DOA esitmation based on support vector machine — Large scale multiclass classification problem

2011 ◽  
Author(s):  
Du Jin-xiang ◽  
Feng Xi-an ◽  
Ma Yan
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Support Vector

scholarly journals Fast outlier detection for high-dimensional data of wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096383
Author(s):  
Yan Qiao ◽  
Xinhong Cui ◽  
Peng Jin ◽  
Wu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Belief Network

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

scholarly journals Weighted Linear Loss Support Vector Machine for Large Scale Problems

2014 ◽  
Vol 31 ◽  
pp. 639-647 ◽  
Author(s):  
Yuan-Hai Shao ◽  
Zhen Wang ◽  
Zhi-Min Yang ◽  
Nai-Yang Deng
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Support Vector ◽  
Large Scale Problems ◽  
Linear Loss

Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

2020 ◽  
Vol 19 (6) ◽  
pp. 2075-2090 ◽  
Author(s):  
Hao Cheng ◽  
Furui Wang ◽  
Linsheng Huo ◽  
Gangbing Song
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Rapid Development ◽  
Voice Recognition ◽  
Support Vector ◽  
Machine Model ◽  
Frequency Scale ◽  
Removal Time ◽  
The Impact ◽  
Non Destructive

Deposits prevention and removal in pipeline has great importance to ensure pipeline operation. Selecting a suitable removal time based on the composition and mass of the deposits not only reduces cost but also improves efficiency. In this article, we develop a new non-destructive approach using the percussion method and voice recognition with support vector machine to detect the sandy deposits in the steel pipeline. Particularly, as the mass of sandy deposits in the pipeline changes, the impact-induced sound signals will be different. A commonly used voice recognition feature, Mel-Frequency Cepstrum Coefficients, which represent the result of a cosine transform of the real logarithm of the short-term energy spectrum on a Mel-frequency scale, is adopted in this research and Mel-Frequency Cepstrum Coefficients are extracted from the obtained sound signals. A support vector machine model was employed to identify the sandy deposits with different mass values by classifying energy summation and Mel-Frequency Cepstrum Coefficients. In addition, the classification accuracies of energy summation and Mel-Frequency Cepstrum Coefficients are compared. The experimental results demonstrated that Mel-Frequency Cepstrum Coefficients perform better in pipeline deposits detection and have great potential in acoustic recognition for structural health monitoring. In addition, the proposed Mel-Frequency Cepstrum Coefficients–based pipeline deposits monitoring model can estimate the deposits in the pipeline with high accuracy. Moreover, compared with current non-destructive deposits detection approaches, the percussion method is easy to implement. With the rapid development of artificial intelligence and acoustic recognition, the proposed method can realize higher accuracy and higher speed in the detection of pipeline deposits, and has great application potential in the future. In addition, the proposed percussion method can enable robotic-based inspection for large-scale implementation.

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