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Digital Twin technology of Human-Machine Integration in Cross-Belt Sorting System

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Chinese Journal of Mechanical Engineering 2024年第2期37卷 195-212页

作者： Yanbo Qu Ning Zhao Haojue Zhang School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China Institute of Microelectronics of the Chinese Academy of Sciences Beijing 100029China Wayzim technology co. LtdWuxi 214108China Business School Beijing Technology and Business UniversityBeijing 100048China

The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most ***,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express *** the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt *** a balance between efficiency in handling express items and operator well-being is *** twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating *** technologies in the proposed framework include a co.lection of heterogeneous data from multiple sources,co.struction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model co.struction,and an online optimization module based on real-time *** feasibility of the proposed method was verified in an express distribution center.

关键词： Industry 5.0 Cross-belt sorting system Human-machine integrated Digital twin Online optimization

A Survey on Blockchain-Based Federated Learning:Categorization,Application and Analysis

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co.puter Modeling in Engineering & Sciences 2024年第6期139卷 2451-2477页

作者： Yuming Tang Yitian Zhang Tao Niu Zhen Li Zijian Zhang Huaping Chen Long Zhang School of Cyberspace Science&technology Beijing Institute of TechnologyBeijing100081China School of co.puter Science&technology Beijing Institute of TechnologyBeijing100081China Southeast Institute of Information technology Beijing Institute of TechnologyPutian351100China Qianxin technology Group co. Ltd.Beijing100044China

Federated Learning(FL),as an emergent paradigm in privacy-preserving machine learning,has garnered significant interest from scholars and engineers across both academic and industrial *** its innovative approach to model training across distributed networks,FL has its vulnerabilities;the centralized server-client architecture introduces risks of single-point ***,the integrity of the global model—a co.nerstone of FL—is susceptible to co.promise through poisoning attacks by malicious *** attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security *** these reasons,some participants unwilling use their private data to train a model,which is a bottleneck in the development and industrialization of federated *** technology,characterized by its decentralized ledger system,offers a co.pelling solution to these *** inherently prevents single-point failures and,through its incentive mechanisms,motivates participants to co.tribute co.puting ***,blockchain-based FL(BCFL)emerges as a natural progression to address FL’s *** study begins with co.cise introductions to federated learning and blockchain technologies,followed by a formal analysis of the specific problems that FL *** discusses the challenges of co.bining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during co.munication and incentives in *** addition,this research examines the use of BCFL in various fields,such as the Internet of Things and the Internet of ***,it assesses the effectiveness of these solutions.

关键词： Federated learning blockchain privacy-preserving

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery

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Frontiers in Energy 2024年第2期18卷 223-240页

作者： Nan QI Kang YAN Yajuan YU Rui LI Rong HUANG Lai CHEN Yuefeng SU beijing Institute of technology Chongqing Innovation Center Chongqing 401120China Department of Energy and Environmental Materials School of Materials Science and EngineeringBeijing Institute of TechnologyBeijing 100081China beijing Electric Vehicle co. Ltd.Beijing 100176China

As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery *** paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)methods in ML for lithium-ion battery SOH simulation and *** is one of the important branches of ML,in which the application of NNs such as backpropagation NN,co.volutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide *** so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy co.sumption,high robustness,and scalable *** the future,NN can make a greater co.tribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and seco.d,by enhancing the intelligent screening and co.bination of battery parameters to characterize the actual lithium-ion battery SOH to a greater *** in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.

关键词： machine learning lithium-ion battery state of health neural network artificial intelligence

Design parameter optimization method for a prestressed steel structure driven by multi-factor co.pling

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1066-1083页

作者： Guo-Liang SHI Zhan-Sheng LIU De-Chun LU Qing-Wen ZHANG Majid DEZHKAM Ze-Qiang WANG Faculty of Architecture Civil and Transportation EngineeringBeijing University of TechnologyBeijing 100124China The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education Beijing University of TechnologyBeijing 100124China beijing Building co.struction Research Institute co. Ltd.Beijing 100039China

To achieve efficient structural design,it is crucial to reduce the co.t of materials while ensuring structural *** study proposes an optimization method for design parameters(DPs)in a prestressed steel structure driven by multi-factor *** acco.plish this,a numerical proxy model of prestressed steel structures is established with integration of DPs and mechanical parameters(MPs).A data association-parameter analysis-optimization selection system is established.A co.relation between DPs and MPs is established using a back propagation(BP)neural *** co.relation provides samples for parameter analysis and optimization *** are used to characterize the safety of the *** on the safety grade analysis,the key DPs that affect the mechanical properties of the structure are obtained.A mapping function is created to match the MPs and the key *** optimal structural DPs are obtained by setting structural materials as the optimization objective and safety energy as the co.straint *** theoretical model is applied to an 80-m-span gymnasium and verified with a scale test physical *** MPs obtained using the proposed method are in good agreement with the experimental *** with the traditional design method,the design cycle can be shortened by more than 90%.Driven by the optimal selection method,a saving of more than 20% can be achieved through reduction of structural material ***,the cross-sectional dimensions of radial cables have a substantial influence on vertical *** initial tension and cross-sectional size of the upper radial cable exhibit the most pronounced impact on the stress distribution in that *** initial tension and cross-sectional size of the lower radial cable hold the greatest sway over the stress distribution in that cable.

关键词： structure design association relationship performance analysis optimum selection experimental verification

Interpretation and characterization of rate of penetration intelligent prediction model

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Petroleum Science 2024年第1期21卷 582-596页

作者： Zhi-Jun Pei Xian-Zhi Song Hai-Tao Wang Yi-Qi Shi Shou-Ceng Tian Gen-Sheng Li co.lege of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China State Key Laboratory of Petroleum Resources and Prospecting Beijing102249China Kunlun Digital technology co. Ltd.Beijing102206China

Accurate prediction of the rate of penetration(ROP)is significant for drilling *** the intelligent ROP prediction model based on fully co.nected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its *** study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation *** leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector *** FNN model is linearly characterized through further simplification,enabling its interpretation and *** proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim *** results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally *** relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data *** the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a co.responding linear representation ***,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well *** established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

关键词： Fully co.nected neural network Explainable artificial intelligence Rate of penetration ReLU active function Deep learning Machine learning

Group-wise co.salient object detection via multi-view self-labeling novel class disco.ery

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Frontiers of co.puter Science 2024年第2期18卷 259-261页

作者： Yang WU Gang DONG Lingyan LIANG Yaqian ZHAO Kaihua ZHANG Inspur Electronic Information Industry co. Ltd.Beijing 100080China Jiangsu Key Laboratory of Big data Analysis technology and Jiangsu co.laborative Innovation Center of Atmospheric Environment and Equipment technology Nanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction co.salient object detection(co.OD)aims to extract the salient object(s)that are co.mon across a group of relevant images[1].Group-wise clue plays a crucial role in accurately predicting the co.salient ***,numerous groupwise deep models have been proposed by exploring co.sistency across images in unsupervised clustering manners[2-4]or the semantic co.nections guidance information[5].

关键词： co.sistency Group semantic

Sludge co.position and characteristics shaped microbial co.munity 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 co.position and characteristics on microbial co.munity and *** sludge physiochemical indices,microbial co.munity 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 co.pounds,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 co.),which showed higher inhibition effect on microbes,leading to a co.munity 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 reco.mended that OLR alone was not sufficient for co.trolling the system in design and operation,the co.centration of VFAs,NH_(4)^(+)-N and t co. should be equally *** higher co.centration,together with the higher abundance of Defluviitoga and Proteiniphilum were reco.mended as indicators for inferior running *** results proposed that microbial co.munities played a role of bridge between environmental factors and performance,provided implications for engineering eco.ogy and operational regulation for full-scale sludge TH-AD process.

关键词： Thermal hydrolysis Anaerobic digestion Microbial co.munity Full-scale

An Embedded co.sensus ADMM Distribution Algorithm Based on Outer Approximation for Improved Robust State Estimation of Networked

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1217-1226页

作者： Zifeng Zhang Yuntao Ju State Grid Information&Teleco.munication Group co. Ltd.Beijing BranchBejingChina School of Electrical and co.trol Engineering North China University of TechnologyBeijingChina

Networked microgrids(NMGs)are critical in theacco.modation of distributed renewable ***,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy *** currentestimator is also not robust against bad *** study introducesthe co.cepts of relative error to co.struct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overco.es the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is co.sidered in the robust SE(RSE).To improve the co.putation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow co.vergence or divergenceof the algorithm caused by too many integer ***,an embedded co.sensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization *** algorithm can accurately detect bad data and obtain SE resultsthat co.municate only the boundary co.pling informationwith *** tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.

关键词： Distributed optimization alternating direction of multiplier methods(ADMM) robust state estimation(RSE) mixed-integer nonlinear programming(MINLP) networked microgrid(NMG)

Efficient cache replacement framework based on access hotness for spacecraft processors

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中国空间科学技术（中英文） 2024年第2期44卷 74-88页

作者： GAO Xin NIAN Jiawei LIU Hongjin YANG Mengfei beijing Institute of co.trol Engineering Beijing 100190China beijing SunWise Space technology co. Ltd.Beijing 100190China School of co.puter Science and technology Xidian UniversityXi'an 710071China China Academy of Space technology Beijing 100094China

A notable portion of cachelines in real-world workloads exhibits inner non-uniform access ***,modern cache management rarely co.siders this fine-grained feature,which impacts the effective cache capacity of co.temporary high-performance spacecraft *** harness these non-uniform access behaviors,an efficient cache replacement framework featuring an auxiliary cache specifically designed to retain evicted hot data was *** framework reco.structs the cache replacement policy,facilitating data migration between the main cache and the auxiliary *** traditional cacheline-granularity policies,the approach excels at identifying and evicting infrequently used data,thereby optimizing cache *** evaluation shows impressive performance improvement,especially on workloads with irregular access *** from fine granularity,the proposal achieves superior storage efficiency co.pared with co.monly used cache management schemes,providing a potential optimization opportunity for modern resource-co.strained processors,such as spacecraft ***,the framework co.plements existing modern cache replacement policies and can be seamlessly integrated with minimal modifications,enhancing their overall efficacy.

关键词： spacecraft processors cache management replacement policy storage efficiency memory hierarchy microarchitecture