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真、善、美:人类和谐存在的三重维度

安徽大学学报(哲学社会科学版) , 2009,
Abstract: 真、善、美统一于人的存在之中。人的存在是真、善、美的存在,又是和谐的存在。真是人的认识之和谐,善是人的道德之和谐,美是人的情感之和谐。因此,真、善、美的统一也就是人的存在之和谐,也可以说是人的存在之最高和谐。在现实中,我们总是能发现真善美存在着事实上的不统一,所以对真善美的解读与实践也就成了达到和谐的必要环节。
卡西尔对文化悲观论的批判

安徽大学学报(哲学社会科学版) , 2007,
Abstract: 西方社会自近代以来,在理性主义精神影响下,一方面物质文明高度发达,但另一方面又弥漫着一种强烈的文化悲观论,而这种文化悲观论恰恰又与理性主义精神的文化传统密切相关。现代著名的德国哲学家卡西尔论证了文化与自然是辩证关系,对人类文化的发展作出了辩证的批判和考察,从而得出了人类的文化是不断更新、不断发展的乐观主义结论,有力地批判了文化悲观论。
基于支持向量机方法的葡萄酒质量预测研究
Research on Wine Quality Prediction Based on Support Vector Machine Method
 [PDF]


Hans Journal of Data Mining (HJDM) , 2016, DOI: 10.12677/HJDM.2016.61006
Abstract:
随着人们生活水平不断的提高,葡萄酒越来越受到人们的喜爱。葡萄酒的产量越来越大。然而葡萄酒质量鉴定手段还是仅靠品酒师的人工品尝打分来判定葡萄酒质量的好坏,显然这种鉴定方式难以满足当今市场的需求。现在有不少学者运用数据挖掘中的一些算法(比如Logistic多项模型,人工神经网络,支持向量机,决策树,Bagging,AdaBoost,最近邻方法等算法)来对葡萄酒质量进行预测研究,其结果并不是很好(误判率均在15%以上),但相对于仅靠品酒师的人工品尝打分来判定,其结果还是较为可靠的,通过前人的研究可以知道仅仅简单使用支持向量机中的常见核函数,并不能很好的预测葡萄酒质量,因此本文基于支持向量机方法的核函数进行修改,主要将支持向量机方法中常见核函数进行线性组合而得到新的核函数。本文通过使用UCI数据库中的“Wine Quality Data Set”的数据来验证本文所提出的方法与数据挖掘常用的算法进行对比,通过十折交叉验证的方法来判断方法的好坏。
With the continuous improvement of people’s living standards, wine has become more and more popular among people. Wine production is growing. However, the quality of the wine is still only determined by wine tasters’ grading, which is obviously difficult to meet the needs of today’s market. Many scholars use data mining algorithms (such as Logistic multinomial model, artificial neural network, support vector machine, decision tree, Bagging, AdaBoost, nearest neighbor algo-rithm) to predict the wine quality. The results are not very good. The results are reliable and can be used to support vector machine (SVM), which can be used to predict the quality of wine. In this paper, we use the Quality Data Set UCI data to verify the proposed method and the data mining al-gorithm for comparison, using ten-fold cross validation method to determine the quality of the method.
我国老龄化人口的影响因素分析
Analysis on the Influencing Factors of Aging Population in China
 [PDF]


Aging Research (AR) , 2016, DOI: 10.12677/AR.2016.32002
Abstract:
随着人类文明不断地进步,人类的医疗水平和生活质量也不断地提高,导致人类的平均寿命不断增长以及其他各方面原因,使得人类老龄化问题越来越严重,尤其是我国老龄化问题。有不少学者提出人口出生率、死亡率、自然增长率这三个因素是影响人口老龄化的主要因素,本文基于这三个主要因素基础,添加一些其他因素来构建一个多元回归分析来定量分析我国老龄化人口数与哪些因素有关。本文一方面定量分析我国老龄化人口的变化趋势与我国人口出生率和我国人均GDP之间的关系,另一方面也论证了我国今年年初实行的放开“二胎”政策的必要性和合理性。本文最后给出一些合理的建议来应对我国老龄化问题。
With the continuous progress of human civilization, human health and quality of life continue to improve, so that the average human life expectancy is growing, which makes the human aging problem is becoming more and more serious, especially the aging problem in China. Many scho-lars put forward that the birth rate, death rate and natural growth rate are the main factors in-fluencing aging. Based on the three main factors, this paper adds some other factors to build a multiple regression analysis to make a quantitative analysis of the reasons of aging population. In this paper, on the one hand the quantitative analysis of the relationship between the aging population trend, birth rates and per capita GDP is conducted. On the other hand, it also demonstrates the necessity and rationality of the “two-child” policy released earlier this year. Finally, we give some reasonable suggestions to deal with the problem of aging.
基于ARIMA模型的中国老龄化人口预测研究
Research on the Prediction of China’s Aging Population Based on ARIMA Model
 [PDF]


Aging Research (AR) , 2016, DOI: 10.12677/AR.2016.31001
Abstract:
随着中国人民的生活水平达到小康水平,精神物质得到进一步保障,人口平均寿命逐渐向后延迟。人口老龄化问题必然出现,相继的问题也随之出现。这样的问题对于一个发展中国家来讲是非常有挑战性的,也是非常棘手的。本文利用ARIMA模型来对中国老龄化人口进行短期预测,通过对老龄人口的短期预测,我们可以发现目前我国的老龄化形势相当严峻,同时针对目前我国所面临的人口老龄化所产生的一些社会问题提供了一些政策性的建议。
With the Chinese people’s living standards achieving a well-off level, the spiritual material is fur-ther protected, and the average life expectancy is extended gradually. Hence aging population appears inevitably, and other problems emerge with it. It is quite a challenge for a developing country, which is very difficult to deal with. In this paper, the ARIMA model is used to forecast the China’s aging population in a short term. We can find that the current situation of China’s population ageing is quite severe, and some policy suggestions are proposed aimed at solving the social problems currently produced from China’s aging population.
基于最小二乘支持向量机的森林火灾预测研究
Prediction of Forest Fires Based on Least Squares Support Vector Machine
 [PDF]

, 费宇
Hans Journal of Data Mining (HJDM) , 2016, DOI: 10.12677/HJDM.2016.61003
Abstract:
森林火灾是一个主要的环境问题,造成经济损失和生态破坏而且危及生命。如何预测、防治或减少森林火灾的危害成为诸多学科领域共同关注的科学任务。传统的做法是使用卫星,红外线扫描仪和局部传感器。但是由于卫星定位的延迟和扫描仪高昂的设备成本和维护成本,这些方案不能用来解决所有的情况。然而,研究表明气象因素对森林火灾有重要的影响。因此,有不少的学者建立森林火灾预测系统并将气象数据纳入量化指标体系。随机计算机的迅速发展,不少的学者将机器学习的方法运用到森林火灾等级预测模型中,但是其预测效果并不十分理想。本文提出基于机器学习中支持向量机方法的改进方法-最小二乘支持向量机,由于最小二乘支持向量机对处理样本容量较小的数据具有较高的准确度而且耗时较短。本文选用UCI数据库中的森林火灾数据进行预测处理,选用高斯函数(径向基函数)作为最小二乘支持向量机的核函数,根据一对一的多分类算法设计出最小二乘支持向量机的多分类器,使用粒子群算法选择最优参数。最后与支持向量机、BP神经网络、决策树等方法进行对比。
Forest fire is a major environmental problem, resulting in economic loss and ecological damage, and endangering life. How to predict, prevent or reduce the damage of forest fire has become a scientific task of many disciplines. The traditional approach is to use a satellite, an infrared scanner, and a local sensor. However, due to the delay of the satellite positioning and the high cost of the scanner’s equipment and maintenance costs, these solutions can not be used to solve all the situation. However, the study shows that the meteorological factors have an important influence on forest fire. Therefore, many scholars have established system for forest fire prediction and the meteorological data into the quantitative index system. With the rapid development of random computer, many scholars have applied the method of machine learning to forest fire grade prediction model, but the effect is not very ideal. This paper presents an improved method of support vector machine method based on machine learning, because the least squares support vector machine is with a higher accuracy and shorter time consuming to process small sample size of the data. In this paper, we select the UCI database of forest fire forecast data processing, select Gaussian function (radial basis function) as the kernel function of least squares support vector machine, according to one of multiple classification algorithm design of least squares support vector machine classifier, using particle swarm optimization algorithm to choose the optimal parameters. Finally, it is compared with the support vector machine, BP neural network, decision tree and so on.
锚泊列阵的设计与研究
马鉴,
海洋工程 , 1996,
Abstract: 虽然随着海洋开发和浮水作业的需要而出现了动力定位的船舶和海上建筑,但锚泊装置作为定位用途,因其较好的经济性而广泛用于水深不大的海域的海上结构物的定位中。本文根据国内外专家对锚泊装置的研究成果,以布设了具有复杂地形地貌海域的10kW潮流实验电站为例,探讨针对复杂海底地形的锚泊定位系统的设计方法,并给出完整的设计步骤
聚2-乙烯吡啶及其季铵盐铑(Ⅰ)化合物的甲醇羰化合成乙酸的催化性能
小宝,,蒋大智
催化学报 , 1996,
Abstract: ?
单轴压力下煤样表面电位实验
艳娜,,
煤炭学报 , 2009,
Abstract: 建立了煤岩变形破裂过程表面电位测试实验系统,研究了不同试样单轴压力下变形破裂过程中的表面电位特征规律.结果表明,原煤、型煤和混凝土在受单轴压力变形破裂过程中都有表面电位产生,并且表面电位在整个变形破裂过程中始终存在.从总体上看,表面电位和载荷的变化趋势一致;载荷突变时,表面电位信号也出现突变,表面电位的突变幅度和载荷的突变幅度成正相关.
碳复合载体负载铑催化剂的制备及其对气相甲醇羰基合成乙酸的催化反应性能
小宝,,田世忠,蒋大智
催化学报 , 1997,
Abstract: ?
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