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| 1 | 应用支持向量机评价土壤环境质量显示文摘基于野外采样和室内分析相结合的方法,采用电感耦合等离子体质谱法(ICP-MS)对羊草沟煤矿研究区表层土壤样品中的Cd、Cr、Zn、Pb和Cu含量进行测定,应用非线性支持向量机模型中的分类支持向量机,选用sigmoid核函数,利用MATLAB编写程序,进行土壤环境质量评价,并利用模糊综合评判法对评价结果进行验证.在此基础上,运用对应分析方法对样品和变量进行了关联分析,进一步了解重金属污染特征.评价结果表明,研究区土壤环境质量多为Ⅰ类,与模糊综合评判法的相同率达到91.67%,将支持向量机用于土壤环境质量评价是可行的.相比于传统的评价方法,支持向量机采用结构风险最小化原则,将复杂的非线性问题转化为线性问题,成功的解决了多分类、高维运算等问题. | 姜雪 卢文喜 杨青春 赵海卿 | 2014 | 中国环境科学2014，34，5： | 25 |
| 2 | Machine learning methods for rockburst prediction-state-of-the-art review显示文摘One of the most serious mining disasters in underground mines is rockburst phenomena.They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment.This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence.However,due to the highly complex relation between geological,mechanical and geometric parameters of the mining environment,the traditional mechanics-based prediction methods do not always yield precise results.With the emergence of machine learning methods,a breakthrough in the prediction of rockburst occurrence has become possible in recent years.This paper presents a state-ofthe-art review of various applications of machine learning methods for the prediction of rockburst potential.First,existing rockburst prediction methods are introduced,and the limitations of such methods are highlighted.A brief overview of typical machine learning methods and their main features as predictive tools is then presented.The current applications of machine learning models in rockburst prediction are surveyed,with related mechanisms,technical details and performance analysis. | Yuanyuan Pu Derek B.Apel Victor Liu Hani Mitri | 2019 | International Journal of Mining Science and Technology2019，29，4： | 19 |
| 3 | Real-time transient stability assessment in power system based on improved SVM显示文摘Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy. | Wei HU Zongxiang LU Shuang WU Weiling ZHANG Yu DONG Rui YU Baisi LIU | 2019 | Journal of Modern Power Systems and Clean Energy2019，7，1： | 14 |
| 4 | Predicting the onset temperature(Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method显示文摘Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature(Tg ) of GexSe1-xglass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-xglass system is presented by a machine learning method named feature selection based two-stage support vector regression(FSTS-SVR). Firstly, Pearson correlation coefficient(PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error(RMSE) and mean absolute percentage error(MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic. | Yue Liu Junming Wu Guang Yang Tianlu Zhao Siqi Shi | 2019 | Science Bulletin2019，64，16： | 12 |
| 5 | 基于边缘检测和颜色纹理直方图的车牌定位方法显示文摘针对已有车牌定位算法在分辨率高、背景复杂图像上存在准确率下降的问题,提出了一种基于边缘检测和颜色纹理直方图的车牌定位算法。该定位算法分为两个阶段:首先利用结合了Canny和Sobel算法的改进边缘检测算法提取图像的垂直边缘,并结合滤波、投影等方法进行车牌粗定位;然后提取候选区域的颜色纹理直方图,与训练好的分类器进行匹配,实现车牌的精确定位。实验表明,该方法对于背景复杂、光照不均等情况均有良好的鲁棒性,在白天和晚上都能取得较好的定位效果。 | 郭延祥 陈耀武 | 2014 | 计算机科学与探索2014，8，6： | 12 |
| 6 | How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?显示文摘Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions. | Yacine Achour Hamid Reza Pourghasemi | 2020 | Geoscience Frontiers2020，11，3： | 11 |
| 7 | Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm显示文摘In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes. | 毛勇 周晓波 皮道映 孙优贤 WONG Stephen T.C. | 2005 | Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)2005，6，10： | 11 |
| 8 | Wind power prediction based on variational mode decomposition multi-frequency combinations显示文摘Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher. | Gang ZHANG Hongchi LIU Jiangbin ZHANG Ye YAN Lei ZHANG Chen WU Xia HUA Yongqing WANG | 2019 | Journal of Modern Power Systems and Clean Energy2019，7，2： | 10 |
| 9 | 基于小波变换和支持向量机的心电信号ST段分类显示文摘为完成ECG(Electrocardiogram)信号特征点提取,并对ST段分类,提出了一种基于离散小波变换和支持向量机的ST分类算法。首先对信号进行预处理,完成噪声消除,QRS波群检测和提取特征值;然后计算ST段平均值、曲线面积和标准差,并结合使用SVM(Support Vector Machine)对ST段进行分类。Matlab仿真结果表明,小波去噪效果明显,ST段未出现失真现象,特征点提取完整。经MIT-BIT数据库验证,分类结果显示交叉验证准确率平均值为80.70%,训练准确率平均值为91.83%,测试准确率平均值为74.28%。 | 杨宇 司玉娟 宋晓洋 | 2016 | 吉林大学学报(信息科学版)2016，34，3： | 8 |
| 10 | Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect显示文摘Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine(SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam.Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution. | Shao-wei Wang Ying-li Xu Chong-shi Gu Teng-fei Bao | 2018 | Water Science and Engineering2018，11，4： | 8 |
| 11 | A Hybrid Deep Sea Navigation System of LBL/DR Integration Based on UKF and PSO-SVM显示文摘In order to improve the navigation accuracy of human occupied vehicle(HOV)precisely and efficiently,an innovative hybrid approach based on unscented Kalman filter(UKF)and support vector machine(SVM)is proposed to fuse integrated navigation data.HOV is generally equipped with long baseline(LBL)acoustic positioning system and dead reckoning(DR)as an integrated navigation system.UKF is adopted to estimate the state of the dynamic model because of its good performance in filtering nonlinear problems.An accurate and stable filtering result can be obtained when both LBL and DR are online.At the same time,SVM is utilized to train DR information with the result when LBL outrages,and the particle swarm optimization(PSO)algorithm is employed for SVM parameters optimization.Therefore,the integrated navigation system can maintain a good performance when the LBL is off-line.Simulation results with the real navigation data of Jiaolong HOV show that the methodology proposed here is able to meet the needs of HOV application. | LIU Ben LIU Kaizhou WANG Yanyan ZHAO Yang CUI Shengguo WANG Xiaohui | 2015 | 机器人2015，37，5： | 7 |
| 12 | Nonlinear joint PP-PS AVO inversion based on improved Bayesian inference and LSSVM显示文摘Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion. | Xie Wei Wang Yan-Chun Liu Xue-Qing Bi Chen-Chen Zhang Feng-Qi Fang Yuan Tahir Azeem | 2019 | Applied Geophysics2019，16，1： | 7 |
| 13 | Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM显示文摘For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition. | Faquan Yang Ling Yang Dong Wang Peihan Qi Haiyan Wang 无 | 2018 | China Communications2018，15，12： | 7 |
| 14 | 基于提升小波和LS-SVM的大坝变形预测显示文摘提出了一种基于提升小波和最小二乘支持向量机的大坝变形预测方法,通过提升小波分析提取大坝监测数据效应量,分别对各效应量使用最小二乘支持向量机模型进行训练预测,再将合成各分量的预测结果作为最终的变形预测结果。算例结果表明,该方法较符合实际情况,具有很高的预测精度和良好的泛化能力。 | 秦栋 郑雪琴 许后磊 | 2010 | 水电能源科学2010，28，9： | 7 |
| 15 | An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method显示文摘Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine(SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization(GA-PSO)method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process. | Kexin Bi Tong Qiu | 2019 | Chinese Journal of Chemical Engineering2019，27，8： | 6 |
| 16 | CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES显示文摘Gears alternately mesh and detach in driving process, and then working conditions of gears are alternately changing, so they are easy to be spalled and worn. But because of the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; their fault features are difficult to extract. This study aims to propose an approach of gear faults classification,using the cumulants and support vector machines. The cumulants can eliminate the additive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vector machines as classifier, which is employed structural risk minimisation principle, is superior to that of conventional neural networks, which is employed traditional empirical risk minimisation principle. Support vector machines as the classifier, and the third and fourth order cumulants as input, gears faults are successfully recognized. The experimental results show that the method of fault classification combining cumulants with support vector machines is very effective. | Lai Wuxing Zhang Guicai Shi Tielin Yang ShuziSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology,Wuhan 430074, China | 2002 | Chinese Journal of Mechanical Engineering2002，15，3： | 6 |
| 17 | Aircraft Engine Gas Path Fault Diagnosis Based on Hybrid PSO-TWSVM显示文摘Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%. | Du Yanbin Xiao Lingfei Chen Yusheng Ding Runze | 2018 | Transactions of Nanjing University of Aeronautics and Astronautics2018，35，2： | 6 |
| 18 | Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery显示文摘Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy. | Wang Hongjun Xu Xiaoli Rosen B G | 2014 | 仪器仪表学报2014，35，S2： | 6 |
| 19 | Spatial scaling of net primary productivity model based on remote sensing显示文摘Spatial scaling for net primary productivity (NPP) refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine (SVM) algorithm are used to make the scaling model from the fine resolution (TM) to the coarse resolution (MODIS). Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher (R2=0.81). Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower (R2=0.69; RMSE=3.47), while the correlation between NPPTM and corrected NPPMODIS_corrected is higher (R2=0.84; RMSE= 1.87). Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error. | WANG Liwen WEI Yaxing NIU Zheng | 2010 | 遥感学报2010，14，6： | 5 |
| 20 | Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network显示文摘Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors. | LIU Yang ZHANG Jian-jing ZHU Chong-hao XIANG Bo WANG Dong | 2019 | Journal of Mountain Science2019，16，8： | 5 |
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