ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

A hybrid trust‐enhanced collaborative filtering recommendation approach for personalized government‐to‐business e‐services

The information overload on the World Wide Web results in the underuse of some existing e‐government services within the business domain. Small‐to‐medium businesses (SMBs), in particular, are seeking “one‐to‐one'' e‐services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e‐governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust‐enhanced recommendation approach to provide personalized government‐to‐business (G2B) e‐services, and in particular, business partner recommendation e‐services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user‐based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust‐enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user‐based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold‐start users. © 2011 Wiley Periodicals, Inc.     

端到端冗余流量消除技术的指纹选择算法研究

为了兼顾服务器端的内存消耗和有效性,在分析了现有端到端冗余流量消除技术中的指纹选择算法后,提出了贪婪指纹选择算法。该算法认为两个数据包中相同片段的邻接片段很有可能也是相同的,基于这一原理,邻接片段的指纹也将被选为代表指纹,有效地避免了在服务器端缓存整个数据包,大大减少了服务器端的内存消耗。仿真实验结果表明,该算法与现有的指纹选择算法相比,能够大大减少服务器端和用户终端的内存消耗,同时冗余消除率在10%-50%之间,与现有算法相近,证明了该算法的有效性。     

CCA-secure unidirectional proxy re-encryption in the adaptive corruption model without random oracles

Up to now, it is still an open question of how to construct a chosen-ciphertext secure unidirectional proxy re-encryption scheme in the adaptive corruption model. To address this problem, we propose a new unidirectional proxy re-encryption scheme, and prove its chosen-ciphertext security in the adaptive corruption model without random oracles. Compared with the best known unidirectional proxy re-encryption scheme proposed by Libert and Vergnaud in PKC’08, our scheme enjoys the advantages of both higher effi...     

HALT技术在变频器中的应用初探

HALT技术可在产品设计阶段快速消除设计缺陷,缩短研发时间和成本,迅速提高产品的质量和固有可靠性。本文从HALT试验的背景出发,介绍了HALT试验的基本原理和意义。在对HALT箱各方面性能验证的基础上,针对我司11kW变频器,详细讲述了HALT试验的具体操作过程。     

利用VC++绘制XMAC各向异性图的实现方法

简要介绍了XMAC各向异性图的成图原理及算法设计。根据成图原理建立了算法的数学模型,用VC++编程具体实现了各向异性图的绘制,并给出了实现的关键步骤。通过与eXpress软件比较绘图的效果,证明了给出的绘制各向异性图的方法,绘图效果达到了工程实际应用的要求,能够满足生产实际需要,并已在GeoSpace解释平台中应用。     

C语言在化工原理离心泵特性曲线测定中的应用

离心泵特性曲线的测定是化工原理的重要内容,涉及到伯努利方程和连续性方程在流体流动中的实际应用,公式复杂,计算量大.本文利用C语言的强大结构化编程和计算功能,处理了“离心泵特性曲线的测定”实验数据,给出了程序设计的思路和方法,得到了离心泵的特性曲线.从曲线可以看出,随着流量的增大,离心泵的压头减少,轴功率升高,和教材完全一致.而离心泵的效率随流量增大而增大,并未出现先增大后减小的情况,可能是由于流量较小所导致.通过分析曲线可以得到离心泵操作相关的知识,离心泵必须在流量较小的区域工作才能获得较大的压头,大流量的离心泵需要配置大功率的电机,离心泵启动之前应关闭出口阀等.此外还发现压力表和真空表的读数随流量的增大而变小,解释了此现象.该程序结构简单,维护方便,通过简单修改可用于处理其它化工原理实验的数据,充分发挥了C语言的特长来解决化工原理计算复杂的特点.