Simultaneous fault detection and robust control for a dynamic observer-based switched time delay systems with car roll dynamic application

Shokrollahi, M. R.; Mousavi, A. K.

doi:10.1080/03081079.2023.2248359

Cited by 1 publication

(4 citation statements)

References 34 publications

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“…In the ROT model (7), the interclass regularization term ensures that samples from Y are transported to samples with the same class labels in X. This enables the classification of all samples in Y based on the labels of the transported samples.…”

Section: Regularized Optimal Transport Modelmentioning

confidence: 99%

“…Since the non-convex nature of problem (7) poses a challenge, the traditional method known as majorization-minimization (MM) [35] can be utilized to tackle this issue.…”

Section: Algorithm For Rotmentioning

confidence: 99%

“…As we know, the MM algorithm can converge with a small iteration number. Thus, the following Algorithm 1 is designed to solve problem (7).…”

Section: Algorithm For Rotmentioning

confidence: 99%

“…Misclassifying a sample from the minority class as belonging to the majority class can have serious consequences or lead to significant damage in terms of water pollution control. Recently, class-imbalanced classification has garnered considerable attention in various applications, such as feature selection [3][4][5], fault diagnosis [6,7], continuous supervising tasks [1], face recognition [8], cancer detection [9], and anomalous event detection [10].…”

Section: Introductionmentioning

confidence: 99%

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Regularized Discrete Optimal Transport for Class-Imbalanced Classifications

Chen,

Wan,

2024

Mathematics

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Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness.

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Section: Regularized Optimal Transport Modelmentioning

confidence: 99%

“…Since the non-convex nature of problem (7) poses a challenge, the traditional method known as majorization-minimization (MM) [35] can be utilized to tackle this issue.…”

Section: Algorithm For Rotmentioning

confidence: 99%

“…As we know, the MM algorithm can converge with a small iteration number. Thus, the following Algorithm 1 is designed to solve problem (7).…”

Section: Algorithm For Rotmentioning

confidence: 99%