Research on the Security of Computer Platforms HMM-based Fault Diagnosis

Wei-gang, MA; Yuan, Cangzhou; Wei, Wei; Wei, Lu; Ma, Jianfeng; Xinhong, Han

doi:10.3923/itj.2013.8686.8695

Cited by 5 publications

(3 citation statements)

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“…In this regard, Schlüter and Hetterscheid ( 2017 ) maintained that the digitalization situations of different SC practices require to be accelerated; as a result, they proposed an application-oriented framework for the extraction of the significant tools in the area of Industry 4.0 mapped to different SC practices. Several tools have been offered to unfold the Industry 4.0 umbrella: big data and analytics(Cai et al 2019 ; Zhang et al 2018 ), autonomous robots, simulation (Buzys et al 2018 ; Wei et al 2017 ), IoT (Han et al 2020 ; Lin et al 2015 ), cybersecurity (Wei-Gang et al 2013 ; Wu et al 2013 ), cloud computing (Chen et al 2016a , b ), additive manufacturing and augmented reality (Shi et al 2016a , b ), Radio Frequency Identification (RFID) (Li et al 2018 ; Wang et al 2018 ) and real-time location system (Wang et al 2020 ; Yang et al 2020 ). Likewise, Oks et al ( 2017 ) introduced an application map in order to distinguish different opportunity areas to apply the industrial cyber-physical systems of Industry 4.0.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: Evaluate the challenges of sustainable supply chain 4.0 implementation under the circular economy concept using new decision making approach

Xin

Lang

Mishra³

2022

Oper Manag Res

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Industry 4.0 has the potential of growing industrialization and, on the other hand, disrupting the sustainability of prevailing manufacturing supply chains through inducing great challenges such as higher resource consumption that, in turn, results in global warming and climate change. As a result, researchers working in the area of sustainable supply chain 4.0 need to make deep evaluations on the challenges arising for manufacturing supply chains contemplating the improvement of their sustainability levels and having a digital transformation toward Industry 4.0. To fill this gap, the current paper designs an innovative framework on the basis of the Stepwise Weight Assessment Ratio Analysis (SWARA) technique and the Complex Proportional Assessment (COPRAS) approach to evaluate the challenges that may arise for supply chain 4.0 in the q-Rung Orthopair Fuzzy Sets (q-ROFSs) setting. The proposed method uses an extended SWARA process to determine the criteria importance degrees considering the experts’ preferences. The performance of the proposed method was assessed by conducting an empirical case study under the q-ROFSs condition. Further, a sensitivity analysis was executed to check whether the proposed method is stable enough to be relied on parameter values. Finally, the results obtained were compared to those of currently used methods to verify the obtained results’ reliability. As revealed by the comparative results, the framework proposed in this article was of higher consistency and strength compared to other prevailing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: Evaluate the challenges of sustainable supply chain 4.0 implementation under the circular economy concept using new decision making approach

Xin

Lang

Mishra³

2022

Oper Manag Res

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“…The main content of industry 4.0 includes the three aspects: firstly, the physical information system should be possessed in the process of realizing industry 4.0, which can play the role of technical support and help the integration of physical and network systems. Several technologies have been proposed to unfold the Industry 4.0 umbrella: big data and analytics (Cai et al , 2019; Zhang et al , 2018), autonomous robots, simulation (Buzys et al , 2018; Wei et al , 2017), Internet of Things (IoT) (Han et al , 2020; Lin et al , 2015), cybersecurity (Wei-Gang et al , 2013; Wu et al , 2013), cloud computing (Chen et al ., 2016a, b), additive manufacturing and augmented reality (Shi et al , 2016), Radio Frequency Identification (RFID) (Li et al , 2018; Wang et al , 2018) and real-time location system (Wang et al , 2020; Yang et al , 2020) technologies. Secondly, intelligent production refers to manufacturing with the help of new intelligent manufacturing technology, similar to 3D printing, human–computer interaction, etc.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Industry 4.0: technology spillover impact on digital manufacturing industry

Miao

2021

JEIM

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PurposeUnder the guidance of the concept of openness and development, the paper grasps the mechanism of technology spillover in developed countries and analyzes how to better absorb advanced manufacturing technology based on empirical analysis so as to point out the path for the transformation and development of China’s digital manufacturing industry.Design/methodology/approachThe paper constructs the panel data model and further analyzes the impact of international technology spillovers on the transformation and development of the digital manufacturing industry.FindingsThis paper measures the level of technology spillover in the Yangtze River Delta region and finds that foreign direct investment (FDI) technology spillover and import trade technology spillover among four provinces and cities show a growth trend from 2010 to 2017. But after 2017, there is a certain degree of decline.Originality/valueWith the advent of industry 4.0, the digital manufacturing industry of all countries in the world is developing with a new attitude, the global technology spillover methods are diverse and the spillover channels have changed greatly, which will affect the transformation and upgrading of China's digital manufacturing industry.

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“…So, in this paper, the system is as the object, and study its fault prediction method. Hidden Markov Model (HMM) is a probability-based method of stochastic data modeling and analysis, which is successfully applied to the fault diagnosis [7][8][9]. Through the statistical learning, the relationship between monitoring information and fault status is studied, which can reflect to the potential relationship characteristics of equipment.…”

Section: Introductionmentioning

confidence: 99%

EMU Fault Prognosis Based on Improved HSMM

Zhang¹,

Li²,

Sun³

2018

dtcse

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Abstract. Due to the limitations of fault diagnosis and forecast using traditional Hidden semi Markov model (HSMM), a model based on fissile PSO is proposed to optimize HSMM parameters. It can avoid local optimum effectively and expand the scope of the search without any impact on training rate. Finally, a more accurate HSMM model verified by experiments is established. The healthy state classifier and lifetime prediction model are established. It shows that the algorithm has high prediction accuracy and can be applied to the fault prediction of complex equipment.

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Research on the Security of Computer Platforms HMM-based Fault Diagnosis

Cited by 5 publications

References 0 publications

RETRACTED ARTICLE: Evaluate the challenges of sustainable supply chain 4.0 implementation under the circular economy concept using new decision making approach

RETRACTED ARTICLE: Evaluate the challenges of sustainable supply chain 4.0 implementation under the circular economy concept using new decision making approach

Industry 4.0: technology spillover impact on digital manufacturing industry

EMU Fault Prognosis Based on Improved HSMM

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