由于高速移动,车车、车路通信信道存在较大多普勒频移,同时接收信号到达角不符合均匀分布。针对该特点,采用了两种方法进行信道建模,一种是采用R.von Mises提出的概率密度谱函数对多径散射信号到达移动接收机的角度进行建模,另一种是通...由于高速移动,车车、车路通信信道存在较大多普勒频移,同时接收信号到达角不符合均匀分布。针对该特点,采用了两种方法进行信道建模,一种是采用R.von Mises提出的概率密度谱函数对多径散射信号到达移动接收机的角度进行建模,另一种是通过研究接收机和发送机之间相对运动进行建模。采用自回归模型法,根据不同的到达角平均方向、发射机和接收机速度比率和到达角宽,建立了不同的车车信道。仿真结果表明所建立的信道理论值与仿真值基本一致,同时揭示了3个参数对车车、车路信道模型二阶特性的影响。为了进一步验证不同信道模型对车车、车路通信的影响,搭建了下一代智能交通通信协议IEEE 802.11p系统测试平台,结果表明在最大多普勒频移为790 Hz、信噪比为5 d B时,简单二维各向异性散射信道比AKKI信道的系统误比特率低17.17 d B,三种信道的误比特率随着移动速度、调制阶数的提高而提高。仿真结果为研究智能交通系统稳定通信建立了基础。展开更多
The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed ...The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.展开更多
There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow ...There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.展开更多
The longwall mining method is often affected by the out-of-seam dilution （OSD）. Therefore, predicting and controlling of dilution are important factors for reducing mining costs. In this study, the fuzzy set theory ...The longwall mining method is often affected by the out-of-seam dilution （OSD）. Therefore, predicting and controlling of dilution are important factors for reducing mining costs. In this study, the fuzzy set theory and multiple regression models with parameters, including variation in seam thickness, dip of seam, seam thickness, depth of seam, and hydraulic radius as inputs to the models were applied to pre- dict the OSD in the longwall coal panels. Field data obtained from Kerman and Tabas coal mines, lran were used to develop and validate the models. Three indices including coefficient of determination （R2）, root mean square error （RMSE） and variance account for （VAF） were used to evaluate the perfor- mance of the models. With 10 randomly selected datasets, for the linear, polynomial, power, exponential, and fuzzy logic models, R2, RSME and VAF are equal to （0.85, 4.4, 84.4）, （0.61, 7.5, 59.6）, （0.84, 4.5, 72.7）, （0.80, 4.1, 79.6）, and （0.97, 2.1, 95.7）, respectively. The obtained results indicate that the fuzzy logic model predictor with R2 = 0.97, RMSE = 2.1, and VAF = 95.7 performs better than the other models.展开更多
In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive ...In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.展开更多
Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast a...Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.展开更多
文摘由于高速移动,车车、车路通信信道存在较大多普勒频移,同时接收信号到达角不符合均匀分布。针对该特点,采用了两种方法进行信道建模,一种是采用R.von Mises提出的概率密度谱函数对多径散射信号到达移动接收机的角度进行建模,另一种是通过研究接收机和发送机之间相对运动进行建模。采用自回归模型法,根据不同的到达角平均方向、发射机和接收机速度比率和到达角宽,建立了不同的车车信道。仿真结果表明所建立的信道理论值与仿真值基本一致,同时揭示了3个参数对车车、车路信道模型二阶特性的影响。为了进一步验证不同信道模型对车车、车路通信的影响,搭建了下一代智能交通通信协议IEEE 802.11p系统测试平台,结果表明在最大多普勒频移为790 Hz、信噪比为5 d B时,简单二维各向异性散射信道比AKKI信道的系统误比特率低17.17 d B,三种信道的误比特率随着移动速度、调制阶数的提高而提高。仿真结果为研究智能交通系统稳定通信建立了基础。
文摘The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.
文摘There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.
文摘The longwall mining method is often affected by the out-of-seam dilution （OSD）. Therefore, predicting and controlling of dilution are important factors for reducing mining costs. In this study, the fuzzy set theory and multiple regression models with parameters, including variation in seam thickness, dip of seam, seam thickness, depth of seam, and hydraulic radius as inputs to the models were applied to pre- dict the OSD in the longwall coal panels. Field data obtained from Kerman and Tabas coal mines, lran were used to develop and validate the models. Three indices including coefficient of determination （R2）, root mean square error （RMSE） and variance account for （VAF） were used to evaluate the perfor- mance of the models. With 10 randomly selected datasets, for the linear, polynomial, power, exponential, and fuzzy logic models, R2, RSME and VAF are equal to （0.85, 4.4, 84.4）, （0.61, 7.5, 59.6）, （0.84, 4.5, 72.7）, （0.80, 4.1, 79.6）, and （0.97, 2.1, 95.7）, respectively. The obtained results indicate that the fuzzy logic model predictor with R2 = 0.97, RMSE = 2.1, and VAF = 95.7 performs better than the other models.
基金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) for support of this project
文摘In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.
基金Indian Institute of Technology,Kharagpur in India for supporting this work
文摘Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.
