Density Normalization in Density Peak Based Clustering

2017 ◽  
pp. 187-196 ◽  
Author(s):  
Jian Hou ◽  
Hongxia Cui
Keyword(s):  
Density Peak

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.

A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm

2021 ◽  
Vol 167 ◽  
pp. 75-84
Author(s):  
Mingyang Li ◽  
Xinhua Bi ◽  
Limin Wang ◽  
Xuming Han
Keyword(s):  
Two Stage ◽  
Density Peak

scholarly journals MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14619-14629
Author(s):  
Wei Yang ◽  
Linlin Zhuo ◽  
Jing Zeng ◽  
Yijun Zhao ◽  
Qiaojie Jiang ◽  
...  
Keyword(s):  
Low Rank ◽  
Density Peak

scholarly journals A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Shudong Wang ◽  
Yigang He ◽  
Baiqiang Yin ◽  
Wenbo Zeng ◽  
Ying Deng ◽  
...  
Keyword(s):  
Partial Discharge ◽  
Density Peak ◽  
Localization Method ◽  
Linear Conversion ◽  
Density Peak Clustering

Research on intelligent clustering of male upper body

2021 ◽  
pp. 004051752110001
Author(s):  
Pengpeng Cheng ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Jianping Wang ◽  
Daoling Chen
Keyword(s):  
Artificial Bee Colony ◽  
Young Men ◽  
Cuckoo Search ◽  
The Body ◽  
Upper Body ◽  
Intelligent Algorithm ◽  
Density Peak ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Length Width

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.

scholarly journals Intradialytic blood pressure pattern recognition based on density peak clustering

2018 ◽  
Vol 83 ◽  
pp. 33-39 ◽  
Author(s):  
Feng Wang ◽  
Jing-yi Zhou ◽  
Yu Tian ◽  
Yu Wang ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Pattern Recognition ◽  
Density Peak ◽  
Pressure Pattern ◽  
Density Peak Clustering

Information-Assisted Density Peak Index for Hyperspectral Band Selection

2017 ◽  
Vol 14 (10) ◽  
pp. 1870-1874 ◽  
Author(s):  
Xiaoyan Luo ◽  
Rui Xue ◽  
Jihao Yin
Keyword(s):  
Band Selection ◽  
Density Peak
Sign in / Sign up

Export Citation Format

Share Document