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An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool:Low-Latency,Energy-Efficient,and Secure 认领 引用
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作者 Zhiwen Lin Kaien Wei +4 位作者 Yiqiao Wang Chuanhai Chen Jinyan Guo Qiang Cheng Zhifeng Liu 《Engineering》 SCIE EI CSCD 2026年第1期201-218,共18页
Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generat... Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships.The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks.Furthermore,there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency.These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data.This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration.This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes,task characteristics,dependency complexity,and resource availability,ultimately facilitating low-latency,energy-efficient,and secure task processing.A novel hybrid hyper-heuristic algorithm has been developed to address largescale task allocation challenges in heterogeneous edge-cloud environments,enabling the flexible allocation of computational resources and performance optimization.Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36%in task processing time and 7.89%in energy efficiency when compared to state-of-the-art techniques,all while maintaining superior security performance.Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing,complex dependency task collaboration,high-computational machine learning workloads,and continuous batch task deployment scenarios,achieving a 37.03%reduction in latency and a 25.93%optimization in energy use relative to previous generation collaboration methods.These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools,thereby contributing to the evolution of next-generation smart manufacturing systems. 展开更多
关键词 Intelligent machine tools Edge-cloud collaboration Task offloading Resilient resources Sustainable computing
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FUTURE OF LAW CONFERENCE: THE INTERNET OF THINGS, SMART CONTRACTS AND INTELLIGENT MACHINES 认领 引用 被引量：1
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作者 ZHANG Jiyu ZHANG Wenke 《Frontiers of Law in China-Selected Publications from Chinese Universities》 2017年第4期673-674,共2页
From 26 to 27 October 2017, the Centre for Cross-Border Commercial Law in Asia of Singapore Management University （SMU） Law School held an international conference entitled ＂Future of Law Conference： The Internet ... From 26 to 27 October 2017, the Centre for Cross-Border Commercial Law in Asia of Singapore Management University （SMU） Law School held an international conference entitled ＂Future of Law Conference： The Internet of Things, Smart Contracts and Intelligent Machines＂ in Singapore. The conference brought together the leading thinkers in academia and practice in the field of information technology law to discuss the legal and regulatory implications of recent technological developments. Associate Professor ZHANG Jiyu and Associate Professor DING Xiaodong of the Law and Technology Institute of Renmin Law School were invited to attend the conference. 展开更多
关键词 FUTURE OF LAW CONFERENCE THE INTERNET OF THINGS, SMART CONTRACTS INTELLIGENT MACHINES
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Intelligent medicine empowering the four diagnostic methods of traditional Chinese medicine:a bibliometric analysis and discussion 认领 引用
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作者 Jian-Lin Wei Hai-Na Zhu +4 位作者 Shi-Jia Wang Qian-Qian Xu Shuang-Qiu Wang Xiao-Yu Zhang Hong-Cai Shang 《Traditional Medicine Research》 2025年第12期29-39,共11页
Background:The integration of intelligent healthcare technologies with traditional Chinese medicine(TCM)diagnostic practices holds significant potential to address longstanding challenges in subjectivity and standardi... Background:The integration of intelligent healthcare technologies with traditional Chinese medicine(TCM)diagnostic practices holds significant potential to address longstanding challenges in subjectivity and standardization;nevertheless,a systematic analysis of research trends,technological foci,and interdisciplinary collaboration within this field remains underexplored.Methods:This study employs bibliometric analysis to examine 497 articles(2003-2025)retrieved from Web of Science,PubMed,and CNKI.Visualization tools(VOSviewer and CiteSpace)were utilized to map research evolution,collaboration networks,and thematic clusters.Results:The analysis indicates a marked upsurge in research on this topic after 2019.Key research clusters identified through bibliometric analysis encompass AI-enabled pattern recognition,neural network architectures,algorithmic classification models,digital tongue image analysis,and computational syndrome differentiation frameworks.These clusters collectively address the subjectivity and standardization challenges inherent in TCM diagnosis.Conclusion:Intelligent healthcare technologies can significantly improve the accuracy,efficiency,and reproducibility of TCM diagnostic practices.Future work should foster international collaboration and develop multi-modal,clinically validated diagnostic models. 展开更多
关键词 bibliometric analysis intelligent medicine traditional Chinese medicine diagnostics artificial intelligence machine intelligence clinical assessment technologies
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From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation 认领 引用 被引量：13
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作者 Yakun ZHANG Guofang GONG +2 位作者 Huayong YANG Jianbin LI Liujie JING 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2024年第5期357-381,共25页
Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional sh... Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology. 展开更多
关键词 Intelligent shield machine Tunnel boring machine(TBM) Tunnel boring robot(TBR) Self-driving Autonomous control Shield machine
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Machine Intelligence for Mental Health Diagnosis: A Systematic Review of Methods, Algorithms, and Key Challenges 认领 引用
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作者 Ravita Chahar Ashutosh Kumar Dubey 《Computers, Materials & Continua》 SCIE EI 2026年第1期67-131,共65页
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a... Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare. 展开更多
关键词 Mental health machine intelligence artificial intelligence deep learning mental disorders diagnostic precision
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Intelligent manufacturing system of impeller for computer numerical control(CNC) programming based on KBE 认领 引用 被引量：6
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作者 王凌云 黄红辉 +1 位作者 Rae W.West 王大中 《Journal of Central South University》 SCIE EI CAS 2014年第12期4577-4584,共8页
To solve the problem of advanced digital manufacturing technology in the practical application, a knowledge engineering technology was introduced into the computer numerical control(CNC) programming. The knowledge acq... To solve the problem of advanced digital manufacturing technology in the practical application, a knowledge engineering technology was introduced into the computer numerical control(CNC) programming. The knowledge acquisition, knowledge representation and reasoning used in CNC programming were researched. The CNC programming system functional architecture of impeller parts based on knowledge based engineering(KBE) was constructed. The structural model of the general knowledge-based system(KBS) was also constructed. The KBS of CNC programming system was established through synthesizing database technology and knowledge base theory. And in the context of corporate needs, based on the knowledge-driven manufacturing platform(i.e. UG CAD/CAM), VC++6.0 and UG/Open, the KBS and UG CAD/CAM were integrated seamlessly and the intelligent CNC programming KBE system for the impeller parts was developed by integrating KBE and UG CAD/CAM system. A method to establish standard process templates was proposed, so as to develop the intelligent CNC programming system in which CNC machining process and process parameters were standardized by using this KBE system. For the impeller parts processing, the method applied in the development of the prototype system is proven to be viable, feasible and practical. 展开更多
关键词 knowledge engineering generalized knowledge intelligent programming impeller machining
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Study on intelligent digital welding machine with a self-learning function 认领 引用
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作者 张晓莉 朱强 +2 位作者 李钰桢 龙鹏 薛家祥 《China Welding》 EI CAS 2013年第4期74-80,共7页
A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced th... A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning. 展开更多
关键词 intelligent digital welding machine self-learning large-step calibration
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Design of intelligent distinguishing system for slot machine based on PLC 认领 引用
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作者 王春常 顾强 安晓红 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2015年第4期368-372,共5页
Due to the emergence of a large number of counterfeit notes and incomplete coins in the slot machine of self-service bus, to improve the automization of intelligent slot machine, based on multi-sensor testing technolo... Due to the emergence of a large number of counterfeit notes and incomplete coins in the slot machine of self-service bus, to improve the automization of intelligent slot machine, based on multi-sensor testing technology, using programming log- ic controller （PLC） as the core of the whole system, the PLC hardware design and software design are accomplished for the first time to detect the counterfeit notes and coins. The system was tested by many groups of experiments. The results show that the system has reliable recognition rate, good flexibility and stability, reaching the accuracy of 97%. 展开更多
关键词 intelligent slot machine distinguishing system programming logic controller （PLC）
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Machine Memory Intelligence:Inspired by Human Memory Mechanisms 认领 引用 被引量：2
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作者 Qinghua Zheng Huan Liu +9 位作者 Xiaoqing Zhang Caixia Yan Xiangyong Cao Tieliang Gong Yong-Jin Liu Bin Shi Zhen Peng Xiaocen Fan Ying Cai Jun Liu 《Engineering》 SCIE EI CSCD 2025年第12期24-35,共12页
Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,su... Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M2I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M2I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M2I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI. 展开更多
关键词 Machine memory intelligence Neural mechanism Associative representation Continual learning Collaborative reasoning
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The Research on Hybrid Intelligent Fault-diagnosisSystem of CNC Machine Tools 认领 引用 被引量：1
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作者 WANG Runxiao ZHOU Hui +1 位作者 QIN Xiansheng JIAN Chongjun 《International Journal of Plant Engineering and Management》 2000年第4期129-135,共7页
After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and ... After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and the running instance of the system. Through tryout and validation, we attain satisfactory results. 展开更多
关键词 CNC machine tools hybrid mechanism intelligent diagnosis machine fault
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Research on the Application of Intelligent Bionic Robot Horse in Juvenile Equestrian Teaching Case Study: Beijing Chaoyang Park Youth Equestrian Center 认领 引用
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作者 Haisu WANG Hong BAI +1 位作者 Jian PANG Yongheng HU 《Journal of Sports Science》 2023年第1期33-42,共10页
Research purposes:in this study,the intelligent bionic robotic horse is introduced into the equestrian teaching for teenagers,compared with the traditional teaching mode of using real horses.This research aims to expl... Research purposes:in this study,the intelligent bionic robotic horse is introduced into the equestrian teaching for teenagers,compared with the traditional teaching mode of using real horses.This research aims to explore the effectiveness of using intelligent bionic robotic horse in equestrian teaching for teenagers,as well as to promote the further development of equestrian teaching for teenagers in China,and to promote the introduction of new technology into the equestrian teaching area in the age of internet.Research methods:the methods used were literature method;mathematical statistics;interviewing the equestrian coaches who participated in the experiment;experimental method.The intelligent bionic robotic horse used in this research is the GETTAEN intelligent bionic robotic horse produced by Joy Game Technology Co.,Ltd.The bionic robotic horse is equipped with Internet technology,and the course is supervised and produced by senior coaches of China Equestrian Team.It also includes multiple operation modes.In this study,40 amateur students in Beijing Chaoyang Park Youth Equestrian Center were selected as the experimental subjects.Students will spend 40 h on studying how to ride a horse.Twenty(20)students in the experimental group,they are accommodated with 20 h of bionic robotic horse courses and 20 h of real horse course;20 students in the control group were taught in the traditional teaching mode with 40 h of real horse courses.Results:(1)in horseback physical fitness test,the average value of the control group was 101.9 s;325.6 s in the experimental group.Independent sample T test p<0.05 has significant difference,the horseback physical performance in experimental group is better than the control group.(2)in horseback physical balance test,the average value of the control group was 3.75,and the average value of the experimental group was 7.1.Independent sample T test p<0.05 has significant difference,the horseback physical balance test results in experimental group have significant difference,and the experimental group is better than the control group.(3)The interview method was used to interview the equestrian coaches who participated in the experiment,coaches think that the bionic robotic horse can speed up the learning progress and has a strong technical consolidation,especially for teaching amateurs;but for the time being,it cannot meet the training and improvement target of the actual horse control ability and the ability to grasp the route,and such experience is not real and good enough for senior students.Conclusion:using real horse and intelligent bionic robotic horse combined,one can improve the teaching effectiveness and promote students’adaptation to horseback and technical mastery.But for the time being,it is only suitable for students with weak foundation or zero foundation.The capability of intelligent bionic robotic horse needs to be strengthened,and technological innovation is needed to adapt to all kinds of students. 展开更多
关键词 Intelligent bionic machine Equestrian teaching teenagers.
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Introduction to the Special Issue on Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision 认领 引用
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作者 Ahmad Taher Azar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第7期29-36,共8页
The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems an... The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems and making decisions in many different fields.These advanced technologies have enabled significant breakthroughs in sectors including entertainment,finance,transportation,and healthcare.AI systems,which can analyze vast volumes of data,have significantly driven efficiency and innovation.With remarkable accuracy,patterns can be identified and predictions generated,improving decision-making processes and facilitating the development of more intelligent solutions.The increasing adoption of these technologies by organizations has expanded the potential for AI to change processes and improve results. 展开更多
关键词 machine learning deep learning dl analyze vast volumes datahave artificial intelligence ai machine learning ml advanced technologies solving complex problems efficiency innovationwith artificial intelligence
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Design of the intelligent feeding machine for largemouth bass on the basis of feeding intensity 认领 引用
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作者 Huang Huang Zeyu Zheng +5 位作者 Yan Shi Xiao Li Peng Wan Zhuo Chen Yunfei Guo He Huang 《International Journal of Agricultural and Biological Engineering》 SCIE 2026年第2期13-27,共15页
Conventional feeders can achieve timed and quantitative feeding,but they cannot optimize feeding strategies on the basis of actual aquaculture conditions.This study evaluated the feeding intensity of largemouth bass a... Conventional feeders can achieve timed and quantitative feeding,but they cannot optimize feeding strategies on the basis of actual aquaculture conditions.This study evaluated the feeding intensity of largemouth bass and developed an intelligent feeder to achieve efficient and precise feeding.A mobile feeding system was built by designing and simulating the structure of the data acquisition,control,feeding power,storage,and mobile modules of the feeder.The surface water pressure signals during largemouth bass feeding were collected through pressure sensors and analyzed,and the feeding intensity was classified into three levels:strong,weak,and none.Signal features were extracted to construct a dataset and input into five machine learning models for optimal parameter tuning.The precision,recall,F1 score,and average accuracy of the random forest model were 96.2%,95.5%,95.6%,and 93.4%,respectively.The YOLOv5 model was adopted to detect remaining feed on the water surface.The feeding system was designed to enable the feeder to automatically track and provide feed into the tank.Experiments were conducted on the intelligent feeding system,with the feed residue rate as the indicator of the practicality of the feeding system.Verification experiments were also performed on eight tanks,and the average feed residue rate was less than 3%,proving that the feeding system has good practicality in actual aquaculture environments. 展开更多
关键词 largemouth bass feeding intensity intelligent feeding machine machine learning mobile feeding
Enhancing flexibility and system performance in 6G and beyond: A user-based numerology and waveform approach 认领 引用
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作者 Mohamed S.Sayed Hatem M.Zakaria Abdelhady M.Abdelhady 《Digital Communications and Networks》 SCIE EI CSCD 2025年第4期974-990,共17页
A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with h... A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with high data rates,low latency,and reliability.Therefore,effective handling of INI is crucial to meet the diverse requirements of these applications.To address INI in MN-OFDM systems,this paper proposes a User-Based Numerology and Waveform(UBNW)approach that uses various OFDM-based waveforms and their parameters to mitigate INI.By assigning a specific waveform and numerology to each user,UBNW mitigates INI,optimizes service characteristics,and addresses user demands efficiently.The required Guard Bands(GB),expressed as a ratio of user bandwidth,vary significantly across different waveforms at an SIR of 25 dB.For instance,OFDM-FOFDM needs only 2.5%,while OFDM-UFMC,OFDM-WOLA,and conventional OFDM require 7.5%,24%,and 40%,respectively.The time-frequency efficiency also varies between the waveforms.FOFDM achieves 85.6%,UFMC achieves 81.6%,WOLA achieves 70.7%,and conventional OFDM achieves 66.8%.The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB. 展开更多
关键词 6G Artificial intelligence and machine learning Inter-numerology interference Mixed numerology OFDM Multiple waveforms User-based numerology and waveform
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CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach 认领 引用 被引量：24
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作者 Jihong Chen Jianzhong Yang +5 位作者 Huicheng Zhou Hua Xiang Zhihong Zhu Yesong Li Chen-Han Lee Guangda Xu 《Engineering》 SCIE EI CAS 2015年第2期247-260,共14页
Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a C... Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools. 展开更多
关键词 cyber-physical system （CPS） big data computer numerical control （CNC） machine tool electronic data of work processes instruction domain intelligent machining
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Towards autonomous and optimal excavation of shield machine:a deep reinforcement learning-based approach 认领 引用 被引量：12
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作者 Ya-kun ZHANG Guo-fang GONG +2 位作者 Hua-yong YANG Yu-xi CHEN Geng-lin CHEN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2022年第6期458-478,共21页
Autonomous excavation operation is a major trend in the development of a new generation of intelligent tunnel boring machines(TBMs).However,existing technologies are limited to supervised machine learning and static o... Autonomous excavation operation is a major trend in the development of a new generation of intelligent tunnel boring machines(TBMs).However,existing technologies are limited to supervised machine learning and static optimization,which cannot outperform human operation and deal with ever changing geological conditions and the long-term performance measure.The aim of this study is to resolve the problem of dynamic optimization of the shield excavation performance,as well as to achieve autonomous optimal excavation.In this study,a novel autonomous optimal excavation approach that integrates deep reinforcement learning and optimal control is proposed for shield machines.Based on a first-principles analysis of the machine-ground interaction dynamics of the excavation process,a deep neural network model is developed using construction field data consisting of 1.1 million samples.The multi-system coupling mechanism is revealed by establishing an overall system model.Based on the overall system analysis,the autonomous optimal excavation problem is decomposed into a multi-objective dynamic optimization problem and an optimal control problem.Subsequently,a dimensionless multi-objective comprehensive excavation performance measure is proposed.A deep reinforcement learning method is used to solve for the optimal action sequence trajectory,and optimal closed-loop feedback controllers are designed to achieve accurate execution.The performance of the proposed approach is compared to that of human operation by using the construction field data.The simulation results show that the proposed approach not only has the potential to replace human operation but also can significantly improve the comprehensive excavation performance. 展开更多
关键词 Shield machine Slurry shield Intelligent tunnel boring machine(TBM) Deep reinforcement learning Optimal control Dynamic optimization Deep learning
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Strategies and Principles of Distributed Machine Learning on Big Data 认领 引用 被引量：22
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作者 Eric P. Xing Qirong Ho +1 位作者 Dai Wei Pengtao Xie 《Engineering》 SCIE EI CAS 2016年第2期179-195,共17页
The rise of big data has led to new demands for machine learning （ML） systems to learn complex mod- els, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer p... The rise of big data has led to new demands for machine learning （ML） systems to learn complex mod- els, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics （such as high-dimensional latent features, intermediate repre- sentations, and decision functions） thereupon. In order to run ML algorithms at such scales, on a distrib- uted cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required-and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that ＂big＂ ML systems can benefit greatly from ML-rooted statistical and algo- rithmic insights-and that ML researchers should therefore not shy away from such systems design-we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solu- tions. These principles and strategies span a continuum from application, to engineering, and to theo- retical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guaran- tees. They concern four key questions that traditionally receive little attention in ML research： How can an ML program be distributed over a cluster？ How can ML computation be bridged with inter-machine communication？ How can such communication be performed？ What should be communicated between machines？ By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area that lies between ML and systems.. 展开更多
关键词 Machine learningArtificial intelligence big dataBig modelDistributed systemsPrinciplesTheoryData-parallelismModel-parallelism
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How AI-enabled SDN technologies improve the security and functionality of industrial IoT network:Architectures,enabling technologies,and opportunities 认领 引用 被引量：2
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作者 Jinfang Jiang Chuan Lin +3 位作者 Guangjie Han Adnan MAbu-Mahfouz Syed Bilal Hussain Shah Miguel Martínez-García 《Digital Communications and Networks》 SCIE CSCD 2023年第6期1351-1362,共12页
The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communi... The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks. 展开更多
关键词 Industrial internet of things(IIoT) Industry 4.0 Artificial intelligence(AI) Machine intelligence Software-defined networking(SDN)
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Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management 认领 引用 被引量：2
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作者 MOHAGHEGH Shahab D. 《Petroleum Exploration and Development》 SCIE 2020年第2期225-228,共4页
Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of e... Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models. 展开更多
关键词 and reservoir management Contribution of artificial intelligence and machine learning to reservoir engineering Subsurface analytics reservoir modeling
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基于因果模型的复杂工业过程数据驱动软传感器自动特征选择方法 认领 引用
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作者 Yan-Ning Sun 秦威 +2 位作者 Jin-Hua Hu Hong-Wei Xu Poly Z.H.Sun 《Engineering》 SCIE EI CAS CSCD 2023年第3期82-93,共12页
关键绩效指标(KPI)的软感知在复杂工业过程的决策中起着至关重要的作用。许多研究人员已经使用尖端的机器学习(ML)或深度学习(DL)模型开发出了数据驱动的软传感器。此外,特征选择是一个关键的问题,因为一个原始的工业数据集通常是高维的... 关键绩效指标(KPI)的软感知在复杂工业过程的决策中起着至关重要的作用。许多研究人员已经使用尖端的机器学习(ML)或深度学习(DL)模型开发出了数据驱动的软传感器。此外,特征选择是一个关键的问题,因为一个原始的工业数据集通常是高维的,并不是所有的特征都有利于软传感器的发展。一个完美的特征选择方法不应该依赖于超参数和后续的ML或DL模型。相反,它应该能够自动选择一个特征子集进行软传感器建模,其中每个特征对工业KPI都有独特的因果影响。因此,本研究提出了一种受因果模型启发的自动特征选择方法,用于工业KPI的软感知。首先,受后非线性因果模型的启发,本研究将该方法与信息论相结合,以量化原始工业数据集中每个特征和KPI之间的因果效应。然后,提出了一种新的特征选择方法,即自动选择具有非零因果效应的特征来构造特征的子集。最后,利用所构造的子集,通过AdaBoost集成策略开发KPI的软传感器。通过对两个实际工业应用的实验证实了该方法的有效性。在未来,该方法也可以应用于其他工业过程,以帮助开发更先进的数据驱动的软传感器。 展开更多
关键词 Big data analytics Machine intelligence Quality prediction Soft sensors Intelligent manufacturing
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