浅谈在信息时代报刊资源的开发和利用

信息时代的迅猛发展给报刊资源开发和利用带来了前未有的挑战和机遇。如何抓住机会,将报刊信息资源的价值发挥在最大化是图书馆业所面临的主要任务。为此,必须深刻认识报刊信息资源的特性,并且从资源分类、载体建设和经济效益方面促进开发和利用。     

连铸坯质量判定模糊专家系统

鉴于连铸过程中存在众多不确定性,考虑某中厚板厂的实际生产状况与中厚板热送热装的控制要求,以表面纵裂为例,按照模糊专家系统的开发思路,建立起连铸坯纵裂知识库,开发推理机制.采用纵裂指数预测和神经网络预测相结合的方法对连铸坯质量进行离线判断、推理和决策,并给出相应的缺陷产生原因解释.采用VC++6.0软件建立人机界面,数据库采用Oracle,调试结果表明该系统可以达到较高的判定准确率(92.8%),为在线应用奠定了基础.     

基于贝叶斯粗糙集模型的属性约简算法

在分析贝叶斯粗糙集模型已有的约简算法的基础上,从含有多个决策类情况下的全局相对增益函数的角度,利用二进制编码方法给出一种求贝叶斯粗糙集所有约简及核的算法,并基于实际应用,给出对求出的所有约简进行筛选的拓展算法。通过算例说明算法的实用性和有效性。     

基于土地利用变化的城市规划环境影响评价

城市规划的环境影响具有显著的不确定性,难以定量评价。该文将社会经济活动落实到空间网格上,以土地利用变化表征城市规划的实施,集成情景分析和Monte Carlo技术来模拟土地利用变化,并结合不确定性分析技术构建了城市规划环境影响的定量评价方法。对大连市发展规划的地表水环境影响评价进行了方法应用,识别了规划年主要河流的环境风险、环境压力、极端环境影响、关键污染物和关键污染源。该方法可以系统识别城市规划环境影响的不确定性并定量评价其影响,为科学规划提供决策支持。     

区域土地资源遥感监测与时空演变分析

以西南地区宁南、会东、巧家3个县为例,基于遥感技术,利用高分辨率SPOT卫星影像,提取了研究区土地利用现状信息;在地理信息系统技术支持下,结合20世纪80年代末以来多期土地利用数据,统计了研究区土地利用结构变化情况,并对土地资源变化的驱动力进行了分析．在此基础上,根据国家退耕还林、还草的有关政策,结合区域高程、坡度数据,对当地未来耕地资源状况进行了预测．     

课堂教学评估的多层次贝叶斯网络分类器方法

课堂教学是为实现一定的教学目标而展开的信息传递、过程控制和策略实施过程.依据课堂教学的特点给出了课堂教学评估的指标体系,并在此基础上建立了课堂教学评估的层次贝叶斯网络分类器模型.为提高分类器的分类识别准确率,在连续属性中引入形状参数,实验结果显示,通过形状参数的优化能够显著提高分类器的分类识别可靠性.     

理性群体决策的概率集结研究

探讨如何集结多个专家的不确定性信息,以获得群体一致判断．首先分析群体理性存在的可能性;在此基础上,给出了几种集结个体概率的模型及算式,讨论了这些模型的特点,实际应用的可能性;最后,评述了Ｄａｌｋｅｙ个体概率集结不可能性定理中潜在的问题。得出几个研究的结论,并且提出了需要进一步研究的问题。     

哈尔滨市区土地资源的可持续利用问题及对策分析

哈尔滨市土地资源的可持续利用是区域社会经济可持续发展的重要基础和根本保证．     

Treed Avalanche Forecasting: Mitigating Avalanche Danger Utilizing Bayesian Additive Regression Trees

Little Cottonwood Canyon Highway is a dead‐end, two‐lane road leading to Utah's Alta and Snowbird ski resorts. It is the only road access to these resorts and is heavily traveled during the ski season. Professional avalanche forecasters monitor this road throughout the ski season in order to make road closure decisions in the face of avalanche danger. Forecasters at the Utah Department of Transportation (UDOT) avalanche guard station at Alta have maintained an extensive daily winter database on explanatory variables relating to avalanche prediction. Whether or not an avalanche crosses the road is modeled in this paper via Bayesian additive tree methods. Utilizing daily winter data from 1995 to 2011, results show that using Bayesian tree analysis outperforms traditional statistical methods in terms of realized misclassification costs that take into consideration asymmetric losses arising from two types of error. Closing the road when an avalanche does not occur is an error harmful to resort owners, and not closing the road when one does may result in injury or death. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.