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2023 China automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education 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 Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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TSN-compatible Industrial Wired/Wireless Multi-protocol Conversion Mechanism and Module 48

TSN-compatible Industrial Wired/Wireless Multi-protocol Conv...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yingxiu Xu, Qimin Zhang, Jinglong Xu, Lei Li, Lingzhi Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Therefore, it is hard to obtain a multiple protocol conversion solution with the flexible configuration. Moreover, the previous works do not adapt to the Time Sensitive Networking (TSN), which is considered as the potential technology of IIoT. To tackle the above problems, we proposed an architecture of the multiple conversion system supporting the conversion of multiple protocols. Then, we proposed a protocol conversion mechanism to improve the flexibility of multiple protocol conversion by separating into data and configuration planes. Moreover, we propose a TSN-compatible frame with dynamic priority mapping algorithm, which adjusts the priority in the VLAN tag by the processing delay. Finally, we develop a hardware module and system supporting multiple protocol conversion, which verify that the proposed architecture could satisfy the flexible conversion requirement through the experiments. © 2022 IEEE.

关键词： Ethernet

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Research on the Security Design of Industrial control Mobile Terminal based on Trusted Software Base 2

Research on the Security Design of Industrial Control Mobile...

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Zhang, Mingmei Gan, Tonglin Meng, Yongping Xiong, Wei Liu, Zhe Automation Department of Control Center State Grid Chongqing Electric Power Company 21 Zhongshan 3rd Road Chongqing400015 China School of Cyber Science and Engineering Wuhan University 211 Bayi Road Wuhan430072 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

With the increasing popularity of the Internet of Things, mobile terminal devices are widely used for industrial control field. However, mobile terminal devices still have security problems. As the most common open-source mobile terminal operating system, Android is under the threat of being attacked by malicious programs frequently. In view of this situation, this paper aims to improve the security of industrial control mobile terminals, covering the whole process of trust establishment during the startup of terminal devices, and proposes a security scheme of Android system based on TF card (Trans-Flash Card). The experiment shows that the designed security scheme can effectively detect the third-party malicious application package and protect the data from the malicious attackers. © 2023 SPIE.

关键词： Android (operating system)

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Research of the Spectrum of a Sequence of Complex Discrete Samples with Sample Rate Variability 10

Research of the Spectrum of a Sequence of Complex Discrete S...

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10th Mediterranean Conference on Embedded Computing, MECO 2021

作者： Bulzakov, Yu. Vitiazeva, T. Mikheev, A. Ryazan State Radio Engineering University RSREU The Department of Automation Informatics and Control System Ryazan Russia

ISBN: (纸本)9780738133614

The issues of estimating the influence of the sampling rate variability on the spectral distribution of complex discrete samples of the isoline drift signal formed on the TP segment of the electrocardiosignal has been examined. Mathematical expressions that relate the spectral components in the zero spectral zone caused by the presence of sampling rate variability and non-zero samples of the isoline drift signal, which allow us to estimate the ratio of these components, have been obtained. © 2021 IEEE.

关键词： Estimation

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Distributed solver for linear matrix inequalities: an optimization perspective

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control Theory and Technology 2021年第4期19卷 507-515页

作者： Weijian Li Wen Deng Xianlin Zeng Yiguang Hong Department of Automation University of Science and Technology of ChinaHefei 230027AnhuiChina Key Lab of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

In this paper,we develop a distributed solver for a group of strict(non-strict)linear matrix inequalities over a multi-agent network,where each agent only knows one inequality,and all agents co-operate to reach a consensus solution in the intersection of all the feasible *** formulation is transformed into a distributed optimization problem by introducing slack variables and consensus ***,by the primal–dual methods,a distributed algorithm is proposed with the help of projection operators and derivative ***,the convergence of the algorithm is analyzed,followed by illustrative simulations.

关键词： Distributed computation Distributed optimization Linear matrix inequalities Primal-dual method

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System for Determining the Shape of the Wavefront of a Nonsinusoidal Periodic Acoustic Wave

System for Determining the Shape of the Wavefront of a Nonsi...

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International Conference on engineering of Modern Electric Systems (EMES)

作者： Adrian Codoban Laura Coroiu Eugen Gergely József Sárosi Department Control Systems Engineering and Management University of Oradea Oradea Romania Department of Mechatronics and Automation University of Szeged Szeged Hungary

The shape of the wavefront is important for the most realistic reproduction of the acoustic wave. In acoustics, the wave front (or wave surface) is defined as the totality of points in the space of the propagating medium that have the same instantaneous elongation amplitude. In the ideal case, in a homogeneous and isotropic propagation medium without other disturbances, the wave has a spherical symmetry, i.e. the shape of the wavefront is circular and at large distances from the wave source, due to dispersion, the shape of the wavefront can be considered plan. In practice, however, due to the multiple component harmonics, reflections and possible summations of the primary acoustic wave, the elongation has a non-sinusoidal shape, and the wavefront may have an irregular random shape. Moreover, if the source of the wave or bodies from which it is reflected are in motion, the shape of the wavefront may even be variable in time. The paper presents a method for determining the shape of the wave front of a non-sinusoidal periodic plane acoustic wave, resulting from the addition of several harmonic components, reflected waves, etc. It uses an array of omnidirectional capacitive microphones arranged in line, at equal distances between them. At each of the microphones, the acoustic wave is captured out of phase depending on the position of the microphone in space and the shape of the wave front. These phase shifts placed next to each other give an image of the shape of the wavefront captured by the microphone system. The method presented in this paper reproduces sounds very realistically and can be used in Active Noise Cancel systems, in open spaces, with one or more mobile noise sources. One of the benefits is the increase in hearing comfort in noisy environments. Another is to record sounds with high fidelity.

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control

Adversarially Robust Reinforcement Learning for Urban Traffi...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Shafly Hamzah Endra Joelianto Herman Yoseph Sutarto Adiyana Putri Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi Bandung Indonesia Doctoral Program of Engineering Physics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems.

关键词： Training Adaptive systems Uncertainty Scalability Urban areas Traffic control Robustness Standards Traffic congestion Testing

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Deep Learning for Improved Aircraft Identification in Shared Airspace

SSRN

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SSRN 2024年

作者： Ahmed, Aisha Sir Elkhatem Engin, Seref Naci Malik, Zainab Abdalgadir, Shahd Aeronautical Engineering Department Sudan University of Science and Technology Khartoum407 Sudan Computer Engineering Department Yildiz Technical University Istanbul34220 Turkey Department of Electrical and Electronics Engineering Sakarya University of Applied Science Sakarya54050 Turkey Control and Automation Engineering Department Yildiz Technical University Istanbul34220 Turkey

Ensuring accurate identification of approaching aircraft is a crucial task for Air Traffic control (ATC) systems. This is essential to prevent misidentification accidents, occurring especially in shared airspace. Misidentification incidents may occur due to "Mistaken Identity" and a system malfunction that results in the aircraft crew being unable to respond to a radar control sector call, as well as radio signal problems due to bad weather. While military Identification Friend or Foe (IFF), and Automatic dependent surveillance-broadcast (ADS-B) systems have traditionally served this purpose, their reliability diminishes in cases where stealthy aircraft, during extreme weather conditions and spacious oceanic regions, are involved. In addition, radio signal- based surveillance systems are reliance on aircraft transponders. Under certain circumstances, this may pose a serious problem for the ATC. This paper introduces a novel approach utilizing deep Convolutional Neural Networks (CNN) for the identification of a type of approaching aircraft as either military or civilian, specifically applied to remote-sensing satellite imagery. The objective is to robustly and accurately determine the type of aircraft whether hostile or friendly to enhance radio signal-based surveillance systems detectability. Experimental evaluation incorporates three distinct datasets: noise-free datasets, noisy datasets, and images affected by varying illumination conditions. The experimental results reveal a remarkably high identification accuracy, averaging 99.50%, across both noise-free and augmented datasets. The introduction of noisy datasets is accomplished through the application of a median filter. However, the results also point out an intrinsic challenge posed by illumination variations that affect CNN classification accuracy. © 2024, The Authors. All rights reserved.

关键词： Air traffic control

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