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IEEE Conference on Global Communications (GLOBECOM)

作者： Tiankai Jin Yehan Ma Zhiduo Ji Cailian Chen Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China

Edge sensing supported by wireless transmission is one of the core enabling technologies for flexibly implementing the Industrial Internet of Things (IIoT). Balancing network resource consumption and sensing accuracy under dynamic network conditions is a critical challenge. In this work, we bridge the gap between edge sensing performance and transmission design through observability analysis and learning-based methods. Particularly, utilizing observability probability as the key metric, we design the network resource reservation for specific sensing performance demands including stability based on our derived upper and lower probability bounds. Then, to further reduce the overall cost of edge sensing and transmission, an intelligent transmission scheduling method (ITSM) based on deep reinforcement learning is provided, which dynamically schedules the number of transmissions for each sensor. In ITSM, the action space is determined according to the amount of our reserved resources, and both the states of sensing error and fading channel are taken into account. Finally, the superiority of our proposed methods is fully demonstrated through numerical simulations in a typical IIoT system of industrial hot rolling.

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Triggered Mechanism for Strict Feedback systems Under Unknown control Directions

Triggered Mechanism for Strict Feedback Systems Under Unknow...

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Asian control Conference

作者： Guilong Liu Hongda Wu Yongliang Yang Wei Xie Weidong Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper presents an event-based asymptotic tracking control scheme designed for strict feedback systems with unknown control directions. The control directions directly determine how the controller acts on the system, thus impacting the system's stability and convergence speed. When the control directions are unknown, it presents a significant challenge in controller design. To tackle this issue, the Nussbaum-type gains are introduced. However, this method increases communication overhead. Hence, an event-triggered mechanism is introduced to alleviate this burden. Additionally, to achieve and maintain desired target states, satisfying control requirements and performance demands, this paper improves the event-triggered mechanism to achieve asymptotic tracking. Finally, the feasibility and effectiveness of the proposed design are validated through a simulation example.

关键词： Asymptotic stability Target tracking Stability criteria control systems Optimization Convergence

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Predominant types of regional cold waves in North China and their historical changes

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Atmospheric and Oceanic Science Letters 2024年第4期17卷 46-51页

作者： Xinyi Cheng Yiyi Zhang Jiandong Li Xin Hao Center for Climate System Prediction Research/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of EducationNanjing University of Information Science&TechnologyNanjingChina Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjingChina

寒潮事件对东亚地区的社会经济,生态系统和人体健康影响巨大.根据1980-2019年间10月至次年3月的每日寒潮记录和K-means聚类,本文识别出了中国两种不同类型的区域性寒潮(T1和T2). T1区域性寒潮主要影响东北地区,T2区域性寒潮则主要影响... 详细信息

寒潮事件对东亚地区的社会经济,生态系统和人体健康影响巨大.根据1980-2019年间10月至次年3月的每日寒潮记录和K-means聚类,本文识别出了中国两种不同类型的区域性寒潮(T1和T2). T1区域性寒潮主要影响东北地区,T2区域性寒潮则主要影响华北和东部地区.与T1区域性寒潮相比, T2区域性寒潮强度更强,持续时间更长,影响范围更广. 1980–2019年期间, T1区域性寒潮的频率显著增加,而T2区域性寒潮的频率则没有趋势变化. T1和T2区域性寒潮事件均与西伯利亚高压增强有关,然而与它们相关的对流层中层波列明显不同.在T1区域性寒潮事件发生期间,西伯利亚-蒙古上空出现负-正模态的500-hPa位势高度异常波列,削弱了东亚大槽,导致西伯利亚冷空气东移.T1区域寒潮频次增加趋势可能与全球变暖引起的500-hPa位势高度的线性趋势变化有关.与T2区域寒潮事件相关的波列则在乌拉尔山脉,蒙古和华北地区形成了脊-槽-脊环流异常,导致冷空气向东南方向入侵.本文得出结论,由于两类区域寒潮影响不同,西伯利亚高压增强和对流层中层波列模态在我国区域性寒潮事件的预报中应综合考虑.

关键词：寒潮 K-means聚类西伯利亚高压波列

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Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

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Collaboration Strategies for Two Heterogeneous Pursuers in A Pursuit-Evasion Game Using Deep Reinforcement Learning

Collaboration Strategies for Two Heterogeneous Pursuers in A...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhanping Zhong Zhuoning Dong Xiaoming Duan Jianping He The School of Automation Science and Electrical Engineering Beihang University Beijing China The Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We investigate a pursuit-evasion game taking place in an unbounded three-dimensional space, where a flexible pursuer with hybrid dynamics collaborates with a fast pursuer and aims to capture a flexible evader within a finite time. The key feature of this problem lies in the hybrid dynamics of the flexible pursuer, which can change its dynamics once during the game and switch to a fast pursuer with increased speed but lower maneuverability. To address this challenge, we devise a hybrid strategy based on the soft actor-critic framework, tailored specifically for the flexible pursuer, which encompasses both maneuvering and switch tactics. We introduce a switch factor to the input of the actor network and incorporate switch actions to further expand the action space. These additions enable the flexible pursuer to execute maneuvering actions and determine a moment to switch to a fast pursuer. The reward function is designed to account for related angle, altitude, speed, and sparse reward. Through extensive ablation experiments conducted in a simulated environment, we demonstrate the efficacy of our algorithm in facilitating the learning of hybrid strategies for the flexible pursuer, resulting in significantly improved capture rates compared to alternative methods.

关键词： Solid modeling Three-dimensional displays Heuristic algorithms Collaboration Switches Games Deep reinforcement learning Vectors Intelligent robots

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Few-Reference Image Quality Assessment with Multiple information Measurement Fusion 9th

Few-Reference Image Quality Assessment with Multiple Inform...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Shi, Shuang Wang, Simeng Liu, Yuchen Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

In the era of media information explosion, there is an urgent need for a fast and reliable image quality assessment (IQA) model to improve the actual application effect of images. To this end, we propose the multiple information measurement fusion metric (MMFM), which innovatively combines two types of information measures (IMs), i.e., local IM and global IM, using only a small number of references for IQA. First, inspired by the free energy theory, we combine 2-dimensional autoregressive model with sparse random sampling method as an inference engine on an input image to generate its associated predicted image. Second, by the inspiration of pixel-wise measurement, we obtain the local IM by calculating the information entropy of the residual error between the input image and its corresponding predicted one. Third, motivated by the histogram-based measurement, we acquire the global IM by computing the two kinds of divergences between the input image and its predicted one. Fourth, we systematically fuse three components, independently including one distance of local IM between the reference and corrupted images and two distances of global IMs between the reference and corrupted images, based on a linear function to derive the final IQA result. The results of experiment on the most popular LIVE database show that our designed algorithm with only one number used as few reference has achieved well performance as compared with several mainstream IQA models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Structured Sparse Coding With the Group Log-regularizer for key Frame Extraction

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1818-1830页

作者： Zhenni Li Yujie Li Benying Tan Shuxue Ding Shengli Xie the School of Automation Guangdong University of TechnologyGuangzhou 510006 the Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology(GDUT)Guangzhou 510006China the School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China the National Institute of Advanced Industrial Science and Technology TsukubaIbaraki 305-8560Japan IEEE the Key Laboratory of Intelligent Information Processing and System Integration of Io T(GDUT) Ministry of Education Guangdong Key Laboratory of IoT Information Technology(GDUT) Guangzhou 510006China

key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire ***,how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a *** this paper,we propose a novel model of structured sparse-codingbased key frame extraction,wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction *** automatically extract key frames,a decomposition scheme is designed to separate the sparse coefficient matrix by *** rows enforced by the nonconvex group log-regularizer become zero or nonzero,leading to the learning of the structured sparse coefficient *** solve the nonconvex problems due to the log-regularizer,the difference of convex algorithm(DCA)is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm,which can be directly obtained through the proximal ***,an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed,which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction *** results demonstrate that the proposed algorithm can extract more accurate key frames from most Sum Me videos compared to the stateof-the-art ***,the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection(SMRS)and an 8% increase compared to SC-det on the VSUMM dataset.

关键词： Difference of convex algorithm(DCA) group logregularizer key frame extraction structured sparse coding

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Data-Driven Bayesian Nonparametric Wasserstein Distributionally Robust Optimization

arXiv

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arXiv 2023年

作者： Ning, Chao Ma, Xutao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

In this work, we develop a novel data-driven Bayesian nonparametric Wasserstein distributionally robust optimization (BNWDRO) framework for decision-making under uncertainty. The proposed framework unifies a Bayesian nonparametric method and the Wasserstein metric to decipher the global-local features of uncertainty data and encode these features into a novel data-driven ambiguity set. By establishing the theoretical connection between this data-driven ambiguity set and the conventional Wasserstein ambiguity set, we prove that the proposed framework enjoys the finite sample guarantee and asymptotic consistency. To efficiently solve the resulting distributionally robust optimization problem under the BNWDRO framework, we derive for this optimization problem an equivalent reformulation, which is kept tractable for many practical scenarios. Numerical experiments on a unit commitment problem verify the effectiveness of the proposed BNWDRO framework compared with existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Terahertz Refractive Index Sensor Based on Tunable Graphene Metamaterial Absorber

Terahertz Refractive Index Sensor Based on Tunable Graphene ...

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Sensor Technology and control (ISSTC), International Symposium on

作者： Maixia Fu Tiantian Zhang Zhonghao Liu Mengyi Liu Dongdong Wang Key Laboratory of Grain Information Processing and Control Ministry of Education (Henan University of Technology) Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China

ISBN: (数字)9798331541637

ISBN: (纸本)9798331541644

A terahertz refractive index sensor constructed with graphene metamaterial absorber has been proposed. The sensor features a patterned graphene structure, consisting of an independent circular ring and a special structure formed by intersecting cross and square patterns. Under normal incidence of TM and TE polarized light, the sensor exhibits a low-frequency peak at 1.55 THz with maximum absorption rates of $\mathbf{9 9. 4 5 \%}$ and a high-frequency peak at 3.83 THz with maximum absorption rates of $\mathbf{9 9. 5 0 \%}$. The quality factors (Q factor) of the two peaks are 31 and 65, respectively. Unlike traditional metal sensors, the absorption peaks of this sensor can achieve dynamic tunability through adjusting the graphene’s Fermi level. By varying the analyte’s thickness and refractive index, the maximum sensitivity of the two absorption peaks can reach $495 \mathrm{GHz} / \mathrm{RIU}$ and 895 $\mathrm{GHz} /$ RIU. The corresponding figures of merit (FOM) are 9.9 and 15.2, respectively. The sensor proposed here are applied to detect hemoglobin biomolecule content and has shown excellent sensing performance in our simulations. Therefore, the present work has broad potential for applications in biomedical and medical diagnostic fields.

关键词： Q-factor Structural rings Sensitivity Absorption Fermi level Graphene Refractive index Metamaterials Surface plasmon resonance Biomedical imaging

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Cross-Modality Guided Multi-Scale Feature Representation Based Image Compression for Industrial Scene Understanding

Cross-Modality Guided Multi-Scale Feature Representation Bas...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jingzheng Tu Yan Yan Ying Lu Cailian Chen Shiru Zhou China Academy of Aerospace Science and Innovation Beijing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Industrial surveillance videos capture subsequent video frames and deliver them to processing servers for video analytics. In such a case, image compression is a promising technology in industrial scene understanding due to its reduced transmission burden. Existing works focus on establishing macroscopic visual feature maps that contain compact image information. In this article, a cross-modality guided multi-scale feature representation based image compression method is proposed for understanding industrial scenes. Firstly, an image compressing framework with multi-grained representation (MGRIC) is proposed for industrial scene understanding, which builds image patch, structure, semantic, and signal levels feature maps. Secondly, an encoder-decoder architecture is designed for image compression with diverse granularities. Thirdly, an interaction policy between different levels is developed to ensure the subsequent level's decoding. Experiments on the CUB-200-2011 dataset demonstrate that the proposed MGRIC method achieves competitive performance, which verifies its effectiveness.

关键词： Image coding Visual analytics Surveillance Semantics Video compression Decoding Servers Informatics Image reconstruction Videos

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