Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

Sinaga, Kristina Pestaria; Hussain, Ishtiaq; Yang, Miin-Shen

doi:10.1109/access.2021.3077622

Cited by 33 publications

(19 citation statements)

References 37 publications

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“…After recognizing people, if there are multiple people, the center point of those people must be calculated, and the angle and depth of those points must be calculated from the stereo camera. Therefore, the position of the center is calculated using the K-means algorithm [2], [52], [53]. The procedure of this algorithm with K clusters is as follows.…”

Section: Human Detection Algorithmmentioning

confidence: 99%

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

Rusydi

Novira

Nakagome

et al. 2022

Journal of Robotics and Control

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In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy.

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Section: Human Detection Algorithmmentioning

confidence: 99%

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

Rusydi

Novira

Nakagome

et al. 2022

Journal of Robotics and Control

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“…In standard procedure mode, SCE is used to insure that each cluster head does not have even a maximum set number of components, it's as follows. [19]:…”

Section: Ga For Wsn Optimizationmentioning

confidence: 99%

K-Means clustering of optimized wireless network sensor using genetic algorithm

Kadim¹,

Al-Mukhtar²,

Hasan³

et al. 2022

PEN

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Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby.

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“…Clustering adalah alat yang ampuh dalam analisis data. Digunakan untuk menemukan struktur cluster dalam kumpulan data dengan kesamaan terbesar dalam cluster yang sama [4].…”

Section: Pendahuluanunclassified

“…Jurnal Sistim Informasi dan Teknologi − Vol. 4 Gambar 4 merupakan pemetaan hasil cluster BTS di Kabupaten Kerinci. Warna orange merupakan L2 atau cluster Jarak Dekat, warna hijau merupakan L1 atau cluster Jarak Menengah dan warna biru merupakan cluster L3 Jarak Jauh.…”

Section: Kesimpulanunclassified

Metode K-Means dalam Visualisasi Berbasis Google Map terhadap Klasterisasi Koordinat BTS (Base Transceiver Station)

Oktiviana¹

2022

jsisfotek

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The supervision of the Telecommunication Tower or Base Transceiver Station (BTS) in Kerinci Regency is carried out by the Kerinci Regency Communication and Information Office. Supervision is carried out by monitoring and monitoring all BTS locations in Kerinci Regency. The problem faced by the Supervisory Team is the grouping of BTS based on the proximity of the distance between BTS based on the number of supervisory teams. This study aims to make it easier for the BTS Supervisory Team to carry out supervision by clustering coordinate points based on the proximity of the distance between BTS based on the number of supervisory teams with visualization based on google map. BTS coordinate data is used as a reference point for grouping. The data used in this study is the BTS coordinate point data in 2021, sourced from the E-Government Service Division of the Communication and Information Office of Kerinci Regency. The dataset consists of 78 BTS coordinate points. Data processing in this study uses the clustering method using the K-Means algorithm. The results in this study obtained 3 BTS Clusters, namely Cluster 1 (L2) Near Distance (JD), Cluster 2 (L1) Medium Distance (JM), Cluster 3 (L3) Long Distance (JJ). The results of visualization of the BTS cluster based on google map provide convenience for the BTS supervisory team in conducting surveillance based on the cluster

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Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

Cited by 33 publications

References 37 publications

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

K-Means clustering of optimized wireless network sensor using genetic algorithm

Metode K-Means dalam Visualisasi Berbasis Google Map terhadap Klasterisasi Koordinat BTS (Base Transceiver Station)

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