Smartphone Using-Mode Recognition Method Based on Novel Clustering Algorithm

2021 ◽  
pp. 3243-3254
Author(s):  
He-Tian Yi ◽  
Qing-Hua Zeng ◽  
Qi-Yao Lei ◽  
Jian-Ye Liu ◽  
Rui-Zhi Chen
Keyword(s):  
Clustering Algorithm ◽  
Recognition Method ◽  
Mode Recognition

Study on Enhancing Terminal Identification in Track and Field Using Digital X-Ray Photography Image

2014 ◽  
Vol 989-994 ◽  
pp. 3851-3855
Author(s):  
Guang Jin Lai
Keyword(s):  
Motion Capture ◽  
Clustering Algorithm ◽  
Digital Signal ◽  
Human Head ◽  
Track And Field ◽  
Image Clustering ◽  
Digital Signals ◽  
Recognition Method ◽  
One Dimensional ◽  
X Ray

Digital X-ray photography technology is under the control of the computer, to use one-dimensional or 2D X-ray detector to convert the captured image into digital signals directly to using image processing technology. It can realize the function of image analysis. We introduce X-ray photography technology into the terminal identification in track and field, and use the clustering algorithm to improve computer image clustering algorithm. Through capturing the digital signal of human head, arms and legs, it enhances the terminal recognition method in track and field. Finally we use MATLAB to calculate the captured image value of X-ray photography. Through calculation, motion capture and recognition of X-ray image are enhanced obviously. It provides a theoretical basis for researching on motion capture technology in track and field.

scholarly journals Research on Action Recognition Method of Dance Video Image Based on Human-Computer Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
FenTian Peng ◽  
Hongkai Zhang
Keyword(s):  
Human Computer Interaction ◽  
Action Recognition ◽  
Clustering Algorithm ◽  
Recognition Rate ◽  
Video Image ◽  
Recognition Method ◽  
Skeleton Model ◽  
Video Images ◽  
Motion Characteristics ◽  
Computer Interaction

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.

HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm

2019 ◽  
Vol 33 (08) ◽  
pp. 1950012 ◽  
Author(s):  
Jianhua Jiang ◽  
Wei Zhou ◽  
Limin Wang ◽  
Xin Tao ◽  
Keqin Li
Keyword(s):  
Clustering Algorithm ◽  
High Dimensionality ◽  
Test Cases ◽  
Low Density ◽  
Recognition Method ◽  
Density Peak ◽  
Irregular Shapes ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Density Peak Clustering

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.

Modulation Recognition of MQAM Signals Based on Semi Supervised Clustering Theory

2014 ◽  
Vol 519-520 ◽  
pp. 975-978
Author(s):  
Ping Ping Li ◽  
Gang Can Sun ◽  
Jin Yuan Shen
Keyword(s):  
Clustering Algorithm ◽  
Error Function ◽  
Computation Complexity ◽  
Modulation Recognition ◽  
Characteristic Parameters ◽  
Recognition Method ◽  
Supervised Clustering ◽  
Clustering Center ◽  
Different Order ◽  
Cluster Points

In the modulation recognition of MQAM signals cluster points of traditional clustering algorithm were not accurate, iterations of the algorithm are multiple and the curve of square error function was not smooth. To solve these problems, this paper presents a theory of modulation recognition method for reconstruction of MQAM signal constellation diagram based on semi supervised clustering, using labeled samples to guide the membership degree and the clustering center update. Analysis the receiving constellation and extracting the characteristic parameters R of constellation compared with parameter Rs of standard constellation, to realize modulation recognition of the different order of MQAM signal. The results show that the method to identify the MQAM signal at rate of 90. Convergence of iterative process is reduced from 40 to 8 times. The algorithm has low computation complexity and the square error function curve is smooth.

scholarly journals Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Zhang ◽  
Lili Pang
Keyword(s):  
Pattern Recognition ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Recognition Accuracy ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Internal Governance ◽  
Social Organizations ◽  
Definition Of ◽  
Internal Supervision

This paper proposes a multiple collaborative supervision pattern recognition method within social organizations based on data clustering algorithm to realize diversified supervision within social organizations and improve the effect of the said pattern recognition. Firstly, the characteristics and functions of social organizations are analyzed, and the definition of social organizations is given. Further, this paper studies the meaning and characteristics of social organization supervision, analyzes the failure of internal supervision of social organizations, and then determines the internal governance elements of social organizations. In addition, the basic steps of pattern recognition are given. Finally, multiple collaborative supervision patterns recognition within social organizations is realized based on data clustering algorithm. Experiments show that this method can improve the recognition accuracy of multiple collaborative supervision patterns and reduce the recognition time.

On Objective-Based Roughc-Regression

2015 ◽  
Vol 19 (1) ◽  
pp. 36-42
Author(s):  
Akira Sugawara ◽  
◽  
Yasunori Endo ◽  
Naohiko Kinoshita ◽  
Keyword(s):  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Pattern Recognition Method ◽  
Clustering Methods ◽  
Recognition Method ◽  
Numerical Examples

The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods,c-regression based on fuzzy set theory, called Fuzzyc-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Roughk-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Roughc-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.

An Online PD Pulses Recognition Method to Extract PD Pulses Buried in External Interferences

2011 ◽  
Vol 48-49 ◽  
pp. 208-214
Author(s):  
Chuan Li Gong ◽  
Yuan Wan ◽  
Hai Long Chi ◽  
Ye Ping Yang
Keyword(s):  
Clustering Algorithm ◽  
Phase Distribution ◽  
Three Dimensional ◽  
Single Pulse ◽  
Online Measurement ◽  
Recognition Method ◽  
Power Cycles ◽  
Site Monitoring ◽  
Dimensional Distribution ◽  
Key Steps

Partial discharge (PD) pulses recognition, which is directly related to the credibility of PD online measurement results, is the key technology of the high-voltage (HV) equipment PD online monitoring. In this paper, a pulses recognition method during PD online measurement was put forward to extract PD pulses buried in external interferences, the method adopted three key steps to identify PD pulses: (a) Firstly, two pretreatments, including discrete spectrum interferences (DSI) suppression and single pulse boundaries determination, are used to convert several consecutive power cycles on-site monitoring signal to plenty of pulse - time sequences; (b) Then, an optimized adaptive clustering algorithm is adopted to classify all pulses into certain categories (c) Finally, the three-dimensional distribution of each category which indicated the pulses phase distribution with power cycles was calculated, and the PD pulses were identified by the statistics characteristics of their three-dimensional distribution. At present, the PD pulses recognition method has been used in industrial application, and on-site monitoring signal processing results has proved the effectiveness of the method.

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