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Chinese Automation Congress (CAC)

作者： Shangbin Jiao Weichao Huang Bo Geng Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi ’an University of Technology Xi’an China Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’ hai China Xi'an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

The safe and stable operation of the generator set is related to the national economy and people's livelihood. As an important part of generator excitation system, carbon brush and slip ring temperature monitoring can effectively evaluate the state of the generator, which plays a vital role. Most of the current researches use infrared images to monitor carbon brush temperature, but there are often problems such as high noise and unclear focus. According to the requirements of infrared image display, this paper combines the method of bistable stochastic resonance to denoise the image, and on this basis, introduces the adaptive stochastic resonance array method to denoise the gray image. Experimental results show that compared with common image denoising methods and classic bistable stochastic resonance, the adaptive stochastic resonance array method has improved both visual effects and peak signal- to-noise ratio (PSNR), which further proves the good application of stochastic resonance in weak signal detection and extraction.

关键词： Temperature sensors Temperature measurement Brushes Stochastic resonance Adaptive arrays Generators Carbon

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Refined recognition and intelligent smart interference of radar signal

Refined recognition and intelligent smart interference of ra...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Yuan, Jianquan Bu, Yunong Yang, Shuyuan Chi, Qingxi Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China Xidian University Xi'an China

ISBN: (纸本)9781728137926

Facing the complex electromagnetic environment on the battlefield, how to effectively identify important radar signals by jammers and how to implement strong and effective electromagnetic interference to the enemy's huge radar detection system under the condition of limited jamming resources will be the difficult problems that need to be solved continuously in the electronic warfare field. The solution of these problems will be the premise of effective interference for wartime jammers. In this paper, the technical gap of traditional jammers is analyzed. For radar signal perception, a refined perception and recognition method is proposed, forming the algorithm idea of multi-scale feature extraction and recognition based on radar fingerprint and multi-channel integrated multi-label recognition and classification based on radar radiation source. For interference implementation, the idea of smart interference based on deep GAN is put forward, laying the ground for further research on cognitive electronic warfare. © 2019 IEEE.

关键词： III-V semiconductors

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Distributed Solving Sylvester equations with an explicit exponential Convergence

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IFAC-PapersOnLine 2020年第2期53卷 3260-3265页

作者： Songsong Cheng Xianlin Zeng Yiguang Hong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology 100081 Beijing China

This paper addresses distributed achieving the least squares solution of Sylvester equations in the form of AX + XB = C. By decomposing the parameter matrices A, B and C, we formulate the problem of distributed solving Sylvester equations as a distributed optimization model and propose a continuous-time algorithm from the primal-dual viewpoint. Then, by constructing a Lyapunov function, we prove that the proposed algorithm can achieve a least squares solution of Sylvester equations with an explicit exponential convergence rate. Additionally, we illustrate the convergence performance by using a numerical example.

关键词： Sylvester equations Distributed optimization Least squares solution exponential convergence

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Twist-induced suppression of solid-state quenching in 9,9'-bianthracene derivatives toward non-doped deep blue OLEDs

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Chemical Engineering Journal 2025年 519卷

作者： Chunyang Chen Kunping Guo Longyu Ren Tao Xue Fanghui Zhang Zhanfeng Li Shaanxi Engineering Research Center of Flat Panel Display Technology School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology 710021 Xi'an China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan 030024 China

Intermolecular interactions among organic chromophores crucially determine the optoelectronic properties of planar emitters. Achieving efficient and photostable solid-state emission from π-conjugated blue emitters has long been hindered by the strong π − π stacking inherent in conventional 9,9′-bianthracene (BAn) derivatives. Herein, a novel family of vertically twisted BAn emitters is designed by modulating rotational terminal substituents, aiming to mitigating the aggregation-caused quenching. The conjugation-extended diphenyl-9,9′-bianthracene (PhBAn) derivatives exhibit a slight spectral shift and significantly improved color purity upon transitioning to the neat film (e.g., the emission bandwidth of the 4-fluorophenyl-substituted derivative narrows from 88 to 58 nm). Moreover, these compounds exhibit dual-state emission characteristics, as evidenced by UV–Vis absorption and photoluminescence spectroscopy, together with morphological studies. As a consequence, the organic light-emitting diode (OLED) doped with 20 wt% 10,10′-bis(4-fluorophenyl)-modified PhBAn exhibits electroluminescence peaking at 456 nm with an external quantum efficiency (EQE) of 4.9%. In contrast, the non-doped device shows a slight bathochromic shift to 460 nm and maintains a high EQE of 4.4% while retaining 4.3% EQE across range of 185 to 1146 cd m −2 , suggesting excellent stability. The results highlight twisted PhBAn derivatives as stable blue fluorescent emitters for nondoped OLEDs and demonstrating their promise in advancing deep-blue OLEDs.

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Improved deep deterministic policy gradient algorithm based on prioritized sampling

Improved deep deterministic policy gradient algorithm based ...

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Chinese intelligent systems Conference, CISC 2018

作者： Zhang, HaoYu Xiong, Kai Bai, Jie Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100190 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9789811322877

Deep reinforcement learning tends to have low sampling efficiency, and prioritized sampling algorithm can improve the sampling efficiency to a certain extent. The prioritized sampling algorithm can be used in deep deterministic policy gradient algorithm, and a small sample sorting method is proposed to solve the problem of high complexity of the common prioritized sampling algorithm. Simulation experiments prove that the improved deep deterministic policy gradient algorithm improves the sampling efficiency and the training performance is better. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Reinforcement learning

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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Chinese Automation Congress (CAC)

作者： Qing Zhang Tingyang Jiao Yinxing Ma Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China The Faculty of Computing Science The University of Alberta Edmonton Alberta Canada Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’hai China Xi’an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system.

关键词： Analytical models Temperature distribution Brushes Neural networks Predictive models Market research Generators

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Improving Data Utility Through Game Theory in Personalized Differential Privacy

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Journal of Computer science & technology 2019年第2期34卷 272-286页

作者： Lei Cui Youyang Qu Mohammad Reza Nosouhi Shui Yu Jian-Wei Niu Gang Xie College of Information and Computer Taiyuan University of Technology Taiyuan 030024 China School of Information Technology Deakin University Melbourne VIC 3125 Australia School of Software University of Technology Sydney Sydney NSW 2007 Australia School of Computer Science and Engineering Beihang University Beijing 100191 China Shanxi Key Laboratory of Advanced Control and Intelligent Information System Taiyuan University of Science and Technology Taiyuan 030024 China

Due to dramatically increasing information published in social networks,privacy issues have given rise to public *** the presence of differential privacy provides privacy protection with theoretical foundations,the trade-off between privacy and data utility still demands further ***,most existing studies do not consider the quantitative impact of the adversary when measuring data *** this paper,we firstly propose a personalized differential privacy method based on social ***,we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each *** formalize all the payoff functions in the differential privacy sense,which is followed by the establishment of a static Bayesian *** trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning *** proposed method achieves fast convergence by reducing the cardinality from n to *** addition,the in-place trade-off can maximize the user's data utility if the action sets of the user and the adversary are public while the strategy sets are *** extensive experiments on the real-world dataset prove the proposed model is effective and feasible.

关键词： personalized privacy protection game theory trade-off reinforcement learning

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Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Zheng, Xu Fu, Chong Xie, Haoyu Chen, Jialei Wang, Xingwei Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Computer Science The University of Auckland New Zealand

Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we proposed a novel uncertainty-aware scheme to make models learn regions purposefully. Specifically, we employ Monte Carlo Sampling as an estimation method to attain an uncertainty map, which can serve as a weight for losses to force the models to focus on the valuable region according to the characteristics of supervised learning and unsupervised learning. Simultaneously, in the backward process, we joint unsupervised and supervised losses to accelerate the convergence of the network via enhancing the gradient flow between different tasks. Quantitatively, we conduct extensive experiments on three challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Correction to: Visual Servoing of Unknown Objects for Family Service Robots

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Journal of intelligent & Robotic systems 2022年第4期106卷 1-1页

作者： Xin, Jing Dong, Caixia Zhang, Youmin Yao, Yumeng Gong, Ailing Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada

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Volume Measuring Method for Forming Additive Manufacturing Based on SGBM

Volume Measuring Method for Forming Additive Manufacturing B...

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Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi Keying Wang Xiaolin Fan Key Lab of Manufacturing Equipment of Shaanxi Province 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

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

For volume measurement problems in the processes of additive manufacturing, a volume measurement method is proposed through using the SGBM algorithm in this paper. In this method, the internal and external parameters of the camera are calibrated by the binocular vision system. And the image processes by SGBM are performed for the image acquired by binocular stereo vision system. The processes are including filtering, correction and stereo matching to obtain the disparity information of the object to be tested in the left and right cameras. The volume of the work-piece to be tested can be obtained by the calculation that measured the three-dimensional coordinate information through the disparity information. The experimental results illustrate that the method is of certain reliability and accuracy for volume measurement.

关键词： Cameras Calibration Volume measurement Three-dimensional printing Mathematical model Measurement by laser beam Three-dimensional displays

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