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.

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.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

scholarly journals Vision-based Pakistani Sign Language Recognition Using Bag-of-Words and Support Vector Machines

2022 ◽  
Author(s):  
Muhammad Shaheer Mirza ◽  
Sheikh Muhammad Munaf ◽  
Shahid Ali ◽  
Fahad Azim ◽  
Saad Jawaid Khan
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Support Vector ◽  
Bag Of Words ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Vector Machines ◽  
Rgb Images ◽  
Native Signers

Abstract In order to perform their daily activities, a person is required to communicating with others. This can be a major obstacle for the deaf population of the world, who communicate using sign languages (SL). Pakistani Sign Language (PSL) is used by more than 250,000 deaf Pakistanis. Developing a SL recognition system would greatly facilitate these people. This study aimed to collect data of static and dynamic PSL alphabets and to develop a vision-based system for their recognition using Bag-of-Words (BoW) and Support Vector Machine (SVM) techniques. A total of 5,120 images for 36 static PSL alphabet signs and 353 videos with 45,224 frames for 3 dynamic PSL alphabet signs were collected from 10 native signers of PSL. The developed system used the collected data as input, resized the data to various scales and converted the RGB images into grayscale. The resized grayscale images were segmented using Thresholding technique and features were extracted using Speeded Up Robust Feature (SURF). The obtained SURF descriptors were clustered using K-means clustering. A BoW was obtained by computing the Euclidean distance between the SURF descriptors and the clustered data. The codebooks were divided into training and testing using 5-fold cross validation. The highest overall classification accuracy for static PSL signs was 97.80% at 750×750 image dimensions and 500 Bags. For dynamic PSL signs a 96.53% accuracy was obtained at 480×270 video resolution and 200 Bags.

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.

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