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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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Adaptive RBF Neural Network control Method for Pneumatic Position Servo system ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8826-8831页

作者： Hai-Peng Ren Shan-Shan Jiao Xuan Wang Jie Li Shaanxi Key Laboratory Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China School of Electrical Engineering Xi’an University of Technology Xi’an China

With the development of control theory and the pneumatic element, the application of pneumatic systems has attracted more attention because of the performance to price ratio improvement. Despite of these, there are still challenge to deal with the nonlinearity of the system, the uncertainty of the parameters, the input saturation and the unknown control direction in the tracking control of pneumatic system. In this paper, the nonlinearity and model uncertainty are treated with adaptive radial basis function neural network (RBFNN), meanwhile, the unknown control direction and input saturation are dealt with the Nussbaum function and Gauss error function, respectively. The stability of the designed controller is proved by Lyapunov theory. Finally, the experimental and comparison results show the effectiveness and superiority of the proposed method.

关键词： Pneumatic system RBFNN unknown model parameters unknown control direction input saturation

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Topology Inference on Partially Observable Mobile Robotic Networks under Formation control

Topology Inference on Partially Observable Mobile Robotic Ne...

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European control Conference (ECC)

作者： Yushan Li Jianping He Lin Cai The Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China University of Victoria BC Canada

Mobile robotic networks (MRNs) have received considerable attention in recent years. Among their wide applications, the interaction topology plays a critical role in achieving efficient cooperation. We demonstrate that the observable interaction process of MRNs under formation control will reveal the interaction topology, presenting increasingly viable threats to the security of MRNs. To practice, we focus on a general situation where the external observer has only partial observation over the global MRN and aims to infer the interaction topology. This is a very challenging problem because the observable state evolution is a coupled consequence of the underlying interaction topology, the unknown leader dynamics, and the unobservable part. To address this issue, we novelly identify the steady pattern of the MRN and filter the impacts of the observable parts by shrinking the inference range. By constructing the informative dataset reflecting the interaction process, the local interaction topology is approximated with high accuracy. Simulations illustrate the feasibility and effectiveness of the proposed method.

关键词： Sufficient conditions Network topology Shape Process control Europe Estimation Observers

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High-Resolution representation object pose estimation from monocular images

High-Resolution representation object pose estimation from m...

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Chinese Automation Congress (CAC)

作者： Qi Guan Wenfa Li Shibei Xue Dewei Li Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665426480

This paper addresses the 6D challenge of monocular 6D pose estimation on small targets and weak texture objects. We propose a monocular object pose estimation algorithm, High Resolution Object Pose Estimation (HRPE) based on the high-resolution feature representation, which detects the 2D keypoints and uses the Perspective-n-Point (PnP) algorithm to compute the 6D pose. HRPE can maintain high-resolution feature representation through parallel convolution of multi-resolution feature branches and repeated cross-scale fusion, generating more precise pose estimation. Experimental results show that HRPE surpasses the existing state-of-the-arts, and achieves a significant accuracy improvement for small objects, which demonstrates the effectiveness of high-resolution feature representation on pose estimation.

关键词： Measurement Automation Convolution Pose estimation Feature extraction

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Spatial-nonlocality-induced non-Markovian electromagnetically induced transparency in a single giant atom

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Physical Review A 2022年第4期106卷 043710-043710页

作者： Y. T. Zhu S. Xue R. B. Wu W. L. Li Z. H. Peng M. Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 People's Republic of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 People's Republic of China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing 100084 People's Republic of China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 People's Republic of China School of Electronics and Information Engineering Soochow University Suzhou 215006 People's Republic of China

The nonlocal waveguide coupling of giant atoms can bring up novel physics and applications. In this paper, we find unconventional electromagnetically induced transparency in giant atoms that has not been reported so far. We present a consistent theory for describing the unconventional EIT which includes a real-space scattering method and a time-delayed master equation. Our analysis shows this phenomenon is a full quantum effect led by a quantized interaction between the atom and the waveguide. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom. This phenomenon results from the inherent nonlocality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time.

关键词： Quantum optics with artificial atoms Scattering theory Quantum master equation

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NccFlow: Unsupervised learning of optical flow with non-occlusion from geometry

arXiv

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arXiv 2021年

作者： Wang, Guangming Ren, Shuaiqi Wang, Hesheng The Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer vision

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Surface Defect Detection Based on Deep Learning and Collaborative Cloud-edge Computation

Surface Defect Detection Based on Deep Learning and Collabor...

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International Conference on Computer and Communications (ICCC)

作者： Shunjie He Bo Yang Dafeng Zhu Cheng Li Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665409513

Surface defect detection is a fundamental step of quality management. Nowadays, surface defect detection system based on machine vision technology has made great progress which can gradually replace the human inspector. With the help of convolutional neural networks, the accuracy and stability of the surface defect detection algorithm are improving rapidly. However, it is difficult to process the images which have small defect areas and complex backgrounds. Furthermore, lacking defective samples and limited computation resources are also tough problems. To deal with these problems, a two-stage algorithm combined with object detection and classification networks is proposed. Transform learning and data augmentation are used to deal with the problem of lacking defect samples. Besides, a detection system based on collaborative cloud-edge computation is designed to improve the performance of the algorithm. The result shows that the proposed method has high performance and low requirements of computation resources.

关键词： Training Collaboration Transforms Object detection Classification algorithms Convolutional neural networks Detection algorithms

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Rapid Response and Recovery Performance of NO2 Gas Sensor Based on Gan Hexagonal Pits at Room Temperature

SSRN

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SSRN 2022年

作者： Han, Xiaomei Han, Dan Liu, Xiaoru Chen, Yi Liu, Lulu Liu, Zhihua Li, Donghui Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China

Although fantastic milestones of Gallium nitride (GaN)-based materials in optoelectronic devices had been reached, the focus on the optimization of their geometrical structure for gas-sensing applications was relatively scarce. In this work, we demonstrated NO2 gas sensor based on p-type GaN hexagonal pits (GaN-HP) with high surface-to-volume ratio by wet etching method. The fabricated NO2 gas sensor had high sensitivity, high selectivity, fast response/recovery times. The response of p-type GaN-HP with etching time for 10 min to 2 ppm NO2 gas was 6.2%, which also have quick response and recovery time at room temperature. UV light drastically enhanced the response of p-type GaN-HP sensor by 1.3 times to 100 ppm NO2 . The gas sensing mechanism of p-type GaN-HP gas sensor was explored, which provided a theoretical and experimental basis for further research and development of high-performance gas sensors based on GaN material. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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Black Phosphorus-Organic Framework-Derived Pei/Peg Nanohybrid Prepared by a One-Pot Method for a Wide Detection Range Co2 Gas Sensor

SSRN

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SSRN 2022年

作者： Liu, Lulu Sang, Shengbo Han, Dan Liu, Zhihua Han, Xiaomei Li, Donghui Chen, Yi Liu, Dongming Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Yang, Kun Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China

In this paper, we report a hybrid PEI/PEG-BP sensor (composite material of black phosphorus-organic framework-derived PEI/PEG nanohybrid) prepared by a one-pot method, in which the existence of PEI/PEG onto BP was confirmed by SEM, EDS, XPS and UPS. The gas sensing properties of the PEI/PEG-BP sensor, including the sensor response, response/recovery times, repeatability, selectivity, and effects of humidity on the sensor, were studied by exposure to several CO 2 gas concentrations ranging from 200 ppm to 300,000 ppm at room temperature (25 ℃). The results show that the PEI/PEG-BP sensor had an extremely wide detection range, with lower and upper detection limits of 200 ppm and 160,000 ppm, respectively. The response of the PEI/PEG-BP sensor to 200 ppm, and 160,000 ppm CO 2 gas at room temperature ( RT ) was 4.48%, and 254.02%, respectively, showing fast response/recovery time, excellent repeatability and high selectivity. The gas-sensing properties of the PEI/PEG-BP sensor are better than BP, PEI, PEG, PEI-BP and PEG-BP sensors. The superior properties of the PEI/PEG-BP sensor can be attributed to the BP framework, porous structure, electron doping, and surface reactive groups such as -NH 2 and -OH. The PEI/PEG-BP sensor, with its excellent gas-sensing performance will help us to further explore the changes of CO 2 concentration in microbial growth systems, natural environments, industrial production and so on. © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

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Modeling and Analysis of Pumping Cell of Nox Sensor - Part Ii: Nox Pumping Cell

SSRN

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SSRN 2022年

作者： Wang, Zhen Deng, Zhong-Hua Wang, Jie Lin, Wei-Xun Fu, Xiao-Wei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China Lambda Company Jiangsu Changzhou213100 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

To improve the performance of the NOx sensor, a combined model based on the electrochemical model and the diffusion model is developed for the NOx pumping cell of the NOx sensor. Considering that a small amount of oxygen enters the third chamber and part of NO is decomposed in the second chamber, the electrochemical model simultaneously considers the reactions of NO and oxygen at cathode, and the diffusion model additionally considers the diffusion of oxygen and the decomposition of NO. To validate the model at different gas concentrations, the sensor is mounted on the designed test rig. Then, the simulation results are compared with experimental data from the test rig and good agreement is obtained. In addition, three key parameters of the model of the NOx pumping cell are analyzed. The values and changing trends of these three parameters are given in the paper. © 2022, The Authors. All rights reserved.

关键词： Diffusion

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