Rebooting data-driven soft-sensors in process industries: A review of kernel methods
Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process
industries, thus allowing for less fault occurrence and better control performance. However …
industries, thus allowing for less fault occurrence and better control performance. However …
Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes
Fault detection and diagnosis is crucial in recent industry sector to ensure safety and
reliability, and improve the overall equipment efficiency. Moreover, fault detection and …
reliability, and improve the overall equipment efficiency. Moreover, fault detection and …
SMKFC-ER: Semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy
The present study aimed to present a new algorithm called Semi-supervised Multiple Kernel
Fuzzy Clustering based on Entropy and Relative entropy (SMKFC-ER) by focusing on …
Fuzzy Clustering based on Entropy and Relative entropy (SMKFC-ER) by focusing on …
A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis
Although satellite images can provide more information about the earth's surface in a
relatively short time and over a large scale, they are affected by observation conditions and …
relatively short time and over a large scale, they are affected by observation conditions and …
A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China
Accurate prediction of water surface evaporation (Ep) is important in the fields of both
hydrology and irrigation engineering. This study evaluated the potential ability of a new …
hydrology and irrigation engineering. This study evaluated the potential ability of a new …
Adaptive safety-aware semi-supervised clustering
Recently, safe semi-supervised clustering (S3C) has become an emerging topic in machine
learning field. S3C aims to reduce the performance degradation probability of wrong prior …
learning field. S3C aims to reduce the performance degradation probability of wrong prior …
Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm
J Xu, G Feng, T Zhao, X Sun, M Zhu - Computers & geosciences, 2019 - Elsevier
Because of the uncertainty in remote sensing images and the ill-posedness of the problem, it
is difficult for traditional unsupervised classification algorithms to create an accurate …
is difficult for traditional unsupervised classification algorithms to create an accurate …
Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data
H Yu, X Xu, H Li, Y Wu, B Lei - Knowledge-Based Systems, 2024 - Elsevier
The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-
means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of …
means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of …
A clustering-based energy consumption evaluation method for process industries with multiple energy consumption patterns
L Sun, Y Ji, Z Sun, Q Li, Y Jin - International Journal of Computer …, 2023 - Taylor & Francis
The production systems in process industries are confirmed to be tremendously energy-
consuming, and the trust in promoting their energy efficiency has become a concern, with its …
consuming, and the trust in promoting their energy efficiency has become a concern, with its …
Multi-kernel support vector data description with boundary information
Abstract The One-Class Classification (OCC) exists in many real-world applications, such as
novelty detection, outlier detection, facial verification, and anomaly detection. SVDD is an …
novelty detection, outlier detection, facial verification, and anomaly detection. SVDD is an …