Batch sizing control of a flow shop based on the entropy-function theorems

Zhang, Zhifeng; David, Janet; Li, Jun

doi:10.1016/j.eswa.2022.118958

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Collectively, the entirety of the data preparation process is engineered to ensure the dataset magnitude that could strain the device's Random Access Memory (RAM) capacity [22]. The "max_length" parameter, defining the model's proficiency in processing questions and answers based on a stipulated number of tokens, is judiciously set to 500.…”

Section: Data Splittingmentioning

confidence: 99%

Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

Dartiko,

Yusa,

Erlansari

et al. 2024

intensif

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The campus introduction process is a stage where new students acquire information about the campus through a series of activities and interactions with existing students. However, the delivery of campus introduction information is still limited to conventional methods, such as using guidebooks. This limitation can result in students having a limited understanding of the information needed during their academic period. The one of solution for this case is to implement a deep learning system with knowledge-based foundations. This research aims to develop a Question Answering System (QAS) as a campus introduction guide by comparing two transformer methods, namely the RoBERTa and IndoBERT architectures. The dataset used is processed in the SQuAD format in the Indonesian language. The collected SQuAD dataset in the Indonesian language consists of 5046 annotated data. The result shows that IndoBERT outperforms RoBERTa with EM and F1-Score values of 81.17 and 91.32, respectively, surpassing RoBERTa with EM and F1-Score values of 79.53 and 90.18.

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Section: Data Splittingmentioning

confidence: 99%

Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

Dartiko,

Yusa,

Erlansari

et al. 2024

intensif

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show abstract

“…This helps identify abnormal behavior paths of components in the equipment, thereby optimizing fault diagnosis strategies. For modeling of interaction behavior, CPN tools can construct Petri nets to represent concurrent and synchronized interaction behaviors between multiple machines on a production line and detects abnormal states during the interaction 92 . Mworks 93 can develop steady-state, dynamic, and faulty behavior models during O&M, and integrate them into a reliable DT model.…”

Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

Digital twin-driven prognostics and health management for industrial assets

Xiao,

Zhong,

Bao

et al. 2024

Sci Rep

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As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

show abstract

Batch sizing control of a flow shop based on the entropy-function theorems

Cited by 2 publications

References 21 publications

Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

Digital twin-driven prognostics and health management for industrial assets

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