Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data

Rodrı́guez, Sara; Carvalho, Francisco de A. T. de

doi:10.1007/978-3-030-01418-6_68

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The Euclidean clustering algorithm is simple and efficient, with powerful point cloud segmentation ability and good real-time performance. It has high reliability in normal scenarios and does not require huge data support [46]. This method performs clustering by computing and comparing the Euclidean distance between each point and its neighbors.…”

Section: Obstacle Detectionmentioning

confidence: 99%

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

et al. 2023

Electronics

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To prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the railway scene. The method first preprocesses the point cloud of the railway scenario collected by LiDAR to divide a basic area containing the rails. Then, the method divides the roadbed plane and fits the rails with the random sample consensus (RANSAC) algorithm, dividing the detection area according to the position of the rails. To address the issue of over or under-segmentation in the traditional Euclidean clustering method, which is due to sparser point clouds the farther the object is from the LiDAR, this paper improves the conventional Euclidean clustering. It introduces an adaptive distance threshold to categorize obstacles. Finally, compared with traditional Euclidean clustering, K-means clustering, and density-based spatial clustering of applications with noise (DBSCAN) clustering, the improved Euclidean cluster has achieved better results in terms of computing time and segmentation accuracy. Experimental results show the ability of the method to detect railway obstacles successfully.

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Section: Obstacle Detectionmentioning

confidence: 99%

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

et al. 2023

Electronics

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“…Even multiple 32-line 3D lidars are needed during the experiment, the cost is too high. The Euclidean clustering algorithm is a simple and efficient algorithm, which has strong point cloud segmentation capability and does not require data volume [11]. Although a fixed distance threshold can cause problems when dealing with objects at different distances, but this defect can be improved.…”

Section: Introductionmentioning

confidence: 99%

Research on 3D Point Cloud De-Distortion Algorithm and Its Application on Euclidean Clustering

Long

Gao

2019

IEEE Access

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The 3D point cloud data collected by 3D lidars have become a significant resource for autonomous vehicles to acquire road information. But these data tend to be inaccurate due to the turning or moving of autonomous vehicles while the lidar is working. Moreover, the traditional Euclidean clustering algorithm often causes false detection in the vicinity or missed detection in the distance if the Euclidean distance threshold is not selected properly. This paper proposes a method which contains three main steps to solve these problems: First, a de-distortion algorithm is applied to diminish the influence of distortion caused by the moving and turning of lidar; second, optimize the structure of the Euclidean clustering algorithm to make it run faster; and third, we applied an adaptive threshold of the Euclidean distance in the improved algorithm, so the improved algorithm is able to detect the relatively small objects in the distance while it can also detect the objects nearby without misjudgment. These features are all confirmed by the results of vehicle experiments which shows that with the help of proposed method, the distortion of point cloud reduced, the detection distance has increased by nearly 8 meters, and the clustering speed has also increased by 15%.INDEX TERMS Autonomous vehicle, 3D-lidar, point cloud, Euclidean clustering, object detection.

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“…Assuming that all components have the same performance exactly, the emphasis of weight distribution falls on the distance between corresponding elements of the time series. Different from the weighted methods based on various distance [9]- [13], the method we discussed does not rely on the approximation degree among sequences, but takes the overall overlap degree as the basis of weight distribution after judging whether the human body information reflected by the corresponding subsequences is overlaped. Meanwhile, in the judging process between the two subsequences, in order to further improve the accuracy of the results, we take the trend factor of human body state into account.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

et al. 2019

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In the Body Sensor Networks (BSNs), a comprehensive and intuitive conclusion can be obtained by analyzing the physiological state in the mode of multi-heterogeneous source fusion. However, if a node in the sensing layer fails, the input-oriented analysis system is no longer reliable. In this paper, we propose a homogenous sensor weight calculation method for a single sensing node in the BSNs. Under the premise of valid sensing, a set of sequences will be randomly selected as a referee to make the rules of weight allocation. Weak competitiveness means that sensors have a high degree of antagonism, which indicates that the measuring results have errors or there may be abnormal sensors in the sensing module. Meanwhile, we introduce the trend factor of human body state to strengthen the connection between adjacent data in time series, so as to further improve the reliability of trust weight. The proposed algorithm is helpful for sensing nodes in BSNs to obtain more reliable original physiological data and ensure the accuracy of subsequent feature analysis and system decision. Specifically, it can (1) improve the robustness of sensing in BSNs applications, (2) suppress the interference of abnormal signals, (3) solve data conflict, and (4) be universal to the sensors in BSNs devices. The ECG simulations verify the validity of the algorithm in all extreme cases of valid sensing, and the PPG experiments show that the proposed algorithm is applicable to different types of biosensors. Finally, the evaluation and comparison with other four known data-level fusion methods show that the proposed algorithm has better fusion performance. INDEX TERMS Body sensor networks, physiological parameter, information conflict, information fusion.

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Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data

Cited by 4 publications

References 12 publications

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

Research on 3D Point Cloud De-Distortion Algorithm and Its Application on Euclidean Clustering

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

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