基于伸缩因子的代数曲线曲面变形方法

提出了一种基于伸缩因子的代数曲线曲面变形方法。首先构造了一种用于代数曲线曲面变形的伸缩因子,然后将所构造的伸缩因子分别作用于待变形的代数曲线与曲面,通过改变伸缩因子各参数的取值实现对代数曲线曲面的变形。实例表明,该方法操作方便,易于控制,可获得丰富的变形效果,是一种简单有效的代数曲线曲面变形方法。     

基于ESB的Web服务集成技术

企业服务总线ESB(Enterprise Service Bus)已成为分布式应用集成的关键技术.探讨了ESB的基本功能,提出了面向Web服务的ESB架构,它以SOAP引擎为核心,以应用适配器作为异构系统消息的统一转换接口,用传输适配器监听数据通信协议,实现消息通信,以服务适配器实现Web服务的查找、定位和执行.给出了基于ESB的政务信息资源交换平台解决方案,能适应电子政务资源异构、复杂与动态性,具有良好的可扩展性.     

基于数据库查询的自然语言接口研究

语音识别技术是近年来信息领域重要的科技发展技术之一.作为一门交叉学科,语音识别正逐步成为信息技术中人机接口的关键技术.探讨了为数据库查询提供自然语言接口的可能性,介绍了图书资料查询系统中语音识别的基本框架,并详细描述了采用微软Speech SDK技术实现图书资料查询的详细过程.给出了查询流程,基于状态转换图的词法分析和语法分析方法,将疑问句转换成SQL查询语句的方法,以及由查询结果生成答句的方法.     

基于PIC单片机的AES算法优化设计

现代鉴别技术,涉及对秘密(也就是密钥)的知识检测,可用合适的算法防止密码被破解,AES算法是现在公认的最有安全度和最有效率的算法。本文首先分析了对称密码和公钥密码及其相应的鉴别模型;然后给出了高级加密标准(AES)算法在PIC单片机上的优化,并进行了仿真;最后对AES加密方案的性能做了进一步的分析;在对加密时间要求不高,并且加密数据量很小的场合,使用8位单片机的软件进行优化和实现,可以简化电路,降低成本和功耗。     

Phase equilibria and microstructure development in Mg-rich Mg-Gd-Sr alloys: Experiments and CALPHAD assessment

Mg-Sr alloys are promising to fabricate orthopedic implants. The alloying of rare earth elements such as Gd may improve the comprehensive mechanical properties of Mg-Sr alloys. The information on the phase diagram and the microstructure development are required to design chemical composition and microstructure of Gd alloyed Mg-Sr alloys. The phase equilibria and the microstructure development in Mg-rich Mg-Gd-Sr alloys (Gd, Sr < 30 at. %) are experimentally investigated via phase identification, chemical analysis, and microstructure observation with respect to the annealed ternary alloys. The onset temperatures of liquid formation are measured by differential scanning calorimetry. A thermodynamic database of the Mg-rich Mg–Gd–Sr ternary system is developed for the first time via CALPHAD (CALculation of PHAse Diagram) approach assisted by First-Principles calculations. The thermodynamic calculations with the developed database enable a well reproduction of the experimental findings and the physical-metallurgical understanding of the microstructure formation in solidification and annealing.     

Thermodynamic assessment of RuO4 oxide

RuO₄ oxide appears much less stable than RuO₂(s) in the Ru–O binary system with a melting point close to room temperature and a certain propensity to vaporize or decompose at low temperatures. Ab initio simulations in the framework of density functional theory (DFT) on RuO₄(s) are performed to analyze the cubic and monoclinic structures and to evaluate the heat capacities at low temperatures. Then, a critical evaluation of thermodynamic data from calorimetry and vapor pressure determinations - was carried out coupled with ab-initio calculations to propose new thermodynamic data: the entropy.S° (RuO_4, s, cubic, 298K) = 132.7 J·K⁻¹mol⁻¹ and formation enthalpy.Δ_fH° (RuO₄, s, cubic, 298K) = −252.4 ± 5.5 kJ mol⁻¹.     

A note on the learning automata based algorithms for adaptive parameter selection in PSO

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection of value for inertia weight and acceleration coefficients. In the first class, particles of a swarm use the same parameter values adjusted by learning automata. In the second class, each particle has its own characteristics and sets its parameter values individually. In addition, for both classed of proposed algorithms, two approaches for changing value of the parameters has been applied. In first approach, named adventurous, value of a parameter is selected from a finite set while in the second approach, named conservative, value of a parameter either changes by a fixed amount or remains unchanged. Experimental results show that proposed learning automata based algorithms compared to other schemes such as SPSO, PSOIW, PSO-TVAC, PSOLP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better solutions. In addition, proposed algorithms converge to stopping criteria for some of the highly multi modal functions significantly faster.     

基于递归和表函数的一到多数据转换研究

针对关系表达式难以进行无限制一到多数据转换的问题,通过关系代数的扩展表达一到多数据转换,采用递归查询和表函数实现无限制一到多数据转换,在每个输入元组上产生一个或者多个输出元组。递归查询通过创建初始结果,递归获取结果集,并返回最终结果集;表函数声明变量集合,使用过程体和游标循环访问表,迭代输出元组。实验分析不同参数影响因素下有限制和无限制转换的不同方法,结果表明扩展方法能够改善系统性能。     

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.