Research on real time feature extraction method for complex manufacturing big data

2016 ◽  
Vol 99 (5-8) ◽  
pp. 1101-1108 ◽  
Author(s):  
Xianguang Kong ◽  
Jiantao Chang ◽  
Meng Niu ◽  
Xiaoyu Huang ◽  
Jihu Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Real Time ◽  
Extraction Method ◽  
Feature Extraction Method

scholarly journals Research on feature extraction method of power grid online data based on big data

2021 ◽  
Vol 2030 (1) ◽  
pp. 012064
Author(s):  
Ji lin Chen ◽  
Yanhao Huang ◽  
Weijiang Qiu ◽  
Xinglei Chen ◽  
Ning An ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Extraction Method ◽  
Power Grid ◽  
Online Data ◽  
Feature Extraction Method

scholarly journals A Robust Feature Extraction Method for Real-Time Speech Recognition System on a Raspberry Pi 3 Board

2019 ◽  
Vol 9 (2) ◽  
pp. 4066-4070 ◽  
Author(s):  
A. Mnassri ◽  
M. Bennasr ◽  
C. Adnane
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Real Time ◽  
Extraction Method ◽  
Recognition System ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Speech Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

The development of a real-time automatic speech recognition system (ASR) better adapted to environmental variabilities, such as noisy surroundings, speaker variations and accents has become a high priority. Robustness is required, and it can be performed at the feature extraction stage which avoids the need for other pre-processing steps. In this paper, a new robust feature extraction method for real-time ASR system is presented. A combination of Mel-frequency cepstral coefficients (MFCC) and discrete wavelet transform (DWT) is proposed. This hybrid system can conserve more extracted speech features which tend to be invariant to noise. The main idea is to extract MFCC features by denoising the obtained coefficients in the wavelet domain by using a median filter (MF). The proposed system has been implemented on Raspberry Pi 3 which is a suitable platform for real-time requirements. The experiments showed a high recognition rate (100%) in clean environment and satisfying results (ranging from 80% to 100%) in noisy environments at different signal to noise ratios (SNRs).

scholarly journals Deep Learning Feature Extraction using Pre-Trained Alex Net Model for Indian Sign Language Recognition

2019 ◽  
Vol 8 (2) ◽  
pp. 6326-6333
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Real Time ◽  
Sign Language ◽  
Extraction Method ◽  
Support Vector ◽  
Light Illumination ◽  
Language Recognition ◽  
Feature Extraction Method ◽  
Indian Sign Language

Indian sign language is communicating language among deaf and dumb people of India. Hand gestures are broadly used as communication gestures among various forms of gesture. The real time classification of different signs is a challenging task due to the variation in shape and position of hands as well as due to the variation in the background which varies from person to person. There seems to be no availability of datasets resembling to Indian signs which poses a problem to the researcher. To address this problem, we design our own dataset which is formed by incorporating 1000 signs for the sign digits from 1 to 10 from 100 different people with varying backgrounds conditions by changing colour, and light illumination situations. The dataset comprises of the signs from left handed as well as right handed people. Feature extraction methodologies are studied and applied to recognition of Sign language. This paper focuses on deep learning CNN (convolution neural network) approach with pretrained model Alexnet for calculation of feature vector. Multiple SVM (Support Vector Machine) is applied to classify Indian sign language in real time surroundings. This paper also shows the comparative analysis between Deep learning feature extraction method with histogram of gradient, bag of feature and Speed up robust feature extraction method. The experimental results shown that Deep learning feature extraction using pretrained Alexnet model give accuracy of around 85% and above for the recognition of signed digit with the use of 60% training set and 40% testing set.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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