scholarly journals Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

Energies ◽  
2017 ◽  
Vol 10 (11) ◽  
pp. 1862 ◽  
Author(s):  
Xiaomin Xu ◽  
Dongxiao Niu ◽  
Lihui Zhang ◽  
Yongli Wang ◽  
Keke Wang
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Data Processing ◽  
Transmission Line ◽  
Power Grid ◽  
Ice Cover ◽  
Support Vector ◽  
Two Stage ◽  
Adaptive Support

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

2016 ◽  
Vol 21 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Alhadi Ali Albousefi ◽  
Hao Ying ◽  
Dimitar Filev ◽  
Fazal Syed ◽  
Kwaku O. Prakah-Asante ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Two Stage ◽  
Training Support ◽  
Lane Departure

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

scholarly journals Intelligent Prediction of Transmission Line Project Cost Based on Least Squares Support Vector Machine Optimized by Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Yi ◽  
Hao Zheng ◽  
Yu Tian ◽  
Jin-peng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Transmission Line ◽  
Least Squares ◽  
Principal Component ◽  
Support Vector ◽  
Swarm Optimization ◽  
Empirical Parameter ◽  
Parameter Prediction

In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

scholarly journals Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 135
Author(s):  
Cai ◽  
Liu ◽  
Luo ◽  
Du ◽  
Tang
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Early Breast Cancer ◽  
Detection Algorithm ◽  
Support Vector ◽  
Important Landmark ◽  
Professional Doctor ◽  
Breast Microcalcification ◽  
Adaptive Support ◽  
Microcalcification Detection

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

Sign in / Sign up

Export Citation Format

Share Document