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Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

IEEE INTERNET OF THINGS JOURNAL 2021年第14期9卷 12450-12459页

作者： Chen, Qimei Xu, Xiaoxia You, Zehua Jiang, Hao Zhang, Jun Wang, Fei-Yue Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

As a key infrastructural technology, Industrial Internet of Things (IIoT) and its related techniques have emerged in the age of Industrial Internet. Among them, an increasing popular and attractive federated edge learning (FEL) mechanism, which performs data analysis and inference at the edge devices distributedly, and aggregates local FEL units at a centralized controller, is introduced to meet the stringent data privacy and low-latency requirements for high-stake IIoT devices. Due to the bandwidth limitation, only parts of the IIoT devices can be selected to transmit their local FEL models to the centralized controller at each learning step. However, the centralized controller prefers to collect all the local FEL models to generate the global FL model since each IIoT device has a differential data set. Existing works mainly focus on selecting an appropriate subset of IIoT devices through advanced scheduling mechanisms without extending the resource bandwidth. However, the new radio in unlicensed spectrum (NR-U) technology in the 5G network opens up new possibilities for FEL since it is a privately owned network with fruitful bandwidth resources. We thus propose a novel communication-efficient FEL mechanism for NR-U-based IIoT networks, which aims to select data importance IIoT devices for local training under relatively sufficient unlicensed resources. The objective function is formulated as a tradeoff between total FEL data importance and the transmission latency via joint learning, device selection, and resource management scheduling, which is a mixed-integer nonlinear programming (MINLP). To deal with this problem, an alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm with low computational complexity has been used. Closed-form expressions for both optimal device selection and resource management are derived, which highlighted significant insights. Numerical results demonstrate the algorithmic advantages and structural

关键词： Industrial Internet of Things Wireless fidelity Servers Resource management Data models Wireless communication Data privacy Communication efficiency federated edge learning (FEL) Industrial Internet of Things (IIoT) new radio in unlicensed spectrum (NR-U) resource management user selection

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Could ChatGPT Imagine: Content control for Artistic Painting Generation Via Large Language Models

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JOURNAL OF INTELLIGENT & ROBOTIC systems 2023年第2期109卷 39-39页

作者： Lu, Yue Guo, Chao Dou, Yong Dai, Xingyuan Wang, Fei-Yue Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Macao Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

Intelligent systems and human-machine interactions have consistently provided convenience in both work and daily life. Artificial Intelligence Generated Content (AIGC) can assist humans in artistic creation by generating painting images based on textual descriptions. However, the quality of generated painting images depends heavily on well-designed prompts, which are labor-intensive and time-consuming in painting creation. Large Language Models (LLMs) like ChatGPT have shown impressive performance in linguistic tasks such as question answering and logical inference, demonstrating strong linguistic intelligence. This paper proposes an assistant painting creation approach to provide precise content control for painting generation by combining LLMs with text-to-image generative models and evaluates the performance of the proposed approach on painting content generation and painting element arrangement. The experimental results show that our approach can provide clear guidance on rich painting content and reasonable arrangements of painting elements, demonstrating its ability of text-based painting scene imagination. In painting generation tasks, LLMs like ChatGPT can help the text-to-image models with precise control over the painting content and improve the overall painting results.

关键词： Intelligent systems Human-machine interactions Artistic painting generation Large language model ChatGPT Linguistic intelligence

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A Time-Series Augmentation Method Based on Empirical Mode Decomposition and Integrated LSTM Neural Network 44

A Time-Series Augmentation Method Based on Empirical Mode De...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Li, Chenguang Yang, Hongjun Cheng, Long Huang, Fubiao Chinese Acad Sci Inst Automat State Key Lab Control & Management Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Boai Hosp China Rehabil Res Ctr Beijing 100068 Peoples R China

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Adequate patients' data have always been critical for disease assessment. However, large amounts of patient data are often difficult to collect, especially when patients are required to complete a series of assessment movements. For example, assessing the hand motor function of stroke patients or Parkinson's patients requires patients to complete a series of evaluation movements, and it is often difficult for patients to complete each group of actions multiple times, resulting in a small amount of data. To solve the problem of insufficient data quantity, this study proposes a data augmentation method based on empirical mode decomposition and integrated long short-term memory neural network (EMD-ILSTM). The method mainly consists of two parts: one is to decompose the raw signal by the method of EMD, and the other is to use LSTM for data augmentation of the decomposed signal. Then, the method is tested on the public dataset named Ninaweb, and the test results show that the classification accuracy can be improved by 5.2% by using the augmented data for classification tasks. Finally, clinical trials are conducted to verify that after dimensionality reduction, the augmented data and raw data have smaller intra-class distances and larger inter-class distances, indicating that data augmentation is effective.

关键词： Dimensionality reduction Empirical mode decomposition Stroke (medical condition) Clinical trials Biology Task analysis Biological neural networks

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Two-Stage Co-Optimization for Utility-Social systems With Social-Aware P2P Trading

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第4期10卷 1875-1887页

作者： Zhao, Pengfei Li, Shuangqi Hu, Paul Jen-Hwa Cao, Zhidong Gu, Chenghong Yan, Xiaohe Huo, Da Hernando-Gil, Ignacio Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100864 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Univ Utah David Eccles Sch Business Salt Lake City UT 84112 USA North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Cranfield Univ Sch Water Energy & Environm Bedford MK43 0AL England Univ Bordeaux ESTIA Inst Technol F-64210 Bidart France

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water-energy-social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

关键词： Water heating Optimization Costs Cogeneration Water resources Resistance heating Peer-to-peer computing Distributionally robust optimization (DRO) peer-to-peer (P2P) trading smart city social-aware management water-energy-social nexus

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An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Dong, Yunyun Liang, Chenbin Sun, Zengguo Shaanxi Normal Univ Northwest Land & Resource Res Ctr Xian 710119 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Image registration based on phase correlation has drawn extensive attention due to its high accuracy and efficiency. However, due to changes in image content, nonlinear gray difference, and other noises of image pairs, the line fitting of phase angle points acquired by the singular value decomposition (SVD) and 1-D phase unwrapping is also an intractable problem in the process of phase correlation image registration. In this letter, we propose a probability-guided random sample consensus (RANSAC), namely utilizing a probability to guide the hypothesis search of RANSAC to fit the line accurately and efficiently. The probability of each phase angle point is predicted by a deep convolution neural network (DCNN) of ProbNet we build and the parameters of the network are optimized effectively by integrating probability-guided RANSAC into an end-to-end trainable displacement estimation pipeline. The qualitative experiment is carried out to illustrate the effectiveness of the proposed method. In the quantitative experiments, two competitive methods of locally optimized RANSAC (LO-RANSAC) and least -square fitting (LSQ) and the naive RANSAC method are brought in to compare. The experimental result illustrates that the proposed method has an increase in the success rate of displacement estimation and efficiency.

关键词： Correlation Task analysis Training Convolution Remote sensing Image registration Neural networks Image registration phase correlation probability-guided random sample consensus (RANSAC)

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Game-theoretical explorations of the mesoscale flow structure and regime transitions in bubble columns

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Particuology 2020年第1期18卷 100-108页

作者： Shuo Yuan Yifen Mu Xiaoping Guan Zhixin Liu Jianhua Chen Ning Yang Lin Zhang Key Lab of Systems and Control Institute of Systems SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Understanding the mesoscale structure and regime transition in bubble columns is of great significance for reactor design and *** on the energy-minimization multiscale(EMMS)model,a noncooperative game model with constraints is proposed to investigate the structural properties of gas-liquid systems in which small and large bubbles are chosen as players and the energy consumption form the objective *** conservation equations of the system can be regarded as the constraints of the *** the formulated noncooperative game model,the concept of the generalized Nash equilibrium(GNE)is used to characterize the *** algorithm is developed to numerically compute the GNE and some important structural parameters in the *** numerical results show the existence of the GNE for all values of the superficial gas velocity *** Ug varies,the trends in the state variables can be observed and the critical point of Ug *** overall trend of the flow regime transition agrees with the original EMMS model and experimental results,although the GNE calculation also reveals different single-bubble dominant mechanisms with increasing Ug.

关键词： Bubble column Mesoscale Regime transition Game theory Generalized Nash equilibrium EMMS model

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Improving University Faculty Evaluations via multi-view Knowledge Graph

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 117卷 181-192页

作者： Lin, Qika Zhu, Yifan Lu, Hao Shi, Kaize Niu, Zhendong Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15260 USA

University faculties generate a large amount of heterogeneous data in e-learning environments that online systems and toolkits have made widely available in all aspects of teaching and scientific researching activities. How to use the data efficiently and scientifically for faculty evaluations has recently become an important issue in university performance systems. However, it is still a challenge to comprehensively assess faculty members using multi-source and multi-modal data due to the lack of uniform representations and evaluation processes. To this end, this paper proposes a novel University Faculty Evaluation System based on a multi-view Knowledge Graph (UFES-KG) that integrates heterogeneous faculty data. Relevant data, collected both on the Internet and through university-administered internal systems, includes faculty information such as scientific research papers, patents, funds, monographs, awards, professional activities and teaching performance. Furthermore, we construct entity representations through knowledge graph embedding methods to retain their semantic information. In addition, by integrating the academic development status of scholars in the previous three years as well as student evaluation data, this paper proposes an academic development factor (ADF) for making predictions about faculty academic development. The experimental results show that this factor is closely related to the features of the knowledge graph and student evaluations. In a certain case study, this factor is superior to the traditional h-index, g-index, and RG score. Intuitively and scientifically, this multi-view approach can improve evaluations of university faculties. (C) 2020 Elsevier B.V. All rights reserved.

关键词： University faculty evaluation Knowledge graph Academic development prediction E-learning

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Process Monitoring, Diagnosis and control of Additive Manufacturing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 1041-1067页

作者： Fang, Qihang Xiong, Gang Zhou, MengChu Tamir, Tariku Sinshaw Yan, Chao-Bo Wu, Huaiyu Shen, Zhen Wang, Fei-Yue Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent M Cloud Comp Ctr Dongguan 523808 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automation Sci & Engn Xian 710049 Shaanxi Peoples R China

Additive manufacturing (AM) can build up complex parts in a layer-by-layer manner, which is a kind of novel and flexible production technology. The special manufacturing capability of AM shows great application potential in various fields. However, an open-loop control method cannot guarantee the reliability and repeatability of an AM process. Defects often occur to deteriorate product quality and lead to material and time waste, which hinders the development of AM industry. In this regard, a lot of efforts have been made to make an AM process more controllable. This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and closed-loop control of an AM process. These three loops constitute the inspection and control of AM from the machine level to product level. Specifically, the measurement requirements for monitoring techniques, defect detection, fault diagnosis, and closed-loop control are summarized. The challenges and future trends in realizing a more reliable and repeatable AM process are discussed. Note to Practitioners-This survey is motivated by urgent need to solve product quality problems in additive manufacturing (AM) caused by open-loop control. Three feedback loops can be established to solve them. The first one is defect detection that inspects part quality during fabrication. The second one is the fault diagnosis of a 3-D printer that monitors the health and operation conditions of its actuators. The last one is closed-loop control that improves AM process reliability and repeatability by regulating process variables in real time. These three loops are all based on the feedback signals of in-situ monitoring systems. This paper reviews the related studies and provides guidance for establishing the monitoring systems, performing defect detection and fault diagnosis, and designing closed-loop control systems, which helps real

关键词： Additive manufacturing (AM) in-situ monitoring defect detection fault diagnosis closed-loop control

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A novel underwater image synthesis method based on a pixel-level self-supervised training strategy

A novel underwater image synthesis method based on a pixel-l...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Wu, Zhiheng Wu, Zhengxing Lu, Yue Wang, Jian Yu, Junzhi Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781665436786

With the rapid development of deep neural networks, underwater vision plays an increasingly important role in the underwater robotic operation. However, the scarce underwater datasets greatly limit the performance of deep learning on underwater visual tasks, further hindering the applications of underwater operation. To solve this problem, we propose an underwater image synthesis method, which can directly convert the natural light image into the synthetic underwater image end-to-end. Particularly, a pixel-level self-supervised training strategy is designed to maximize the structural similarity between the synthesized and real images, through training the real underwater images. Finally, extensive experiments are carried out, and the obtained results demonstrate the effectiveness and superiority of our methods by quantitative and qualitative comparisons. The proposed underwater image synthesis method offers a valuable sight for underwater vision and manipulating control. © 2021 IEEE.

关键词： Deep neural networks

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Hierarchical Reinforcement Learning-Based Policy Switching Towards Multi-Scenarios Autonomous Driving

Hierarchical Reinforcement Learning-Based Policy Switching T...

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International Joint Conference on Neural Networks (IJCNN)

作者： Guo, Youtian Zhang, Qichao Wang, Junjie Liu, Shasha Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artif Intelligence Beijing Peoples R China

ISBN: (纸本)9780738133669

Reinforcement learning has gained a lot of attention and applications in the field of autonomous driving in recent years. However, the actual scenarios of automatic driving applications are often diverse, so the reinforcement learning algorithm using only a single driving strategy is difficult to meet the multiple requirements of efficiency and safety in the multi-scenarios autonomous driving task. To solve this challenge, we propose a hierarchical reinforcement learning structure to learn a unified top-level switching master policy between different driving styles policies. The whole framework uses a bottom-up training manner with diverse reward function designing. Through experimental comparison, our method exceeds the performance of single policy and rule-based switching strategy. Based on this framework, we won the first place in the DAI 2020 Autonomous Driving Workshop single-agent track competition.

关键词： Autonomous Driving Hierarchical Reinforcement Learning Semi-Markov Decision Process

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