Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel i...Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction.展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with...Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.展开更多
In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and time...In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used as indicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%,the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.展开更多
Because the oilfields in eastern China are in the very high water cut development stage, accurate forecast of oilfield development indices is important for exploiting the oilfields efficiently. Regarding the problems ...Because the oilfields in eastern China are in the very high water cut development stage, accurate forecast of oilfield development indices is important for exploiting the oilfields efficiently. Regarding the problems of the small number of samples collected for oilfield development indices, a new support vector regression prediction method for development indices is proposed in this paper. This method uses the principle of functional simulation to determine the input-output of a support vector machine prediction system based on historical oilfield development data. It chooses the kernel function of the support vector machine by analyzing time series characteristics of the development index; trains and tests the support vector machine network with historical data to construct the support vector regression prediction model of oilfield development indices; and predicts the development index. The case study shows that the proposed method is feasible, and predicted development indices agree well with the development performance of very high water cut oilfields.展开更多
INSITE(Integrated System for Information Technology and Engineering)软件是哈里伯顿公司支持其随钻测井服务的地面系统,其核心部分是一个庞大的开放式数据库(ODBC)连接的数据库系统。通过探索INSITE软件的隐藏功能,可以在广域网和...INSITE(Integrated System for Information Technology and Engineering)软件是哈里伯顿公司支持其随钻测井服务的地面系统,其核心部分是一个庞大的开放式数据库(ODBC)连接的数据库系统。通过探索INSITE软件的隐藏功能,可以在广域网和局域网内让全球INSITE工作站实时连接在一起,在任何地方都可以共享到这些数据库,从而实现24小时的实时监控,为现场作业提供强大的实时支持。主要介绍的随钻测井、测量相关模块的数据库原理。同时通过现场对隐藏功能的开发应用,实现现场作业难题的解决,减少因故障造成的经济损失。展开更多
文摘Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金supported by the National Natural Science Foundation (71301119)the Shanghai Natural Science Foundation (12ZR1434100)
文摘Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.
基金supported by the Research and Innovation Program for College and University Graduate Students in Jiangsu Province (No.CX10B-141Z)the National Natural Science Foundation of China (No. 41071273)
文摘In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used as indicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%,the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.
基金support from Scientific Research Fund of Sichuan Provincial Education Department, P. R. China (No. 07za143)
文摘Because the oilfields in eastern China are in the very high water cut development stage, accurate forecast of oilfield development indices is important for exploiting the oilfields efficiently. Regarding the problems of the small number of samples collected for oilfield development indices, a new support vector regression prediction method for development indices is proposed in this paper. This method uses the principle of functional simulation to determine the input-output of a support vector machine prediction system based on historical oilfield development data. It chooses the kernel function of the support vector machine by analyzing time series characteristics of the development index; trains and tests the support vector machine network with historical data to construct the support vector regression prediction model of oilfield development indices; and predicts the development index. The case study shows that the proposed method is feasible, and predicted development indices agree well with the development performance of very high water cut oilfields.
文摘INSITE(Integrated System for Information Technology and Engineering)软件是哈里伯顿公司支持其随钻测井服务的地面系统,其核心部分是一个庞大的开放式数据库(ODBC)连接的数据库系统。通过探索INSITE软件的隐藏功能,可以在广域网和局域网内让全球INSITE工作站实时连接在一起,在任何地方都可以共享到这些数据库,从而实现24小时的实时监控,为现场作业提供强大的实时支持。主要介绍的随钻测井、测量相关模块的数据库原理。同时通过现场对隐藏功能的开发应用,实现现场作业难题的解决,减少因故障造成的经济损失。
文摘工作在复杂环境下的多元退化设备面临失效数据少、多源信息融合准确度低和监督学习数据不平衡等问题,对此本文提出一种基于时间序列生成对抗网络(Time-series Generative Adversarial Networks,TimeGAN)与单分类支持向量机(One-Class Support Vector Machine,OCSVM)组合模型的小子样数据增广方法.方法引入了TimeGAN模型拟合真实数据时间序列相关性,从而生成新的多元退化设备数据.本文提出了一种基于最大均值差异改进方法的可信度判据,避免强相关特征对生成数据质量评价的影响,通过使用T-分布随机邻近嵌入(T-distributed Stochastic Neighbor Embedding,T-SNE)和全局最大均值差异(Global Maximum Mean Discrepancy,GMMD)的组合方法,定性定量地评价生成数据的质量水平.基于训练后的OCSVM模型,对生成数据进行异常检测与剔除,进一步提高生成数据的质量.以航空发动机数据集C-MAPSS为例进行方法验证分析,通过与其他数据增强模型对比验证了所提方法的可行性和有效性.
