Clustering Centroid Selection using a K-means and Rapid Density Peak Search Fusion Algorithm

2020 ◽  
Author(s):  
Chenyang Zhang ◽  
Jiamei Wang ◽  
Xinyun Li ◽  
Fei Fu ◽  
Weiquan Wang
Keyword(s):  
Fusion Algorithm ◽  
Density Peak ◽  
Peak Search

scholarly journals Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meng Li ◽  
Yanxue Wang ◽  
Chuyuan Wei
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Computational Time ◽  
Intelligent Fault Diagnosis ◽  
Computational Accuracy ◽  
Density Peak ◽  
Density Peaks ◽  
Peak Search

Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.

Data stream clustering by fast density-peak-search

2018 ◽  
Vol 11 (1) ◽  
pp. 183-189
Author(s):  
Jinxia Su ◽  
Yanwen Li ◽  
Xuejing Zhao
Keyword(s):  
Data Stream ◽  
Density Peak ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Peak Search

scholarly journals An infrared dim target detection algorithm based on density peak search and region consistency

2021 ◽  
Vol 53 (7) ◽  
Author(s):  
Baohua Zhang ◽  
Jinhui Zhu ◽  
Xiaoqi Lu ◽  
Yu Gu ◽  
Jianjun Li ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Density Peak ◽  
Peak Search

scholarly journals An Infrared Dim Target Detection Algorithm based on Density Peak Search and Region Consistency

2021 ◽  
Author(s):  
Baohua Zhang ◽  
Jinhui Zhu ◽  
XIAOQI Lu ◽  
Yu Gu ◽  
Jianjun Li ◽  
...  
Keyword(s):  
Target Detection ◽  
Search Algorithm ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Density Peak ◽  
Regional Stability ◽  
Target Region ◽  
Candidate Target ◽  
Background Clutter ◽  
Peak Search

Abstract To suppress background clutter and improve detection accuracy, this paper propose a dim target detection algorithm based on density peak search and region consistency. Firstly the density peak search algorithm is used to extract the candidate targets. And then the candidate targets are classified and marked according to the local mosaic probability factor, which is important to suppress the background clutter and accurately strip the candidate target region from the background. Considering the regional stability of the dim targets, local mosaic gradient factors are used to screen real targets from the candidate targets, and then facet kernel filter is used to extract the irregular contours of the dim targets, and as a result, the targets can be enhanced. The experimental results show that compared with the existing algorithms, the proposed method has better detection accuracy and stronger robustness in different complex scenarios.

High-Resolution Backside GMR Magnetic Current Imaging on a Contour-Milled Globally Ultrathin Die

2014 ◽  
Author(s):  
D. Vallett ◽  
J. Gaudestad ◽  
C. Richardson
Keyword(s):  
Quantum Interference ◽  
Flip Chip ◽  
Milling Process ◽  
Dramatic Improvement ◽  
Superconducting Quantum Interference Device ◽  
Density Peak ◽  
Magnetic Current ◽  
Gmr Sensors ◽  
Silicon Thickness ◽  
A Current

Abstract Magnetic current imaging (MCI) using superconducting quantum interference device (SQUID) and giant-magnetoresistive (GMR) sensors is an effective method for localizing defects and current paths [1]. The spatial resolution (and sensitivity) of MCI is improved significantly when the sensor is as close as possible to the current paths and associated magnetic fields of interest. This is accomplished in part by nondestructive removal of any intervening passive layers (e.g. silicon) in the sample. This paper will present a die backside contour-milling process resulting in an edge-to-edge remaining silicon thickness (RST) of < 5 microns, followed by a backside GMR-based MCI measurement performed directly on the ultra-thin silicon surface. The dramatic improvement in resolving current paths in an ESD protect circuit is shown as is nanometer scale resolution of a current density peak due to a power supply shortcircuit defect at the edge of a flip-chip packaged die.

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