Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

Shi, Wei; Tang, Dunbing; Zou, Ping

doi:10.17559/tv-20210718091658

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Starting from the background of industrial internet and deep learning based rolling bearing fault diagnosis, this article explores a cloud edge collaboration [37] +adversarial network fault diagnosis method based on industrial internet platform [35][36] . On the one hand, due to the superposition of signals on the industrial site, nonlinear and unstable, it is difficult to obtain fault samples, resulting in difficulty in extracting fault features.…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

2023

Teh. vjesn.

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With the continuous improvement of product quality, production efficiency, and complexity, higher requirements are put forward for the reliability and stability of equipment, and the difficulty of real-time diagnosis of faults and functional failures is also increasing. The traditional fault diagnosis methods based on signal processing and Convolutional neural network cannot meet the requirements of on-site online real-time fault diagnosis of equipment. One is that the vibration signals on the industrial site are superimposed on each other, nonlinear and unstable and traditional feature extraction methods take a long time, resulting in unstable extraction results. Second, massive data and fault diagnosis algorithms need rich computing and storage resources. The traditional Convolutional neural network method conflicts with the real-time response requirements of fault diagnosis. At the same time, different models of fault diagnosis models have poor generalization ability, and the diagnostic accuracy is not high or even impossible to diagnose. To solve the above problems, this paper proposes a fault diagnosis method based on industrial Internet platform, which is equipment cloud edge collaboration + adaptive countermeasure network Transfer learning. On the edge side, the vibration signals collected from key components of the model are processed using empirical mode decomposition (EEMD) to solve the problem of signal nonlinearity and stationarity. In the cloud, EEMD signals of different models are decomposed into source domain and target domain for confrontation training, which is used as the input of the improved domain adversarial network model DANN (Domain Adversarial Neural Networks), so as to improve the accuracy of fault diagnosis of different models by using cloud computing power and the improved adversarial network Transfer learning algorithm. Through the analysis of experimental data, this paper verifies that the model after the confrontation network Transfer learning is more accurate than the traditional fault diagnosis method. Through the coordination of computing resources and real-time requirements, real-time diagnosis of cloud side collaborative bearing fault is realized.

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Section: Discussionmentioning

confidence: 99%

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

2023

Teh. vjesn.

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“…This enables enterprises to better manage and use resources and improve the efficiency of resource use. W. Shi et al [17] proposed an inter-firm cooperative scheduling strategy for manufacturing resources by using the mixed set programming method. J. Delaram et al [18] adopted the fuzzy VIKOR method to rate the demanders according to three indicators: quality, time and sustainability.…”

Section: A Functions and Application Areas Of The Cloud Manufacturing...mentioning

confidence: 99%

An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform

Pang,

Shi,

Dai

2024

IEEE Access

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Supply-demand matching degree evaluation is an effective way to reduce the transaction risk of manufacturing supply-demand subjects. Cloud manufacturing provides a platform for supply-demand entities through the network. Based on the characteristics of cloud manufacturing, this paper proposes an intelligent evaluation method of supply-demand matching degree based on ELECTRE III and VIKOR. Firstly, the demand side uploads a bill of material on the cloud manufacturing platform, and the supplier provides a supply plan. The demand side forms an evaluation team to evaluate the quality of the supplied product. Secondly, the weight of the evaluators is calculated in cloud manufacturing platform based on the similarity measurement method. Then, the cloud manufacturing platform carries out the weighted aggregation of evaluation information and uses the ELECTRE III method to build the weak advantage relationship among indicators. Finally, the cloud manufacturing platform performs a VIKOR compromise calculation and selects the optimal supply-demand matching degree. At the end of the paper, an example is taken to evaluate the matching degree of supply-demand of robot machined parts, to demonstrate the practicability of the new method. Based on Canny algorithm, a robot action stability test platform is built to verify the calculation results of the new method.INDEX TERMS Canny algorithm, cloud manufacturing platform, ELECTRE III, supply-demand matching degree evaluation, VIKOR.

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“…It further presents distinct kinds of tasks executed for addressing communication, computation, and storage necessities of users [1]. Cloud services mostly depend upon the usage of virtual machines ideology that splits the resources logically in a cloud, as individual machines, for alleviating the user to receive services from Pay-as-you-Go way [2,3]. It says that the less VMs or resources were utilized, the less payment for the services which took place.…”

Section: Introductionmentioning

confidence: 99%

Block Chain Technology Assisted Privacy Preserving Resource Allocation Scheme for Internet of Things Based Cloud Computing

2023

Teh. vjesn.

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Resource scheduling in cloud environments is a complex task, as it involves allocating suitable resources based on Quality of Service (QoS) requirements. Existing resource allocation policies face challenges due to resource dispersion, heterogeneity, and uncertainty. In this research, the authors propose a novel approach called Quasi-Oppositional Artificial Jellyfish Optimization Algorithm (QO-AJFOA) for resource scheduling in cloud computing (CC) environments. The QO-AJFOA model aims to optimize the allocation of computing power and bandwidth resources in servers, with the goal of maximizing long-term utility. The technique combines quasi-oppositional based learning (QOBL) with traditional AJFOA. Additionally, a blockchain-assisted Smart Contract protocol is used to distribute resource allocation, ensuring agreement on wireless channel utilization. Experimental validation of the QO-AJFOA technique demonstrates its promising performance compared to recent models, as tested with varying numbers of tasks and iterations. The proposed approach addresses the challenges of resource scheduling in cloud environments and contributes to the existing literature on resource allocation policies.

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Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

Cited by 3 publications

References 25 publications

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform

Block Chain Technology Assisted Privacy Preserving Resource Allocation Scheme for Internet of Things Based Cloud Computing

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