Zobrazeno 1 - 10
of 7 603
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
Autor:
Nathaniel Hanson, Immanuel Ampomah Mensah, Sonia F. Roberts, Jessica Healey, Celina Wu, Kristen L. Dorsey
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
We demonstrate proprioceptive feedback control of a one degree of freedom soft, pneumatically actuated origami robot and an assembly of two robots into a two degree of freedom system. The base unit of the robot is a 41 mm long, 3-D printed Kresling-i
Externí odkaz:
https://doaj.org/article/9cc816b769234928ac9741425bbf31c9
Publikováno v:
Advanced Intelligent Systems, Vol 6, Iss 5, Pp n/a-n/a (2024)
This article presents a novel airflow rate sensing method based on the principle of a hot‐film flow sensing device with data‐driven machine learning (ML) models. In addition, to combine the two signals of the sensor (the resistance changes of the
Externí odkaz:
https://doaj.org/article/f6efdf1f1b554798b4ad270fededf593