基于改进RBF的空间插值算法及其在矿体三维可视化中的应用

为径向基神经网络确定更为优化的初始中心,增强径向基网络的性能。通过采用改进的模拟退火蚁群算法作为径向基神经网络径向基层的训练法,将改进的径向基神经网络模型应用于地层高程的面插值和矿体品位的空间体插值,并与普通克里金法进行交叉验证,优化效果明显,然后利用VC++与OpenGL开发环境开发出矿体可视化系统,结果在结合矿体实际数据进行实例应用的过程中,实效性明显。     

Settlement Prediction of Dredger Fill with the Optimal Combination Model

Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model（ i. e., cannot reflect various regulations of settlement at some stages or the entire process）. In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.     

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters（ i. e.,raw material index inputs） have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network（ KBANN） was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set（ RS）-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.     

上市公司财务困境教学案例分析——以民和牧业为例

本文主要以一家上市公司民和牧业(002234)为例,从盈利能力、营运能力、偿债能力、成长能力、现金流量、杠杆系数和非财务指标等方面分析该公司从2009年至2013年的公司发展营运状况,由此判断它在2014年被特别处理(ST)的原因。     

邢台矿建筑物下充填开采沉陷规律的研究

为了保护井巷、建筑物、水体以及铁路等,使它们免受或少受开采的有害影响,减少地下资源的浪费,邢台矿首次采用建筑物下综合机械化充填采煤技术进行工业广场下开采,本文介绍了建筑下综合机械化充填采煤技术的主要思路,针对7606充填工作面建立岩移观测站,总结了开采沉陷规律及预计参数,并通过7608充填工作面对预计参数进行验证。结果显示:预计移动参数选取合理,预计方法正确,预计结果能较好的反映矸石粉煤灰充填开采的地表移动变形规律。     

基于小波分解的统计模型对小时风速的预测

为提高小时风速的预测精度,提出了基于小波分解和AR模型的混合模型(WD-AR).模型应用小波分解技术将风速序列进行多层分解,再利用AR模型分别对各分解层的风速序列进行预测,最后将预测结果叠加得到预测值.采用河西地区风速观测数据对模型进行分析验证,结果表明:WD-AR模型预测精度指标R,RMSE和MAP E值分别是0.89,0.36和27%,与AR模型相比有了较大的改善,提高了小时风速的预测精度,说明WD-AR模型具有更好的预测能力.     

昆明市区域商品住宅价格及影响因素研究

结合历史房价数据及人均生产总值等因素,以昆明市作为研究对象,通过建立GM(1,N)模型对昆明市的商品住宅房价进行了预测,采用理论与实证相结合的方法进行研究,并检验了模型的精度,试图弥补房价与影响其变化的多个经济变量之间的定量关系无法用精确的数学表达式来描述的不足,以期为昆明市的房地产投资决策提供一定的指导和借鉴意义.     

基于修正模态应变能法的硬涂层薄板阻尼性能预估

在考虑硬涂层材料的小阻尼特点以及经典模态应变能法计算误差的前提下,提出了一种适用于硬涂层复合结构阻尼性能预估的新方法.推导了修正模态应变能法的原理性公式.以一个涂敷Mg-Al硬涂层的悬臂薄板为例进行了实例研究,并讨论了硬涂层材料参数、涂层厚度对结构系统阻尼性能的影响.结果发现增大硬涂层的杨氏模量、损耗因子及厚度均可提高复合结构的阻尼性能.相关研究成果可为硬涂层材料制备及阻尼减振设计提供参考.     

Temporal dynamics in social trust prediction

Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the cor- responding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results dem- onstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.     

隧道监控量测方案设计

新奥法经过几十年的发展,已被广泛应用于世界各国隧道工程的施工,其要素分别为喷混凝土、锚杆和监控量测．作为新奥法三要素之一,监控量测在隧道施工中发挥着重要作用．结合某隧道监控量测工程,从监控量测方案设计出发,系统论述了隧道监控量测的目的、方案制定原则、监测项目的选择、监测间距和频率、数据分析处理和信息反馈．在此基础上重点分析了隧道监测项目的选择和数据分析中现有预测方法．