perception for ultra-low altitude obstacle avoidance, a motion parallax estimation algorithm is proposed by combing the deep learning and the epipolar geometry in this paper. By utilizing the deep neural network, the end-to-end mapping relationship from sequence images to depth values of pixels is constructed for spatial perception. At the same time, to overcome the interference caused by the image consistency hypothesis, the loss function is constructed based on the 3D geometric information of the scene rather than the re-projection error between images. Finally, the proposed algorithm is evaluated on the KITTI dataset and infrared image dataset to verify its effectiveness and generalization.

关键词： Motion parallax Deep learning Epipolar geometry component Motion Parallax Space Perception flight safety Unmanned Aerial Vehicle Obstacle avoidance Collision avoidance

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