铁路机车检修计划生成系统的研究

基于模糊理论分析了机车在运行过程中的多种影响因子对机车磨损的影响,对机车检修周期的基本影响因子J、走行公里相关因素Z以及动态因素D进行了深入研究,引入机车当量公里的数学模型,制定出合理的检修计划,建立了铁路机车检修计划生成系统.该系统为机车检修周期提供了合理的依据,大大提高了机车在运行中的经济效益.     

基于观测数据的数学模型预测性检验理论

基于显著性检验的意义及其存在的问题,提出了评价经验模型预测性的观点与方法,从理论与实践两方面探讨有关的统计检验手段,进而提出预测性检验理论.所涉方法用于化工装置的建模实践,实际效果良好.在其他领域,同样具有理论和实践的指导意义.     

基于模糊理论的非处方药的满意度评价系统

为了帮助顾客从众多对症的非处方药中选出最满意的药品,提出了开发1个基于Web的非处方药满意度评价系统。系统根据顾客提供的典型症状,采用疾病与症状相结合的选药策略,查找出所有对症的非处方药,并以药品安全性、疗效、稳定性、易用性、价格作为评价因素,根据顾客的关注角度和关注程度确定各因素权重,用模型b=W·R对对症药品进行满意度评价,按满意度高低依次推荐。经实际应用,系统推荐准确度在70%以上。     

用Mathematica解热量传递过程中的数学问题

本文建议用数学软件Mathematica计算热量传递过程,包括用符号运算功能推导保温层临界直径,用曲线拟合法处理传热实验数据,用积分法求取传热系数为温度函数时的换热面积,用Solve等命令求解换热和保温过程所产生的非线性方程,用DSolve命令求解非稳态传热过程产生的微分方程等等。同时绘制出有关函数图形,准确、形象地展示计算过程和结果,方便教师讲解和学生理解,避免复杂、繁琐、耗时的手工计算,提高学生利用计算机解决热量传递问题的能力。     

拓扑距离特征指数对多氯联苯的QSPR研究

根据分子拓扑理论,提出1种新的多氯联苯(PCBs)化合物分子中,顶点原子(基团)点价值δ_i的计算方法,据以构建分子的结构参数——拓扑距离特征指数(TDEI);同时考虑分子体积以及氯原子在苯环上的位置和数目对PCBs性质的影响,引入分子体积参数(V_R)和基团定位指数(D),然后,采用多元线性回归法,分别建立了PCBs的正辛醇-水分配系数(lgK_(OW))和水溶解度(-lgS_w)的定量结构-性质相关模型,其相关系数分别达到0.9972和0.9730,均优于前人的工作。利用模型预测另外7个PCBs分子的lgK_(OW)和-lgS_W,预测值与实验值的一致性令人满意。     

Single image super-resolution using global enhanced upscale network

Current works on super-resolution have obtained satisfactory results since the advance of the convolution neural network. Nevertheless, most previous works use one network for one integer scale factor so ignore the super-resolution of the arbitrary scale factor. In this work, we propose a novel approach called Global Enhanced Upscale Network (GEUN) to tackle super-resolution with a single model adapting the arbitrary scale factor. In our GEUN, we propose the Global Enhanced Upscale module to replace the conventional upscale module. Our GEUN can upscale low-resolution images with an arbitrary scale factor through only one model. Extensive experimental results demonstrate the superiority of our GEUN.

     

Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Computational Economics - The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory...     

Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

Computational Economics - Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other...     

The impact of industrial agglomeration on industrial pollutant emission: evidence from China under New Normal

The purpose of this study is to analyze the environmental pollution effects elicited by industrial agglomeration and to devise necessary changes before and after China going into the New Normal, a contemporary phase of less rapid but more sustainable economic development. An empirical model is constructed based on the Copeland–Taylor model, and empirical research is conducted using statistical panel data derived from 285 Chinese cities between 2003 and 2014. To study the relationship between industrial agglomeration and industrial pollutant emission both before and after the ‘New Normal,’ the sample data are divided into two time periods: 2003–2008 and 2009–2014. Estimated results are as follows. First, industrial agglomeration exacerbates industrial pollution levels overall although the negative environmental effect of industrial agglomeration is weakened following China’s entry into the New Normal phase of economy. Second, both the interaction term of industrial agglomeration and foreign direct investment (FDI) and the interaction term of industrial agglomeration and environmental regulation are negatively related to industrial agglomeration. These findings indicate that FDI and environmental regulation can indirectly reduce industrial pollutant emissions by way of industrial agglomeration.