Zobrazeno 1 - 10
of 7 630
pro vyhledávání: '"soft-sensor"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2
Externí odkaz:
https://doaj.org/article/eaedfeeaaf7142618e1708e83e83b502
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate
Externí odkaz:
https://doaj.org/article/c36856a6013c4d6596fcf5dd8a1fa046
Autor:
Gabryel M. Raposo de Alencar, Fernanda M. Lima Fernandes, Rafael Moura Duarte, Petrônio Ferreira de Melo, Altamar Alencar Cardoso, Heber Pimentel Gomes, Juan M. Mauricio Villanueva
Publikováno v:
Automation, Vol 5, Iss 2, Pp 106-127 (2024)
The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sens
Externí odkaz:
https://doaj.org/article/cb5da352da964c998cbb8fe78fb04eba
Autor:
JIANG Dongnian, WANG Renjie
Publikováno v:
Xibei Gongye Daxue Xuebao, Vol 42, Iss 2, Pp 344-352 (2024)
To solve the problem of low precision in soft sensor models caused by sensor data loss in industrial processes, a new method of sensor data generation based on generative adversarial nets (GAN) is proposed. Firstly, the missing area of sensor data is
Externí odkaz:
https://doaj.org/article/c18e06c9752742b49649d24ad3db69ef
Autor:
Afrânio Melo, Tiago S.M. Lemos, Rafael M. Soares, Deris Spina, Nayher Clavijo, Luiz Felipe de O. Campos, Maurício Melo Câmara, Thiago Feital, Thiago K. Anzai, Pedro H. Thompson, Fábio C. Diehl, José Carlos Pinto
Publikováno v:
Digital Chemical Engineering, Vol 13, Iss , Pp 100182- (2024)
This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, and process condition monitoring. Key features include regression and reconstruction models, preprocessing pip
Externí odkaz:
https://doaj.org/article/390bdfd826fa469781abab3bff6de4c0
Publikováno v:
Results in Chemistry, Vol 9, Iss , Pp 101677- (2024)
In the film manufacturing process, process variables, such as temperature and pressure, are measured and controlled to manage the film properties, such as thickness and optical characteristics. Each film property is regulated by the product specifica
Externí odkaz:
https://doaj.org/article/1701542d9aed4e1286d285c61c279e67
Publikováno v:
High Temperature Materials and Processes, Vol 43, Iss 1, Pp pp. 51-58 (2024)
Endpoint control stands as a pivotal determinant of steel quality. However, the data derived from the BOF steelmaking process are characterized by high dimension, with intricate nonlinear relationships between variables and diverse working conditions
Externí odkaz:
https://doaj.org/article/5d24d6f9108749eab9bcb81b2352007c
Publikováno v:
Heliyon, Vol 10, Iss 12, Pp e32901- (2024)
A new method is required to address the challenge of predicting process parameters in high-temperature, high-pressure industrial processes. This study proposes a multi-model Long Short-Term Memory (LSTM) network prediction algorithm with irregular ti
Externí odkaz:
https://doaj.org/article/8febdf2c559942e2b5aed0e47d67b207
Publikováno v:
IEEE Access, Vol 12, Pp 80633-80645 (2024)
For deep learning based soft sensors, the spatiotemporal attention (STA)-LSTM is a newly emerged technique which provides efficient predictions for quality variables of industrial processes. However, the STA-LSTM methods calls for an enormous network
Externí odkaz:
https://doaj.org/article/a8e629f0c8444568adf3d6051dc5c57c
Autor:
Wenyi Liu, Takehisa Yairi
Publikováno v:
IEEE Access, Vol 12, Pp 5920-5932 (2024)
State-space formulations offer a flexible approach for developing soft sensors in industrial processes, leveraging both data information and domain knowledge of process dynamics. On one hand, the state vector introduces varying perspectives in modeli
Externí odkaz:
https://doaj.org/article/f4e2827365a348e5b434fefe99961d68