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An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression Research Papers
Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 816-828 doi: 10.1631/FITEE.1800146
The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.
Keywords: Power grid Artificial intelligence Node importance Text-associated DeepWalk Network embedding Support vector regression
Image quality assessmentmethod based on nonlinear feature extraction in kernel space Article
Yong DING,Nan LI,Yang ZHAO,Kai HUANG
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10, Pages 1008-1017 doi: 10.1631/FITEE.1500439
Keywords: Image quality assessment Full-reference method Feature extraction Kernel space Support vector regression
Interactive image segmentation with a regression based ensemble learning paradigm Article
Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7, Pages 1002-1020 doi: 10.1631/FITEE.1601401
Keywords: Interactive image segmentation Multivariate adaptive regression splines (MARS) Ensemble learning Thin-plate spline regression (TPSR) Semi-supervised learning Support vector regression (SVR)
No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis Research Article
Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao,hyao@usst.edu.cn,xudong@zjcc.org.cn,yaojc@zjcc.org.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12, Pages 1551-1684 doi: 10.1631/FITEE.2000716
Keywords: 噪声图像质量评价;噪声估计;峰度;人类视觉系统;支持向量回归
Xiang Xiaodong
Strategic Study of CAE 2008, Volume 10, Issue 11, Pages 89-92
According to the theory that the present data contains more future information than historical data in time-series,the paper extends the prediction method of least square support vector machine and obtains a more general prediction model of least square support vector machine,and develops algorithm of the extended prediction model.Prediction examples of two time-series show that the extended model is more effective.Therefore it improves the value of the prediction method of least square support vector machine.
Keywords: least square support vector machine generalization time series forecasting
A robust intelligent audio watermarking scheme using support vector machine Article
Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12, Pages 1320-1330 doi: 10.1631/FITEE.1500297
Keywords: Audio watermarking Copyright protection Singular value decomposition (SVD) Machine learning Support vector machine (SVM)
Man-machine verification of mouse trajectory based on the random forestmodel Research Articles
Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 7, Pages 925-929 doi: 10.1631/FITEE.1700442
Identifying code has been widely used in man-machine verification to maintain network security. The challenge in engaging man-machine verification involves the correct classification of man and machine tracks. In this study, we propose a random forest (RF) model for man-machine verification based on the mouse movement trajectory dataset. We also compare the RF model with the baseline models (logistic regression and support vector machine) based on performance metrics such as precision, recall, false positive rates, false negative rates, F-measure, and weighted accuracy. The performance metrics of the RF model exceed those of the baseline models.
Keywords: Man-machine verification Random forest Support vector machine Logistic regression Performance metrics
应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断 Article
俊红 张,昱 刘
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 272-286 doi: 10.1631/FITEE.1500337
Anefficient parallel and distributed solution to nonconvex penalized linear SVMs Personal View
Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4, Pages 587-603 doi: 10.1631/FITEE.1800566
Keywords: Linear classification Support vector machine (SVM) Nonconvex penalty Alternating direction method of multipliers (ADMM) Parallel algorithm
Performance analysis of new word weighting procedures for opinion mining Article
G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11, Pages 1186-1198 doi: 10.1631/FITEE.1500283
Keywords: Inferred word weight Opinion mining Supervised classification Support vector machine (SVM) Machine learning
A new feature selection method for handling redundant information in text classification None
You-wei WANG, Li-zhou FENG
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2, Pages 221-234 doi: 10.1631/FITEE.1601761
Keywords: Feature selection Dimensionality reduction Text classification Redundant features Support vector machine Naïve Bayes Mutual information
Luo Bowen,Xia Yimin,Wang Tao,Bai Hongfeng,Wang Kezhi
Strategic Study of CAE 2010, Volume 12, Issue 9, Pages 78-83
One of the key technologies to solve the dilution problem of cobalt crust is to calculate the best exploitation depth by surface elevation information. A single-beam swing ultrasonic micro-terrain detection method was proposed based on occurrence of cobalt crust and mining method. To test the feasibility of the method a laboratory bench has been successfully built, and the fact that the detection accuracy of vertical bench can achieve centimeter level has been verified. Subsequently. an improved data windowing algorithm to further improve the accuracy of removing exceptional elevation value was submitted; linear correlation coefficient method was adopted to choose linear and nonlinear regression support vector.It is proposed and proved that Gaussian radial basis function parameter σ can be determined according to the slope, slope length information on measuring line.
Keywords: cobalt-rich crusts ultrasonic micro-terrain windowed support vector
Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO
Lian Jijian,He Longjun,Wang Haijun
Strategic Study of CAE 2011, Volume 13, Issue 12, Pages 45-50
The vibration of powerhouse structures is mainly induced by hydraulics factors, mechanical and electromagnetic factors of the generating unit. It nonlinearly couples with the generating unit. Based on prototype observation data of Ertan Hydropower Station, the paper analyzes the coupling effect between vibration of units and powerhouse,and then the vibration response forecasting model of the powerhouse is built based on LS-SVM optimized by particle swarm optimization algorithm, and the prediction results are coincide with the observed data. Further, the paper introduces the running water head as an input divisor into the intelligent prediction model while the forecasting range is extended, and the result is satisfactory.
Keywords: powerhouse coupled vibration particle swarm optimization algorithm least squares support vector machines response prediction
Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article
Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang
Engineering 2021, Volume 7, Issue 12, Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028
Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.
Keywords: Multivariate regression methods Reduced rank regression Sparsity Dimensionality reduction Variable selection
Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee
Engineering 2020, Volume 6, Issue 8, Pages 919-926 doi: 10.1016/j.eng.2020.07.001
Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.
Keywords: Pine wilt disease Drone remote sensing Artificial neural network Support vector machine Global positioning system
Title Author Date Type Operation
An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression
Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE
Journal Article
Image quality assessmentmethod based on nonlinear feature extraction in kernel space
Yong DING,Nan LI,Yang ZHAO,Kai HUANG
Journal Article
Interactive image segmentation with a regression based ensemble learning paradigm
Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU
Journal Article
No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis
Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao,hyao@usst.edu.cn,xudong@zjcc.org.cn,yaojc@zjcc.org.cn
Journal Article
Generalization and application in time series forecasting of the least square support vector machine method
Xiang Xiaodong
Journal Article
A robust intelligent audio watermarking scheme using support vector machine
Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR
Journal Article
Man-machine verification of mouse trajectory based on the random forestmodel
Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG
Journal Article
Anefficient parallel and distributed solution to nonconvex penalized linear SVMs
Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU
Journal Article
Performance analysis of new word weighting procedures for opinion mining
G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI
Journal Article
A new feature selection method for handling redundant information in text classification
You-wei WANG, Li-zhou FENG
Journal Article
Experimental research on micro-terrain exploring technology based on deep-ocean cobalt-rich crusts exploitation
Luo Bowen,Xia Yimin,Wang Tao,Bai Hongfeng,Wang Kezhi
Journal Article
Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO
Lian Jijian,He Longjun,Wang Haijun
Journal Article
Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research
Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang
Journal Article