scholarly journals Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jian-Jun Yan ◽  
Rui Guo ◽  
Yi-Qin Wang ◽  
Guo-Ping Liu ◽  
Hai-Xia Yan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fractal Dimension ◽  
Chinese Medicine ◽  
Healthy Subjects ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Male And Female ◽  
Multiclass Support Vector Machine

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

Study about Recognition of Digital Meter Dial Reading Based on SVM

2014 ◽  
Vol 615 ◽  
pp. 194-197
Author(s):  
Zhen Yuan Tu ◽  
Fang Hua Ning ◽  
Wu Jia Yu
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Feature Points ◽  
Svm Classification ◽  
Performance Factors ◽  
Model Combining ◽  
Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

Quantitative inspection for identifying broken coal mine wire rope based on wavelet packet sparse representation

2021 ◽  
Vol 63 (2) ◽  
pp. 102-110
Author(s):  
Junying Zhou ◽  
Jie Tian ◽  
Peng Cheng ◽  
Xu Li ◽  
Decheng Wang
Keyword(s):  
Sparse Representation ◽  
Matching Pursuit ◽  
Wavelet Packet ◽  
Median Filter ◽  
Recognition Rate ◽  
Wire Rope ◽  
Classification Model ◽  
Filter Method ◽  
Support Vector ◽  
Reconstructed Signal

The magnetic flux leakage (MFL) signal of steel wire rope is easily affected by background noise, rope strands and so on. A preprocessing method for the damage signal based on wavelet packet sparse representation is proposed. This method is suitable for the damage signal of the wire rope. The original signal is decomposed into three layers of wavelet packets and the wavelet packet coefficients are sparsely represented by the matching pursuit (MP) and orthogonal matching pursuit (OMP) algorithms. The signal-to-noise ratio (SNR) of the reconstructed signal is much higher than that obtained through the wavelet threshold shrinkage method, the median filter method and the singular value difference spectrum method. The proposed method can significantly improve the noise reduction effect of the damage signal. A principal component analysis (PCA)-based particle swarm optimisation support vector machine (PSO-SVM) model for quantitative recognition is proposed. Seven global eigenvalues and wavelet packet energy entropy details of damage signals are extracted as effective eigenvalues. The eight eigenvalues are used as the input for the SVM that is designed and trained. A PSO-SVM classification model based on PCA is proposed. The results show that the recognition rate of the SVM is 94.73%. The quantitative recognition accuracy is improved.

scholarly journals Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

2021 ◽  
Author(s):  
Christian Kubik ◽  
Sebastian Michael Knauer ◽  
Peter Groche
Keyword(s):  
Support Vector Machine ◽  
Data Acquisition ◽  
Value Chain ◽  
Manufacturing Systems ◽  
Classification Model ◽  
Support Vector ◽  
Process Conditions ◽  
Manufacturing Companies ◽  
Feature Extraction And Selection ◽  
Multiclass Support Vector Machine

AbstractIn consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.

scholarly journals Research on the Open-Categorical Classification of the Internet-of-Things Based on Generative Adversarial Networks

2018 ◽  
Vol 8 (12) ◽  
pp. 2351 ◽  
Author(s):  
Caidan Zhao ◽  
Mingxian Shi ◽  
Zhibiao Cai ◽  
Caiyun Chen
Keyword(s):  
Support Vector Machine ◽  
Internet Of Things ◽  
Recognition Rate ◽  
Classification Model ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Real Samples ◽  
The Real ◽  
Adversarial Networks ◽  
Categorical Classification

Nowadays, it is more and more important to deal with the potential security issues of internet-of-things (IoT). Indeed, using the physical layer features of IoT wireless signals to achieve individual identity authentication is an effective way to enhance the security of IoT. However, traditional classifiers need to know all the categories in advance to get the recognition models. Realistically, it is difficult to collect all types of samples, which will result in some mistakes that the unknown target class may be decided as a known one. Consequently, this paper constructs an improving open-categorical classification model based on the generative adversarial networks (OCC-GAN) to solve the above problems. Here, we have modified the loss function of the generative model G and the discriminative model D. Compared to the traditional GAN model which can generate the fake sample overlapping with the real samples, our proposed G model generates the fake samples as negative samples which are evenly surrounding with the real samples, while the D model learns to distinguish between real samples and fake samples. Besides, we add auxiliary training not only to gain a better recognition result but also to improve the efficiency of the model. Furthermore, Our proposed model is verified through experimental study. Compared to other common methods, such as one-class support vector machine (OC-SVM) and one-versus-rest support vector machine (OvR-SVM), the OCC-GAN model has a better performance. The recognition rate of the OCC-GAN model can reach more than 90% with a recall rate of 97% by the data of the IoT module.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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