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Revisiting the LQR Problem of Singular systems

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2236-2252页

作者： Komeil Nosrati Juri Belikov Aleksei Tepljakov Eduard Petlenkov IEEE the Department of Computer systems Tallinn University of Technology the Department of software Science Tallinn University of Technology

In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.

关键词： DC motor optimal control penalized weighted regression power system quadratic regulator singular system

Application of sparse S transform network with knowledge distillation in seismic attenuation delineation

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Petroleum Science 2024年第4期21卷 2345-2355页

作者： Nai-Hao Liu Yu-Xin Zhang Yang Yang Rong-Chang Liu Jing-Huai Gao Nan Zhang School of Information and Communications Engineering Xi'an Jiaotong University School of software Engineering Xi'an Jiaotong University PetroChina research Institute of Petroleum Exploration and Development (RIPED) CNPC research Institute of Exploration and Development Yumen Oilfield Company CNPC

Time-frequency analysis is a successfully used tool for analyzing the local features of seismic ***, it suffers from several inevitable limitations, such as the restricted time-frequency resolution,the difficulty in selecting parameters, and the low computational efficiency. Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis. The sparse S transform network(SSTNet) is first built to map the relationship between synthetic traces and sparse S transform spectra, which can be easily pre-trained by using synthetic traces and training labels. Next, we introduce knowledge distillation(KD) based transfer learning to re-train SSTNet by using a field data set without training labels, which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way, we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels. To test the availability of the suggested KD-SSTNet, we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.

关键词： S transform Deep learning Knowledge distillation Transfer learning Seismic attenuation delineation

Study on tungsten sintering temperature field based on electromagnetic simulation and computational fluid dynamics

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Tungsten 2024年第2期6卷 355-366页

作者： Lu-Yan Wang Yue-Guang Yu Jian-Xin Dong Kai-Ping Du Shan-Yu Liu Cao-Bing Li University of Science and Technology Beijing Beijing 100083China BGRIMM Technology group Beijing 100160China China Iron&Steel research Institute group Beijing 100081China

In order to study the influence of various sintering variables more intuitively and clearly,electromagnetic field analysis and computational fl uid dynamics are used to analyze the infl uence of gas flow,heating power and other process parameters on the temperature range of medium-frequency sintering in this *** results indicate that the uniformity of temperature fi eld is determined by the gas flow rate and heating *** the heating power is 50 kW,the stable temperature drops about 10 K for every 1 m^(3)·h^(−1)increase of hydrogen *** peak value of the maximum temperature diff erence on the tungsten rods gradually increases with the increase of the hydrogen flow rate,and it appears slightly *** temperature distribution of tungsten rods in furnace is similar with diff erent hydrogen flow *** the same time,the closer to the furnace wall,the higher the temperature of the tungsten *** temperature diff erence between tungsten rods mainly occurred within 1 h after the beginning of *** the heating power can improve the stable temperature,but has little eff ect on the heating *** heating power can increase the peak value of the maximum temperature diff erence on the tungsten rods,but has little eff ect on the occurrence time of the peak value of the maximum temperature diff erence on the tungsten *** the heating power will increase the temperature diff erence of a single tungsten rod,but the temperature diff erence of a single tungsten rod never exceeds 65 *** study can be used to guide the process optimization *** with numerical simulation calculation,the sintering parameters with low energy consumption and high temperature uniformity can be quickly and eff ectively found,thus improving the microstructure and properties of sintered tungsten products.

关键词： Tungsten products Sintering Hydrogen flow rate Heating power

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Deep Industrial Image Anomaly Detection: A Survey

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Machine Intelligence research 2024年第1期21卷 104-135页

作者： Jiaqi Liu Guoyang Xie Jinbao Wang Shangnian Li Chengjie Wang Feng Zheng Yaochu Jin research Institute of Trustworthy Autonomous systems Southern University of Science and TechnologyShenzhen 518055China NICEgroup Universityof SurreyGuildford GU27YXUK Youtu Lab TencentShanghai 200233China NICE group Bielefeld UniversityBielefeld 33619Germany

The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection(IAD).In this pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and *** addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed ***,we highlight several opening chal-lenges for image anomaly *** merits and downsides of representative network architectures under varying supervision are ***,we summarize the research findings and point out future research *** resources are available at https://***/M-3LAB/awesome-industrial-anomaly-detection.

关键词： Image anomaly detection defect detection industrial manufacturing deep learning computer vision

Learnware:small models do big

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Science China(Information Sciences) 2024年第1期67卷 39-50页

作者： Zhi-Hua ZHOU Zhi-Hao TAN National Key Laboratory for Novel software Technology Nanjing University

There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills,the difficulty of continual learning,the risk of catastrophic forgetting,and the leaking of data privacy/*** research efforts have been focusing on one of those concerned issues separately,paying less attention to the fact that most issues are entangled in *** prevailing big model paradigm,which has achieved impressive results in natural language processing and computer vision applications,has not yet addressed those issues,whereas becoming a serious source of carbon *** article offers an overview of the learnware paradigm,which attempts to enable users not to need to build machine learning models from scratch,with the hope of reusing small models to do things even beyond their original purposes,where the key ingredient is the specification which enables a trained model to be adequately identified to reuse according to the requirement of future users who know nothing about the model in advance.

关键词： artificial intelligence machine learning learnware

Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic research Institute Nanjing210044China Department of Information systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

Sludge composition and characteristics shaped microbial community and further determined process performance: A study on full-scale thermal hydrolysis-anaerobic digestion processes

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Journal of Environmental Sciences 2024年第3期137卷 96-107页

作者： Wei Liu Chenxiang Sun Wei Li Tianle Li Zhan Chen Jiawei Wang Zhengran Ren Xianghua Wen School of Environment Tsinghua UniversityBeijing 100084China Jinneng Holding group DatongShanxi 037000China research and Development Center Beijing Drainage Group Co.Ltd.Beijing 100124China Beijing Dabeinong Science and Technology group Co. Ltd.Beijing 100080China

Anaerobic digestion (AD) with thermal hydrolysis (TH) pretreatment is a promising process for excess sludge treatment,while there lacks of the knowledge from full-scale process about the impact of sludge composition and characteristics on microbial community and *** sludge physiochemical indices,microbial community and performance data of four full-scale TH-AD plants were characterized,and their relationships was *** four plants were operated under almost similar total organic loading rate (OLR)but their methanogenesis performance differentiate into two groups,namely superior group(SupG) and the inferior group (Inf G).In both groups,TH effectively solubilized particulate organic compounds,meanwhile raised the ammonia nitrogen (NH_(4)^(+)-N) and volatile fatty acid (VFA) *** with the Sup G,thermal hydrolyzed sludge of Inf G had higher level of VFAs,NH_(4)^(+)-N and total chemical oxygen demand (t COD),which showed higher inhibition effect on microbes,leading to a community with lower diversity,lower abundance of carbohydrate degrading functional guild,higher protein degrading one,and methanogens that adapted to limited substrates,and further declined the methane production ***,it was recommended that OLR alone was not sufficient for controlling the system in design and operation,the concentration of VFAs,NH_(4)^(+)-N and t COD should be equally *** higher concentration,together with the higher abundance of Defluviitoga and Proteiniphilum were recommended as indicators for inferior running *** results proposed that microbial communities played a role of bridge between environmental factors and performance,provided implications for engineering ecology and operational regulation for full-scale sludge TH-AD process.

关键词： Thermal hydrolysis Anaerobic digestion Microbial community Full-scale

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NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

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Digital Communications and Networks 2024年第2期10卷 439-449页

作者： Chaopeng Guo Zhaojin Zhong Zexin Zhang Jie Song software Campus Northeastern UniversityShenyang110000LiaoningChina

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing *** accurate energy prediction approach is critical to provide measurement and lead optimization ***,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training *** paper presents a novel energy prediction model,*** treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy *** has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and *** results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture *** code is available at https://***/NEUSoftGreenAI/***.

关键词： Internet of things Neural network energy prediction Graph neural networks Graph structure embedding Multi-head attention

Challenges and Opportunities of Sub-6 GHz Integrated Sensing and Communications for 5G-Advanced and Beyond

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Chinese Journal of Electronics 2024年第2期33卷 323-325页

作者： Yuhong HUANG China Mobile research Institute China Mobile Communications group Co. Ltd

The integrated sensing and communications(ISAC) technology has been perceived as a key feature of5G-Advanced and beyond mobile communication networks. Compared with sensing in millimeter-wave bands, sensing at sub-6 GHz band is an exceptional incentive to promote the industrialization process of ISAC due to its incomparable advantages in industrial development, especially for intelligent transportation and smart drone networks. This paper elaborates on the top challenges of sub-6 GHz ISAC technologies, as well as the potential solutions to improve the sensing capability.

关键词： Electric potential Millimeter wave technology Transportation Mobile communication Sensors Millimeter wave communication Drones