scholarly journals An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

2018 ◽  
Vol 5 (9) ◽  
pp. 180160 ◽  
Author(s):  
Jian-hong Yang ◽  
Huai-ying Fang ◽  
Ren-cheng Zhang ◽  
Kai Yang
Keyword(s):  
Low Voltage ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Signal Loss ◽  
Current Waveform ◽  
Accurate Identification ◽  
Metallic Contacts ◽  
Vector Machines ◽  
Integral Characteristics

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

Research on Snoring Recognition Algorithms

2019 ◽  
Vol 31 (1) ◽  
pp. 70-77
Author(s):  
Yongping Dan ◽  
Yaming Song ◽  
Dongyun Wang ◽  
Fenghui Zhang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Endpoint Detection ◽  
Accuracy Rate ◽  
Vector Machines ◽  
Extraction Algorithm ◽  
Speech Endpoint Detection

A snoring recognition algorithm based on machine learning is proposed to effectively and precisely recognize snoring. To obtain a dataset, the speech endpoint detection algorithm and Mel frequency cepstrum coefficient feature extraction algorithm are applied to process speech signal samples. The dataset is classified into snoring and nonsnoring data (other speech signals) using support vector machines. Experimental results show that the algorithm recognizes snoring signals with a high accuracy rate of 97% and positively impacts subsequent research and related engineering applications.

scholarly journals Creating a Chinese suicide dictionary for identifying suicide risk on social media

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1455 ◽  
Author(s):  
Meizhen Lv ◽  
Ang Li ◽  
Tianli Liu ◽  
Tingshao Zhu
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Classification Performance ◽  
Support Vector ◽  
Accurate Identification ◽  
Vector Machines ◽  
Social Media Service ◽  
Linguistic Inquiry ◽  
Suicide Prevention Programs ◽  
Expert Ratings

Introduction.Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary.Methods.To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models.Results and Discussion.Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r= 0.507) and evaluating individual suicide risk (r= 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1:F1= 0.48; t2:F1= 0.56) produced a more accurate identification than SCLIWC (t1:F1= 0.41; t2:F1= 0.48) on different observation windows.Conclusions.This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries.

scholarly journals ECG Multilead QT Interval Estimation Using Support Vector Machines

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jhosmary Cuadros ◽  
Nelson Dugarte ◽  
Sara Wong ◽  
Pablo Vanegas ◽  
Villie Morocho ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Interval Estimation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Support Vector ◽  
Interval Measurement ◽  
Ecg Signals ◽  
Percent Error ◽  
Vector Machines ◽  
Measurement Algorithm

This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points (Qini and Tend) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60±2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49±1.99 msec.

Portable Wireless Device for Automated Agitation Detection

2013 ◽  
pp. 236-255
Author(s):  
George E. Sakr ◽  
Imad H. Elhajj ◽  
Mohamad Khaled Joujou ◽  
Sarah Abboud ◽  
Huda Abu-Saad Huijer
Keyword(s):  
Medical Devices ◽  
Detection Algorithm ◽  
Future Research ◽  
Support Vector ◽  
Portable Devices ◽  
Research Directions ◽  
Vector Machines ◽  
Skin Conductivity ◽  
Wireless Device ◽  
Future Research Directions

The objective of this chapter is to provide an overview of existing portable medical devices. The chapter then focuses on portable automated agitation detection. The design and prototyping of a device capable of portable wireless agitation detection is detailed. In addition, the agitation detection algorithm, which uses Support Vector Machines (SVM) based on the measurement of skin conductivity, skin temperature, and inter-beat interval, is described. Experimental results pertaining to the performance of the device and the agitation detection are provided. The chapter concludes with challenges to the development of medical portable devices in general and to agitation detection specifically. Some potential future research directions are highlighted.

Recognition algorithm for the disengagement of cement asphalt mortar based on dynamic responses of vehicles

2018 ◽  
Vol 233 (3) ◽  
pp. 270-282 ◽  
Author(s):  
Hui Shi ◽  
Zujun Yu ◽  
Hongmei Shi ◽  
Liqiang Zhu
Keyword(s):  
Support Vector Machines ◽  
Coupled Model ◽  
Recognition Algorithm ◽  
Dynamic Responses ◽  
Support Vector ◽  
Safe Operation ◽  
Cement Asphalt Mortar ◽  
Vector Machines ◽  
Track Substructure ◽  
Asphalt Mortar

Disengagement of emulsified cement asphalt mortar will increase the dynamic action between the vehicle and the track; as a consequence, the rate of cement asphalt mortar disengagement will increase further. This is a serious threat for the safe operation of high-speed railways and the service life of rail equipment. In this study, a vertical coupled model for the vehicle–China Railway Track System II-type slab track with cement asphalt disengagement was established. The cement asphalt mortar was divided into units in order to simulate the arbitrary length of disengagement. Under different conditions, the effects of the cement asphalt mortar disengagement on the dynamic characteristics of the coupled model were analyzed. The results show that when the length of disengagement exceeds 0.65 m under the condition of horizontal complete disengagement, the dynamic responses of the system increase much sharply than the condition of horizontal partly disengagement. Because of the difficulty in identifying defects in the track substructure, a novel method was proposed to rapidly identify the cement asphalt mortar disengagement based on the dynamic responses of the coupled system and particle swarm optimization–support vector machines. The feature vectors were extracted from the acceleration of the wheelset, which were used as training samples in support vector machines. The classification results show that the recognition algorithm based on the acceleration of the wheelset and support vector machines is effective. The location of the track plate with the cement asphalt mortar disengagement at lengths of 0.65 m, 1.3 m, and 1.95 m can be identified with an acceptable accuracy. The robustness of the proposed algorithm under different vehicle speeds, track spectrums, and signal–noise ratios was verified. Recognition of defects in the track substructure using sensors mounted on in-service vehicles has the potential to provide a valuable tool for ensuring the safe operation of railways and for developing a maintenance plan.

scholarly journals Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Phat Nguyen Huu ◽  
Tan Phung Ngoc
Keyword(s):  
Gesture Recognition ◽  
Processing Speed ◽  
Industrial Applications ◽  
Recognition Algorithm ◽  
Robotic System ◽  
Support Vector ◽  
Histogram Of Oriented Gradient ◽  
Real Time Processing ◽  
Vector Machines ◽  
The Status

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

A Detection Algorithm for Projectile Target

2014 ◽  
Vol 1044-1045 ◽  
pp. 972-975
Author(s):  
Zai Fei Shang ◽  
Chun Ping Wang
Keyword(s):  
Target Detection ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Support Vector ◽  
Performance Simulation ◽  
Histogram Of Oriented Gradient ◽  
Mean Shift Algorithm ◽  
Small Target Detection ◽  
Time Performance ◽  
Vector Machines

For consistency of performance in the shape of the projectile targets, a projectile target detection algorithm is presented based on HOG (Histogram of Oriented Gradient) characterization algorithm. First, detecting the bullet image corner, and secondly, by Mean-shift algorithm improves the corner position accuracy and reduces the number of corner points, finally, applying support vector machines to extract the projectile targets. Compared with the traditional small target detection algorithm, the algorithm describes the targets more accurately, along with better real-time performance. Simulation, the projectile target detection rate of over 80% and verify the effectiveness of the algorithm.

Feature recognition of spoken Japanese input based on support vector machine

2020 ◽  
pp. 1-12
Author(s):  
Yanping Han
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Research Model ◽  
Language Recognition ◽  
Time Translation ◽  
Vector Machines ◽  
System Requirements ◽  
Japanese Communication ◽  
Translation Systems

The feature recognition of spoken Japanese is an effective carrier for Sino-Japanese communication. At present, most of the existing intelligent translation equipment only have equipment that converts English into other languages, and some Japanese translation systems have problems with accuracy and real-time translation. Based on this, based on support vector machines, this research studies and recognizes the input features of spoken Japanese, and improves traditional algorithms to adapt to the needs of spoken language recognition. Moreover, this study uses improved spectral subtraction based on spectral entropy for enhancement processing, modifies Mel filter bank, and introduces several improved MFCC feature parameters. In addition, this study selects an improved feature recognition algorithm suitable for this research system and conducts experimental analysis of input feature recognition of spoken Japanese on the basis of this research model. The research results show that this research model has improved the recognition speed and recognition accuracy, and this research model meets the system requirements, which can provide a reference for subsequent related research.

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