Can industrial intelligence promote industrial transformation? --Case of mining enterprises

Shao, Anlin

doi:10.15302/j-fem-2017108

Front. Eng

2017

DOI: 10.15302/j-fem-2017108

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Can industrial intelligence promote industrial transformation? --Case of mining enterprises

Anlin Shao¹

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Cited by 3 publications

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“…Using BPR and integrated lean thinking, this paper presented the process and principle for EISs. Using this technique, more conventional manufacturers can redesign their EISs to satisfy SMS standards [16]. In order to improve its production efficiency, competitiveness, and innovation ability.…”

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Big Data Analysis and Deep Learning Optimization in Artificial Intelligence Production of Information Enterprises

Gao,

2024

SCPE

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Intelligent manufacturing technology is required to upgrade existing enterprises' management and production operations. To construct a ground breaking fusion structure, this project unites the theoretical underpinnings, technical breakthroughs, and applications of data analytics, optimisation, and intelligent production engineering. It is driven by China's desire of cutting-edge commodities and efficient growth methods. This research establishes the broad framework for merging optimisation and data analytics. There is a list of data analytics and system optimisation technologies that can address important challenges with intelligent manufacturing. By integrating data analytics and optimisation, businesses may better forecasting and management of new terrain, as well as reveal hidden information to increase decision-making efficacy.

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confidence: 99%

Big Data Analysis and Deep Learning Optimization in Artificial Intelligence Production of Information Enterprises

Gao,

2024

SCPE

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Data analytics and optimization for smart industry

2020

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Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem.To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models.

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Research on the impact of industrial intelligence on FDI: empirical evidence from China

He,

Chen,

2024

Applied Economics Letters

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Can industrial intelligence promote industrial transformation? --Case of mining enterprises

Cited by 3 publications

References 7 publications

Big Data Analysis and Deep Learning Optimization in Artificial Intelligence Production of Information Enterprises

Big Data Analysis and Deep Learning Optimization in Artificial Intelligence Production of Information Enterprises

Data analytics and optimization for smart industry

Research on the impact of industrial intelligence on FDI: empirical evidence from China

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