Trust-Tech Source-Point Method for Systematically Computing Multiple Local Optimal Solutions: Theory and Method

Zeng, Lin; Chiang, Hsiao‐Dong; Liang, Dong; Xia, Mingchao; Dong, Ning

doi:10.1109/tcyb.2021.3071462

Cited by 2 publications

(3 citation statements)

References 43 publications

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“…(i) Apply a hierarchical scheme to provide higher-quality initial points for the K-means algorithm to local optimal solutions; the resulting scheme is termed the hierarchical Kmeans algorithm (H-K algorithm) [31];…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

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Hierarchical Trust-Tech-Enhanced K-Means Methods and Their Applications to Power Grids

Chiang

et al. 2022

IEEE Open J. Power Energy

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K-means has been widely used in solving a wide range of clustering problems arising in engineering and industrial applications, but it still suffers from several issues. To address these issues, a hierarchical K-means method enhanced by Trust-Tech (H-KTT) is presented in this paper. The proposed H-KTT method is composed of two stages. The first stage of H-KTT is a hierarchical K-means (H-K-means) method for enhancing K-means with better initial points. Second, the H-Kmeans method is further enhanced to find multiple high-quality clustering results by the Trust-Tech methodology. The H-KTT method was evaluated on several test datasets including the clustering of Automatic Meter Reading (AMR), popular in power grids, with promising results. In particular, the evaluation results indicate that the proposed H-KTT method can significantly improve both the quality and stability of the clustering results by the K-means method. Furthermore, while the K-means gives stochastic clustering results, the proposed H-KTT method usually gives deterministic clustering results.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

“…To resolve these difficulties, we next present a Trust-Tech source-point method [31]. Theorem 4 below establishes the existence of source points on the quasi-stability boundary and the relationship between a source point and the quasi-stability neighboring regions.…”

Section: Multiple Clustering Solutions By Trust-tech Methodsmentioning

confidence: 99%

Hierarchical Trust-Tech-Enhanced K-Means Methods and Their Applications to Power Grids

Chiang

et al. 2022

IEEE Open J. Power Energy

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“…The first is to set the appropriate initial conditions according to the actual situation of different problems, c so as to better find the local optimal solution. The second is to optimise each local solution and obtain a locally optimal solution by iteration of the greedy algorithm [10]. The final step is to determine the termination conditions of the algorithm.…”

Section: Analysis Of Model Ideasmentioning

confidence: 99%

UAV Formation Adjustment Strategy Based on Greedy Criterion

Wang

Zhang

Yang

et al. 2023

J. Phys.: Conf. Ser.

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In order to ensure that the UAV cluster remains electromagnetically silent during the attempted formation flight, so that the emission of electromagnetic wave signalsj is minimised, a purely azimuthal passive positioning method can be used to adjust the position of the UAVs. As it is difficult to keep the UAVs in formation flying on the same horizontal plane for a long time and the initial position information of the UAVs is not known. Therefore, this paper uses the sine and cosine theorems of in-plane triangles to connect slightly deviating UAVs to a determined UAV position, and in combination with the characteristics of the model, constructs a greedy criterion based on the minimum number of UAVs required to transmit the signal, and in this way gives an adjustment scheme for the UAV position.

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Trust-Tech Source-Point Method for Systematically Computing Multiple Local Optimal Solutions: Theory and Method

Cited by 2 publications

References 43 publications

Hierarchical Trust-Tech-Enhanced K-Means Methods and Their Applications to Power Grids

Hierarchical Trust-Tech-Enhanced K-Means Methods and Their Applications to Power Grids

UAV Formation Adjustment Strategy Based on Greedy Criterion

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