站城融合模式下既有铁路车站城市功能开发体量预测研究

针对站城融合模式,提出对既有铁路车站及其影响区域的城市功能开发体量预测方法.考虑车站周边道路交通剩余能力和城市规划中对土地容积率的要求,协调枢纽城市功能和交通功能体量的比例关系,建立铁路既有车站城市功能的体量预测模型,并利用商业求解器对模型求解,得到各业态开发总体量的范围.以重庆沙坪坝综合交通枢纽城市功能开发为例,检验求解结果与实际开发体量的契合度及有效性.分析枢纽交通功能占比和容积率的敏感性,验证模型的适用性.计算结果表明,本文方法能有效利用道路承载能力,在适应城市整体发展要求的情况下,实现枢纽和土地的一体化发展,为投资者加强土地集约开发提供新思路.

基于土地利用系数的光伏发电并网体量预测研究

近十几年来,光伏发电已成为全球范围内增长最快的能源方式,是众多国家控制碳排放量的重要手段。首先分析了我国光伏发电的现状,分析了光伏产业对土地利用的矛盾和建筑物发展光伏的政策;再次研究了综合考虑光伏产业补贴成本的来源,对集中和分布式光伏并网的收益情况进行对比;最后构建了基于土地利用系数的光伏容量预测模型,计算出一定建筑用地发展光伏的体量。

SVM method for predicting the thickness of sandstone

The Support Vector Machine （SVM） method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the actual sample data by using the SVM method. As a result, the function not only gets a higher fit precision but is also better generalized. The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon. The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology, fluid composition, and formation thickness. It directly predicts sandstone thickness using the seismic waveform. This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction. Model examples and actual applications show the applicability of this method.

Quantitative analysis of geological ore-controlling factors and stereoscopic quantitative prediction of concealed ore bodies

To address the issues for assessing and prospecting the replaceable resource of crisis mines, a geological ore-controlling field model and a mineralization distribution field model were proposed from the viewpoint of field analysis. By dint of solving the field models through transferring the continuous models into the discrete ones, the relationship between the geological ore-controlling effect field and the mineralization distribution field was analyzed, and the quantitative and located parameters were extracted for describing the geological factors controlling mineralization enrichment. The method was applied to the 3-dimensional localization and quantitative prediction for concealed ore bodies in the depths and margins of the Daehang mine in Guangxi, China, and the 3-dimensional distribution models of mineralization indexes and ore-controlling factors such as magmatic rocks, strata, faults, lithology and folds were built. With the methods of statistical analysis and the non-linear programming, the quantitative index set of the geological ore-controlling factors was obtained. In addition, the stereoscopic located and quantitative prediction models were set up by exploring the relationship between the mineralization indexes and the geological ore-controlling factors. So far, some concealed ore bodies with the resource volume of a medium-sized mineral deposit are found in the deep parts of the Dachang Mine by means of the deep prospecting drills following the prediction results, from which the effectiveness of the predication models and results is proved.

Dynamic prediction of gas emission based on wavelet neural network toolbox

This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.

Development of a multivariate empirical model for predicting weak rock mass modulus

Estimating weak rock mass modulus has historically proven difficult although this mechanical property is an important input to many types of geotechnical analyses. An empirical database of weak rock mass modulus with associated detailed geotechnical parameters was assembled from plate loading tests per- formed at underground mines in Nevada, the Bakhtiary Dam project, and Portugues Dam project. The database was used to assess the accuracy of published single-variate models and to develop a multivari- ate model for predicting in-situ weak rock mass modulus when limited geoteehnical data are available. Only two of the published models were adequate for predicting modulus of weak rock masses over lim- ited ranges of alteration intensities, and none of the models provided good estimates of modulus over a range of geotechnical properties. In light of this shortcoming, a multivariate model was developed from the weak rock mass modulus dataset, and the new model is exponential in form and has the following independent variables： （1） average block size or joint spacing, （2） field estimated rock strength, （3） dis- continuity roughness, and （4） discontinuity infilling hardness. The multivariate model provided better estimates of modulus for both hard-blocky rock masses and intensely-altered rock masses.

Advances in gas content based on outburst control technology in Huainan, China

The sudden and violent nature of coal and gas outbursts continues to pose a serious threat to coal mine safety in China. One of the key issues is to predict the occurrence of outbursts. Current methods that are used for predicting the outbursts in China are considered to be inadequate, inappropriate or impractical in some seam conditions. In recent years, Huainan Mining Industry Group(Huainan) in China and the Commonwealth Scientific and Industrial Research Organisation(CSIRO) in Australia have been jointly developing technology based on gas content in coal seams to predict the occurrence of outbursts in Huainan. Significant progresses in the technology development have been made, including the development of a more rapid and accurate system in determining gas content in coal seams, the invention of a sampling-while-drilling unit for fast and pointed coal sampling, and the coupling of DEM and LBM codes for advanced numerical simulation of outburst initiation and propagation. These advances are described in this paper.

Analytical solution of coal-bed methane migration with slippage effects in hypotonic reservoir

Using theoretical analysis, the single-phase gas seepage mathematical model influenced by slippage effects was established. The results show that the pressure of producing wells attenuates more violently than the wells without slippage effects. The decay rate of reservoir pressure is more violent as the Klinkenherg factor increases. The gas prediction output gradually increases as the Klinenberg factor increases when considering gas slippage effects. Through specific examples, analyzed the law of stope pore pressure and gas output forecast changing in a hypotonic reservoir with slippage effects. The results have great theoretical significance in the study of the law of coal-bed methane migration in hypotonic reservoirs and for the exploitation of coal-bed methane.

PRELIMINARY PROGRAMMED WHOLE-BODY COUNTER

It is capable of giving the initial intakes of radionuclides and the assessment quantities used in radiation protection according to its measured results of radionuclides in vivo. It is accomplished by providing the software of controlling, interface and internal dose estimation programs to the original iron cabin shielding whole-body counter. The preliminary application shows that its data processing is rapid and correct, and can meet the requirement of rapid internal radioactive contamination monitoring and diagnosing in case of lots of internal contamination subjects happened innuclear accident.

Exploration of Urinary Creatinine to Determine the Carcass and Its Protein Weight in Beef Cattle

This study was aimed to explore the use of creatinine as indicator to predict carcass and its protein weight in beef cattle. Eight Ongole crossbred cattle with initial body weight ranged at 133.5-228 kg and age of 6-18 months were used in this study. The cattle were fed Napier grass ad libitum and concentrate feeding for three months prior to be slaughtered. Concentrate feeding was consisted of rice bran and soybean meal which was provided to fulfill dry matter requirement at 2.1% of body weight （BW）. The availability of creatinine for prediction indicator was done by evaluate the correlation between the amount of daily urinary creatinine and the carcass and its protein weight. Carcass and its protein weight were measured by slaughtering the cattle, and chemically analyzed for determining protein content of carcass. The results showed that creatinine excreted in urine have a strong correlation with the cattle body weight （r = 0.88）, carcass weight （r = 0.67）, body protein （r = 0.70） and carcass protein （r = 0.72）. The conclusion of this study is creatinine excreted in urine have a strong relationship with the carcass and its protein, and therefore could be used to predict the carcass and its protein weight of beef cattle.

Application of Artificial Neural Network in Predicting Crop Yield： A Review

Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that affecting yields with the crop performances. The application of neural network to the task of solving non-linear and complex systems is promising. This paper presents a review on the use of artificial neural network （ANN） in predicting crop yield using various crop performance factors. General overview on the application of ANN and the basic concept of neural network architecture are also presented. From the literature, it has been shown that ANN provides better interpretation of crop variability compared to the other methods.

Diffusivity Models and Greenhouse Gases Fluxes from a Forest,Pasture,Grassland and Corn Field in Northern Hokkaido,Japan

Information on the most influential factors determining gas flux from soils is needed in predictive models for greenhouse gases emissions. We conducted an intensive soil and air sampling along a 2 000 m transect extending from a forest, pasture, grassland and corn field in Shizunai, Hokkaido （Japan）, measured CO2, CH4, N20 and NO fluxes and calculated soil bulk density （Pb）, air-filled porosity （fa） and total porosity （Ф）. Using diffusivity models based on either fa alone or on a combination of fa and 4, we predicted two pore space indices： the relative gas diffusion coefficient （Ds/Do） and the pore tortuosity factor （T）. The relationships between pore space indices （Ds/Do and T） and C02, CH4, N2O and NO fluxes were also studied. Results showed that the grassland had the highest Pb while fa and Ф were the highest in the forest. CO2, CH4, N20 and NO fluxes were the highest in the grassland while N20 dominated in the corn field. Few correlations existed between fa, Ф, Pb and gases fluxes while all models predicted that Ds/Do and T significantly correlated with CO2 and CH4 with correlation coefficient （r） ranging from 0.20 to 0.80. Overall, diffusivity models based on fa alone gave higher Ds/Do, lower τ, and higher R2 and better explained the relationship between pore space indices （Ds/Do and τ） and gases fluxes. Inclusion of Ds/Do and τ in predictive models will improve our understanding of the dynamics of greenhouse gas fluxes from soils. Ds/Do and τ can be easily obtained by measurements of soil air and water and existing diffusivity models.

A SVM-kNN method for quasar-star classification

We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.

Prediction of fish body's passive visco-elastic properties and related muscle mechanical performance in vivo during steady swimming

For attaining the optimized locomotory performance of swimming fishes,both the passive visco-elastic properties of the fish body and the mechanical behavior of the active muscles should coordinate with the fish body’s undulatory motion pattern.However,it is difficult to directly measure the visco-elastic constitutive relation and the muscular mechanical performance in vivo.In the present paper,a new approach based on the continuous beam model for steady swimming fish is proposed to predict the fish body’s visco-elastic properties and the related muscle mechanical behavior in vivo.Given the lateral travelling-wave-like movement as the input condition,the required muscle force and the energy consumption are functions of the fish body’s visco-elastic parameters,i.e.the Young’s modulus E and the viscosity coefficient in the Kelvin model.After investigating the variations of the propagating speed of the required muscle force with the fish body’s visco-elastic parameters,we analyze the impacts of the visco-elastic properties on the energy efficiencies,including the energy utilization ratios of each element of the kinematic chain in fish swimming and the overall efficiency.Under the constraints of reasonable wave speed of muscle activation and the physiological feasibility,the optimal design of the passive visco-elastic properties can be predicted aiming at maximizing the overall efficiency.The analysis is based on the small-amplitude steady swimming of the carangiform swimmer,with typical Reynolds number varying from 2.5×104to 2.5×105,and the present results show that the non-dimensional Young’s modulus is 112±34,and the non-dimensional viscosity coefficient is 13 approximately.In the present estimated ranges,the overall efficiency of the swimming fish is insensitive to the viscosity,and its magnitude is about 0.11±0.02,in the predicted range given by previous study.