Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squ...Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.展开更多
A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set l...A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.展开更多
Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decompos...Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.展开更多
Determining the number of chemical species is the first step in analyses of a chemical or biological system. A novel method is proposed to address this issue by taking advantage of frequency differences between chemic...Determining the number of chemical species is the first step in analyses of a chemical or biological system. A novel method is proposed to address this issue by taking advantage of frequency differences between chemical information and noise. Two interlaced submatrices were obtained by downsampling an original data spectra matrix in an interlacing manner. The two interlaced submatrices contained similar chemical information but different noise levels. The number of relevant chemical species was determined through pairwise comparisons of principal components obtained by principal component analysis of the two interlaced submatrices. The proposed method, referred to as SRISM, uses two self-referencing interlaced submatrices to make the determination. SRISM was able to selectively distinguish relevant chemical species from various types of interference factors such as signal overlapping, minor components and noise in simulated datasets. Its performance was further validated using experimental datasets that contained high-levels of instrument aberrations, signal overlapping and collinearity. SRISM was also applied to infrared spectral data obtained from atmospheric monitoring. It has great potential for overcoming various types of interference factor. This method is mathematically rigorous, computationally efficient, and readily automated.展开更多
基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化...基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化学反应列表,通过物种检索进一步对反应路径进行分类。但VARxMD目前的反应分析针对的是某一确定条件下单一的ReaxFF MD模拟轨迹,利用VARxMD分析获得一次模拟的完整反应列表需要消耗大量计算资源和时间。本文提出基于数据库来储存VARxMD反应分析结果数据,基于数据库检索进一步分析反应的思路,并采用MVVM(model-view-view model)的系统设计模式、结合渐进式框架Vue.js建立了ReaxFF MD模拟的化学反应数据系统ReaxMDDB(reaction database of ReaxFF MD simulation)。系统应用于多个RP-3模型热解和氧化模拟反应数据的结果表明:该系统不仅实现了多个ReaxFF MD模拟的详细反应的统一分析和化学反应的2D分子结构显示,而且可永久保存模拟获得的反应数据集以备后续进一步分析反应机理。ReaxMDDB具有很好的通用性,为认识不同反应模拟所揭示的共性化学反应机理提供了方便的平台。展开更多
文摘Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.
基金supported by the National Natural Science Key Foundation of China(69974021)
文摘A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.
文摘Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.
基金supported by the Program for Changjiang Scholars and Innovative Research Team in University and Fundamental Research Funds for the Central Universities(wk2060190040)
文摘Determining the number of chemical species is the first step in analyses of a chemical or biological system. A novel method is proposed to address this issue by taking advantage of frequency differences between chemical information and noise. Two interlaced submatrices were obtained by downsampling an original data spectra matrix in an interlacing manner. The two interlaced submatrices contained similar chemical information but different noise levels. The number of relevant chemical species was determined through pairwise comparisons of principal components obtained by principal component analysis of the two interlaced submatrices. The proposed method, referred to as SRISM, uses two self-referencing interlaced submatrices to make the determination. SRISM was able to selectively distinguish relevant chemical species from various types of interference factors such as signal overlapping, minor components and noise in simulated datasets. Its performance was further validated using experimental datasets that contained high-levels of instrument aberrations, signal overlapping and collinearity. SRISM was also applied to infrared spectral data obtained from atmospheric monitoring. It has great potential for overcoming various types of interference factor. This method is mathematically rigorous, computationally efficient, and readily automated.
文摘基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化学反应列表,通过物种检索进一步对反应路径进行分类。但VARxMD目前的反应分析针对的是某一确定条件下单一的ReaxFF MD模拟轨迹,利用VARxMD分析获得一次模拟的完整反应列表需要消耗大量计算资源和时间。本文提出基于数据库来储存VARxMD反应分析结果数据,基于数据库检索进一步分析反应的思路,并采用MVVM(model-view-view model)的系统设计模式、结合渐进式框架Vue.js建立了ReaxFF MD模拟的化学反应数据系统ReaxMDDB(reaction database of ReaxFF MD simulation)。系统应用于多个RP-3模型热解和氧化模拟反应数据的结果表明:该系统不仅实现了多个ReaxFF MD模拟的详细反应的统一分析和化学反应的2D分子结构显示,而且可永久保存模拟获得的反应数据集以备后续进一步分析反应机理。ReaxMDDB具有很好的通用性,为认识不同反应模拟所揭示的共性化学反应机理提供了方便的平台。
