纺织面料热阻和湿阻的回归测量法

为进一步减小纺织面料热阻及湿阻的测量误差,提出面料热阻和湿阻的回归测量法,该方法通过测量1~4层面料的总热阻和总湿阻,经线性回归直接得到面料热阻和湿阻及其上方空气层的热阻和湿阻,解决了传统测量法使用空板空气层热阻或湿阻代替面料上方空气层热阻或湿阻带入系统误差的问题。试验结果表明,回归测量法在热阻测量的准确性方面改善尤为明显,在湿阻测量方面的改善相对较小,还需进一步研究。

考虑轨面设备的无绝缘轨道电路道砟电阻回归测量方法

针对无绝缘轨道电路中因补偿电容和调谐区单元等轨面设备影响而难以准确测量道砟电阻的问题,提出由轨面电流幅值测量、参数拟合和回归计算3部分组成的道砟电阻回归测量方法。根据传输线理论,构建轨面电流幅值包络模型,分析道砟电阻、补偿电容和调谐区设备对轨面电流幅值的影响规律;对轨面电流幅值进行指数拟合,得到不同道砟电阻所对应的衰减因子,构建衰减因子与道砟电阻的回归计算式;基于人工方式实地测量的轨面电流幅值,回归计算得到道砟电阻;通过轨道电路半实物仿真实验平台,对该测量方法分别进行功能验证和性能验证。结果表明:功能验证中,仅测量指定3个位置点的轨面电流,即可较为准确地估算出道砟电阻,绝对误差为0.08Ω⋅km,相对误差为4.04%;性能验证中,计算得到道砟电阻的最大绝对误差仅为0.157Ω⋅km,对应的相对误差为7.9%,且测量结果受补偿电容和调谐区设备故障的影响较小。

基于时间近邻拉氏正则的多工况软测量回归

针对流程工业中,工况改变导致传统软测量模型预测精度下降的问题,考虑到工业数据连续性、序列性、多重共线性、数据量庞大等特殊性对模型建立的影响,提出一种基于时间近邻拉普拉斯正则的多工况软测量回归模型框架。针对工业数据的多重共线性,回归框架采用非线性迭代偏最小二乘方法,同时引入域适应正则项改善工况变化对模型的影响,在此基础上,提出时间近邻拉普拉斯正则项,能够在映射过程中保持住数据的序列结构,并且大幅度减少模型训练时间以满足工业实时性要求。实验部分以三聚氰胺聚合过程多工况数据集为例,对本文模型的预测有效性以及减少训练时间的有效性进行了实验和分析。结果表明,与传统方法偏最小二乘回归相比,当目标工况为工况1到工况4时,本文方法使平均均方根误差分别降低了30.3%、31.4%、29.3%和24.1%。且相较于传统全连接法,时间近邻法构建拉普拉斯正则项能够使得四个工况上模型训练时间分别降低14.11、1.01、26.43和0.71 s,表明该模型的预测准确性和训练时间均得到有效改善.

基于软测量的化工精馏过程推断控制策略

针对化工精馏过程产品成分无法在线检测及其用温度间接控制产品成分的常规控制策略存在着控制精度低的问题,提出基于软测量的精馏过程成分非线性串级推断控制策略。该控制策略首先提出核岭回归的实时软测量方法,即利用满足Mercer条件的核函数改进线性岭回归算法,实现精馏过程产品成分的在线检测;然后在此基础上,提出一种新的非线性串级推断控制策略,即副环采用常规的温度间接控制,主环采用基于核岭回归软测量的推断控制策略。通过Mejedell等建立的精馏塔动态模型分别对单端和双端成分非线性串级推断控制策略性能进行分析,仿真结果表明,与传统控制方案比较,新控制策略的控制质量有了较大提高,控制结构简单,易于实施。

中国成年人人体尺寸数据相关性研究

本文基于最新的中国成年人人体尺寸数据,开展了人体测量基础项目与5项人体关键尺寸测量项目之间的相关性研究,建立了线性回归方程,为人体尺寸测量项目的优化提供了技术支持。研究成果在人体测量工作中的应用,可有效降低人体测量工作难度,缩短工作时间,减少资金投入,为提高人体尺寸数据更新速度提供帮助。

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Efficient fundamental frequency transformation for voice conversion

In order to improve the performance of voice conversion, the fundamental frequency （F0） transformation methods are investigated, and an efficient F0 transformation algorithm is proposed. First, unlike the traditional linear transformation methods, the relationships between F0s and spectral parameters are explored. In each component of the Gaussian mixture model （GMM）, the F0s are predicted from the converted spectral parameters using the support vector regression （SVR） method. Then, in order to reduce the over- smoothing caused by the statistical average of the GMM, a mixed transformation method combining SVR with the traditional mean-variance linear （MVL） conversion is presented. Meanwhile, the adaptive median filter, prevalent in image processing, is adopted to solve the discontinuity problem caused by the frame-wise transformation. Objective and subjective experiments are carried out to evaluate the performance of the proposed method. The results demonstrate that the proposed method outperforms the traditional F0 transformation methods in terms of the similarity and the quality.

Quality prediction of batch process using the global-local discriminant analysis based Gaussian process regression model

The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR） approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model （HMM） is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced- dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM （HMM-GPR） model.

Settlement Prediction for Buildings Surrounding Foundation Pits Based on a Stationary Auto-regression Model

To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.

Safety and efficacy of Profermin~to induce remission in ulcerative colitis

AIM:To test the efficacy and safety of Profermin in inducing remission in patients with active ulcerative colitis(UC).METHODS:The study included 39 patients with mild to moderate UC defined as a Simple Clinical Colitis Activity Index(SCCAI)>4 and<12(median:7.5),who were treated open-label with Profermintwice daily for 24 wk.Daily SCCAI was reported observer blinded via the Internet.RESULTS:In an intention to treat(ITT)analysis,the mean reduction in SCCAI score was 56.5%.Of the 39 patients,24(62%)reached the primary endpoint,which was proportion of patients with≥50%reduction in SCCAI.Our secondary endpoint,the proportion of patients in remission defined as SCCAI≤2.5,was in ITT analysis reached in 18 of the 39 patients(46%).In a repeated-measure regression analysis,the estimated mean reduction in score was 5.0 points(95%CI:4.1-5.9,P<0.001)and the estimated mean time taken to obtain half the reduction in score was 28 d(95%CI:26-30).There were no serious adverse events(AEs)or withdrawals due to AEs.Profermin was generally well tolerated.CONCLUSION:Profermin is safe and may be effective in inducing remission of active UC.

Mapping methods for output-based objective speech quality assessment using data mining

Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.

Research on Natural Gas Short-Term Load Forecasting Based on Support Vector Regression

Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost.Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.

A Comparative Study of Three Machine Learning Methods for Software Fault Prediction

The contribution of this paper is comparing three popular machine learning methods for software fault prediction. They are classification tree, neural network and case-based reasoning. First, three different classifiers are built based on these three different approaches. Second, the three different classifiers utilize the same product metrics as predictor variables to identify the fault-prone components. Third, the predicting results are compared on two aspects, how good prediction capabilities these models are, and how the models support understanding a process represented by the data.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Analyses and predictions of rock cuttabilities under different confining stresses and rock properties based on rock indentation tests by conical pick

The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vector machine(SVM)and generalized regression neural network(GRNN)were used to find the relationship among rock cuttability,uniaxial confining stress applied to rock,uniaxial compressive strength(UCS)and tensile strength of rock material.It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability,which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material.Based on prediction models for revealing the optimal stress condition and determining the cutting parameters,the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.

Beak Measurements of Octopus (Octopus variabilis) in Jiaozhou Bay and Their Use in Size and Biomass Estimation

Cephalopods play key roles in global marine ecosystems as both predators and preys.Regressive estimation of original size and weight of cephalopod from beak measurements is a powerful tool of interrogating the feeding ecology of predators at higher trophic levels.In this study,regressive relationships among beak measurements and body length and weight were determined for an octopus species(Octopus variabilis),an important endemic cephalopod species in the northwest Pacific Ocean.A total of 193 individuals(63 males and 130 females) were collected at a monthly interval from Jiaozhou Bay,China.Regressive relationships among 6 beak measurements(upper hood length,UHL;upper crest length,UCL;lower hood length,LHL;lower crest length,LCL;and upper and lower beak weights) and mantle length(ML),total length(TL) and body weight(W) were determined.Results showed that the relationships between beak size and TL and beak size and ML were linearly regressive,while those between beak size and W fitted a power function model.LHL and UCL were the most useful measurements for estimating the size and biomass of O.variabilis.The relationships among beak measurements and body length(either ML or TL) were not significantly different between two sexes;while those among several beak measurements(UHL,LHL and LBW) and body weight(W) were sexually different.Since male individuals of this species have a slightly greater body weight distribution than female individuals,the body weight was not an appropriate measurement for estimating size and biomass,especially when the sex of individuals in the stomachs of predators was unknown.These relationships provided essential information for future use in size and biomass estimation of O.variabilis,as well as the estimation of predator/prey size ratios in the diet of top predators.

Ratio K: a New Way of Metering and Evaluating the Risk and Return of Stock Investment

Although widely used, both the Markowitz model and VAR (Value at Risk) model have some limitations in evaluating the risk and return of stock investment. By the analysis of the conceptions of risk and return, together with the three hypotheses of technological analysis, a novelty model of metering and evaluating the risk and return of stock investment is established. The major indicator of this model , risk-return ratio K, combines the characteristic indicators of risk and return. Regardless of the form of the risk-return probability density functions, this indicator K can always reflect the risk-return performances of the invested stocks clearly and accurately. How to use the model to make optimum investment and how to make portfolio combined with clustering analysis is also explained.

Shock Tube Measurement of Ethylene Ignition Delay Time and Molecular Collision Theory Analysis

In this study, 75% and 96% argon diluent conditions were selected to determine the ig- nition delay time of stoichiometric mixture of C2Ha/O2/Ar within a range of pressures （1.3-：3.0 arm） and temperatures （1092-1743 K）. Results showed a logarithmic linear rela- tionship of the ignition delay time with the reciprocal of temperatures. Under both two diluent conditions, ignition delay time decreased with increased temperature. By multiple linear regression analysis, the ignition delay correlation was deduced. According to this correlation, the calculated ignition delay time in 96% diluent was found to be nearly five times that in 75% diluent. To explain this discrepancy, the hard-sphere collision theory was adopted, and the collision numbers of ethylene to oxygen were calculated. The total collision numbers of ethylene to oxygen were 5.99×10^30 s^-1cm^-3 in 75% diluent and 1.53×10^29 s^-1cm^-3 in 96% diluent （about 40 times that in 75% diluent）. According to the discrepancy between ignition delay time and collision numbers, viz. 5 times corresponds to 40 times, the steric factor can

Farmers＇ Perceived Impact of Fair Trade： The Case of Costa Rica

The perceived usefulness of Fair Trade influences both its effectiveness and farmers＇ long-term participation. The aim of this paper is to measure the perceived economic, social and environmental impact of Fair Trade by farmers in Costa Rica. One hundred farmers were interviewed, and their perceived change in living and working conditions due to Fair Trade participation was measured through a t-test analysis. The sample characters＇ influence on the perceived change was also measured, adopting a regression model and a t-test. The results showed a positive perception of the impact of Fair Trade, with a particularly strong perceived improvement in the farmers＇ technical, economic and managerial skills. There was relatively less perceived change in the environmental, educational and sanitary conditions. The results showed the need for Fair Trade to better adjust its strategy to the expectations of the farmers＇ communities.