我国古代造园手法在现代城市园林规划设计中的应用

中国造园有着悠久的历史和高深的造诣,在世界造园史上,它独树一帜,并对东、西方造园业产生了一定的影响。随着我国城市化进程的加快,如今的人们越来越渴望自然、亲近自然。如果我们敢于求教于先人,能够将这些先人们的造园法则运用于现代城市园林规划设计中的话,必将会为我国现代城市园林规划设计带来极大的帮助。     

微电子器件温度分布测试技术研究

为改善微电子器件性能,提高可靠性,确定筛选、考核条件等都必须对器件的结温、表面温度分布以及热斑等热特性进行精确测量,红外热像法是当前测量微电子器件热特性最有效的方法之一。文章针对InfrascopeⅡ型显微红外热像仪在实际应用中如何提高其测温准确度和一致性等问题进行了研究,重点分析了样品发射率、背景环境和大气衰减等因素的影响,并给出验证解决方案,取得了满意的结果。     

基于Flash ActionScript 3.0的动画设计的研究

分析了ActionScript 3.0动画编程的原理,讲解了ActionScript 3.0中通过Event.ENTER_FRAME事件和Ti merEvent.TI MER事件产生动画的方法。ActionScript 3.0动画编程要涉及到数学、物理等方面的内容,介绍了物理定律与数学知识相结合,建立物体运动的模型,再通过代码来实现的方法。     

基于灰度共生矩阵和聚类方法的木材缺陷识别

文章提出了一种新的基于灰度共生矩阵的木材纹理特征提取和聚类分析的木材缺陷识别方法。该方法基于灰度共生矩阵,提取5个具有代表性的纹理特征：能量（E）,惯性矩（I）,均值和（SOA）,聚类阴影（SOC）,方差和（SOV）,实现数据降维,对产生的特征数据集分别利用k-means算法及AP算法进行聚类,自动找出并正确标识木材表面缺陷位置,并对比分析不同木材表面缺陷类型识别效率。实验结果表明,该方法能快速有效地进行木材表面缺陷自动识别。     

Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital

“Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment.This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.     

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

A data warehouse is an important decision support system with cleaned and integrated data for knowledge discovery and data mining systems. In reality, the data warehouse mining system has provided many applicable solutions in industries, yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. To improve the overall data warehouse mining process, we present an intelligent data warehouse mining approach incorporated with schema ontology, schema constraint ontology, domain ontology and user preference ontology. The structures of these ontologies are illustrated and how they benefit the mining process is also demonstrated by examples utilizing rule mining. Finally, we present a prototype multidimensional association mining system, which with intelligent assistance through the support of the ontologies, can help users build useful data mining models, prevent ineffective pattern generation, discover concept extended rules, and provide an active knowledge re-discovering mechanism.     

Evaluating the influence of E-marketing on hotel performance by DEA and grey entropy

This study evaluated the hotels’ websites in Taiwan from an Internet marketing perspective. Content analysis was used to analyze and compare the marketing practice on Internet. DEA and grey entropy were used to analyze the impact of Internet marketing on hotel performance. The result showed that Internet marketing can affect the operating performance of tourist hotels. Hoteliers should adopt a more strategic Internet approach to increase business success.     

Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy wavelet support vector classifier machine to diagnosing the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory, wavelet analysis theory and v-support vector classifier machine, fuzzy wavelet v-support vector classifier machine (FWv-SVCM) is proposed. To seek the optimal parameters of FWv-SVCM, genetic algorithm (GA) is also applied to optimize unknown parameters of FWv-SVCM. A diagnosing method based on FWv-SVCM and GA is put forward. The results of the application in car assembly line diagnosis confirm the feasibility and the validity of the diagnosing method. Compared with the traditional model and other SVCM methods, FWv-SVCM method requires fewer samples and has better diagnosing precision.     

A new linguistic MCDM method based on multiple-criterion data fusion

Multiple-criteria decision-making (MCDM) is concerned with the ranking of decision alternatives based on preference judgements made on decision alternatives over a number of criteria. First, taking advantage of data fusion technology to comprehensively consider each criterion data is a reasonable idea to solve the MCDM problem. Second, in order to efficiently handle uncertain information in the process of decision making, some well developed mathematical tools, such as fuzzy sets theory and Dempster Shafer theory of evidence, are used to deal with MCDM. Based on the two main reasons above, a new fuzzy evidential MCDM method under uncertain environments is proposed. The rating of the criteria and the importance weight of the criteria are given by experts’ judgments, represented by triangular fuzzy numbers. Then, the weights are transformed into discounting coefficients and the ratings are transformed into basic probability assignments. The final results can be obtained through the Dempster rule of combination in a simple and straight way. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.     

Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of Gaussian noises and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating fuzzy theory, Gaussian loss function and v-support vector classifier machine, the fuzzy Gaussian v-support vector regression machine (Fg-SVCM) is proposed. To seek the optimal parameters of Fg-SVCM, the modified genetic algorithm (GA) is also applied to optimize parameters of Fg-SVCM. A diagnosing method based on Fg-SVCM and GA is put forward. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Fg-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other v-SVCMs.