选择催化还原(SCR)脱硝技术在中国燃煤锅炉上的应用(下)

针对燃煤电厂采用高尘选择催化还原(SCR)脱硝装置时可能遇到的问题进行分析,并对设计SCR反应器和选择适合的催化剂方面提出了建议。在选择催化剂时,针对中国煤,要考虑催化剂活性位潜在积灰以及飞灰中高CaO含量所引起的催化剂的失活。以台山电厂600MW机组和阳城电厂600MW机组及美国肯塔基州Elmer Smith电站150MW机组的SCR脱硝工程为例进行了说明。     

函数的温度参数对人工神经网络的影响

对人工神经网络的激励函数作了一些研究。用遗传算法优化各神经元Ｓｉｇｍｏｉｄ函数的温度参数来解决‘异或’问题，取得良好效果。在大量试验基础上总结了一些规律。表明了激励函数对于网络学习性能的重要作用。     

Maximum entropy versus minimum risk and applications to some classical discrete distributions

The game which can be taken to lie behind the maximum-entropy principle is studied. Refining previous techniques, new theoretical results are obtained. These results are illustrated by concrete examples pertaining to well-known classical models.     

地源热泵空调及生活热水系统调试监控

以某工程的地源热泵空调及生活热水系统调试的有关监控要求为例,从生活热水系统运行要求、系统调试监控要点及其调试结果分析等方面介绍了调试监控的方法,即为确保该系统满足设计要求的节能减排目标,应先进行分区、分子系统调试,合格后再联合调试,实时总结调试期间出现的不足并及时修正,为类似工程的调试监控工作提供借鉴。     

Refinements of Pinsker's inequality

Let V and D denote, respectively, total variation and divergence. We study lower bounds of D with V fixed. The theoretically best (i.e., largest) lower bound determines a function L=L(V), Vajda's (1970) tight lower bound. The main result is an exact parametrization of L. This leads to Taylor polynomials which are lower bounds for L, and thereby to extensions of the classical Pinsker (1960) inequality which has numerous applications, cf. Pinsker and followers.     

乳粉中蛋白质含量测定结果的不确定度评定

通过建立数学模型,分析乳粉中蛋白质含量测定的不确定度及其影响因素.同时对各分量标准不确定度也进行了分析和计算,得出了样品消耗HCI标准溶液引入的标准不确定度、空白消耗HCI标准溶液引入的标准不确定度以及试验重复测量导致的标准不确定度是不确定度来源的最主要影响因素.最终通过合成和扩展得出了乳粉中蛋白质含量测定的扩展不确定度为(21.7±0.15)%.     

选择催化还原(SCR)脱硝技术在中国燃煤锅炉上的应用(上)

针对燃煤电厂采用高尘选择催化还原(SCR)脱硝装置时可能遇到的问题进行分析,并对设计SCR反应器和选择适合的催化剂方面提出了建议。在选择催化剂时,针对中国煤要考虑催化剂活性位潜在积灰,以及飞灰中高CaO含量所引起的催化剂的失活。以台山电厂600 MW机组和阳城电厂600 MW机组及美国肯塔基州Elmer Smith电站150 MW机组的SCR脱硝工程为例进行了说明。     

AVI在多媒体技术中的应用研究

通过分析AVI视频格式的结构、分类和特点,综合考虑多媒体视频的特点,详细论述AVI在多媒体技术中的应用及意义。AVI是将语音和影像同步组合在一起的文件格式,是视频数据和音频数据交叉编码的一种RIFF文件,其多用于音视频捕捉、编辑、播放等,其文件名后缀为.avi。AVI在多媒体技术中的应用主要是制作多媒体光盘,用来保存电视、电影等各种影像信息。     

Inequalities between entropy and index of coincidence derived frominformation diagrams

To any discrete probability distribution P we can associate its entropy H(P)=-Σp_i ln p_i and its index of coincidence IC(P)=Σp_i². The main result of the paper is the determination of the precise range of the map P→(IC(P), H(P)). The range looks much like that of the map P→(P_max, H(P)) where P_max is the maximal point probability, cf. research from 1965 (Kovalevskij (1965)) to 1994 (Feder and Merhav (1994)). The earlier results, which actually focus on the probability of error 1-P_max rather than P_max, can be conceived as limiting cases of results obtained by methods presented here. Ranges of maps as those indicated are called information diagrams. The main result gives rise to precise lower as well as upper bounds for the entropy function. Some of these bounds are essential for the exact solution of certain problems of universal coding and prediction for Bernoulli sources. Other applications concern Shannon theory (relations between various measures of divergence), statistical decision theory, and rate distortion theory. Two methods are developed. One is topological; the other involves convex analysis and is based on a “lemma of replacement” which is of independent interest in relation to problems of optimization of mixed type (concave/convex optimization)