Zobrazeno 1 - 10
of 501
pro vyhledávání: '"kernel principal component analysis (KPCA)"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of
Externí odkaz:
https://doaj.org/article/718d45c3c79e49528d89eea7def9abdb
Publikováno v:
In Geohazard Mechanics December 2024 2(4):279-288
Publikováno v:
BioResources, Vol 19, Iss 2, Pp 3505-3519 (2024)
The extent of removal of lignin and hemicellulose are crucial indicators for evaluating the efficiency of enzymatic hydrolysis of crop straw. Numerous factors influence these two indices. Establishing a quantitative model that correlates these factor
Externí odkaz:
https://doaj.org/article/6dc04d8d9f334342875c2cddfb335ec2
Publikováno v:
电力工程技术, Vol 43, Iss 1, Pp 229-237 (2024)
In the novel power system of urban grid, the multiple resources increase and the data collection becomes more difficult, which lead to a higher random missing data rate. It is difficult to meet the demand for refined analysis and decision making. For
Externí odkaz:
https://doaj.org/article/17f7d7d45fe64a0a9385dd3dbb4b1cda
Autor:
Mohammed Tahar Habib Kaib, Abdelmalek Kouadri, Mohamed-Faouzi Harkat, Abderazak Bensmail, Majdi Mansouri
Publikováno v:
IEEE Access, Vol 12, Pp 11470-11480 (2024)
Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). Unfortunately, PCA’s reliabili
Externí odkaz:
https://doaj.org/article/4de09f34ad044addab9eecd189d8572e
Publikováno v:
Proceedings on Engineering Sciences, Vol 5, Iss S1, Pp 35-46 (2023)
The term deep learning-based framework for smart mobility refers to a concept or research article that suggests a framework for traffic pattern prediction using deep learning methods in the context of smart mobility. To improve traffic prediction ski
Externí odkaz:
https://doaj.org/article/5a9c5647df514279b7227dcb2ae679ac
Publikováno v:
Proceedings on Engineering Sciences, Vol 5, Iss S1, Pp 69-78 (2023)
The effectiveness and dependability of these vital energy infrastructures depend heavily on the early detection of anomalies in nuclear power plants (NPPs). Anomalies in a plant's operations might be signs of the impending equipment failure, a danger
Externí odkaz:
https://doaj.org/article/8378735607264ac69e9e52d7ff49615d
Publikováno v:
Proceedings on Engineering Sciences, Vol 5, Iss S1, Pp 79-88 (2023)
Mass customization is becoming the more and more of emphasis on the production optimization. In many manufacturing and service organizations, production planning and scheduling are characterized as the daily decision-making procedures. The significan
Externí odkaz:
https://doaj.org/article/238a81f2f4b640e986e0cae49d8ffa18
Autor:
HUA Xingyue, SHAO Liangshan
Publikováno v:
Meikuang Anquan, Vol 54, Iss 2, Pp 195-200 (2023)
In order to improve the accuracy of mine water inrush source identification, a KPCA-GWO-SVM-based mine water inrush source identification model is proposed. The algorithm uses kernel principal component analysis(KPCA) for feature dimension reduction
Externí odkaz:
https://doaj.org/article/5499f1efa14146938c72e89f353be85c
Autor:
WANG Yan, LYU Yanping
Publikováno v:
Jisuanji kexue yu tansuo, Vol 17, Iss 2, Pp 385-395 (2023)
Convolutional neural networks (CNN) in deep learning can make full use of the computing power of computers to efficiently extract the features of remote sensing images. This has achieved good results, especially in the classification of hyperspectral
Externí odkaz:
https://doaj.org/article/3315c5d877204b55baad94584806644a