The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massiv...The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.展开更多
As one of the most important methods for machining process with high accuracy,ultra-precision grinding is widely used in fields such as aerospace,automotive and mold,etc.Simultaneously,it is common that wheel and spin...As one of the most important methods for machining process with high accuracy,ultra-precision grinding is widely used in fields such as aerospace,automotive and mold,etc.Simultaneously,it is common that wheel and spindle axis do not coincide with each other due to wheel settings,machining errors and so on.This could result in the generation of wheel runout,which may reduce the machining surface's quality.In this paper,combining this phenomenon,an analytic algorithm method for the multi-axis grinding process is introduced according to the envelope theory.After that,the accuracy of this method is verified.Two experiments are carried out on a 5-axis machining center.The artificial runout is set up and calculated utilizing the least square method.Finally,using the presented method,two examples with and without runout are introduced to illustrate the validation of the proposed model.The error due to the runout effect is also analyzed.展开更多
文摘The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.
基金supported by the National Natural Science Foundation of China(No.51605147)National Science Fund for Distinguished Young Scholars of Henan Polytechnic University(J2019-2)Young Backbone Project of Henan Polytechnic University(No.2018XQG-05)。
文摘As one of the most important methods for machining process with high accuracy,ultra-precision grinding is widely used in fields such as aerospace,automotive and mold,etc.Simultaneously,it is common that wheel and spindle axis do not coincide with each other due to wheel settings,machining errors and so on.This could result in the generation of wheel runout,which may reduce the machining surface's quality.In this paper,combining this phenomenon,an analytic algorithm method for the multi-axis grinding process is introduced according to the envelope theory.After that,the accuracy of this method is verified.Two experiments are carried out on a 5-axis machining center.The artificial runout is set up and calculated utilizing the least square method.Finally,using the presented method,two examples with and without runout are introduced to illustrate the validation of the proposed model.The error due to the runout effect is also analyzed.
