基于双种群的小生境差分进化算法

将非线性方程组的求解问题转化为函数优化问题,当方程组有多个解时,它的适应值函数就是具有多个最优解的多峰函数.为此,提出了基于双种群的小生境差分进化算法.在该算法中,进化在两个不同的子群间并行进行,通过使用不同的变异策略,实现种群在解空间具有尽可能分散的探索能力的同时在局部具有尽可能细致的搜索能力.通过子群重组实现子群间的信息交换,然后引入小生境淘汰机制.对典型测试函数的优化结果表明,该算法能找到全部解,而且精度好.     

基于ISM模型的行业收入差距成因仿真分析

在社会经济体系研究中,从系统结构和仿真的技术人手,在综合利用SPSS等软件的基础上,通过矩阵运算,测算出行业收入差距基尼系数和泰尔指数,发现行业收入差距不断扩大.根据经济学和系统工程理论筛选影响行业收入差距的因素,构建ISM模型,对行业收入差距成因系统进行测试,通过T检验优化模型和ISM层次筛选法得出主要原因.综合分析得出结论,微观因素行业收入差距产生和扩大的主要原因,经济体制市场化改革足差距产生和扩大的重要环境因素.     

A learning classifier system for mazes with aliasing clones

Maze problems represent a simplified virtual model of the real environment and can be used for developing core algorithms of many real-world application related to the problem of navigation. Learning Classifier Systems (LCS) are the most widely used class of algorithms for reinforcement learning in mazes. However, LCSs best achievements in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons for failure. Moreover, there is a lack of knowledge of what makes a maze problem hard to solve by a learning agent. To overcome this restriction we try to improve our understanding of the nature and structure of maze environments. In this paper we describe a new LCS agent that has a simpler and more transparent performance mechanism. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability to Associative Perception, adopted from psychology. We then assess the new LCS with Associative Perception on an extensive set of mazes and analyse the results to discover which features of the environments play the most significant role in the learning process. We identify a particularly hard feature for learning in mazes, aliasing clones, which arise when groups of aliasing cells occur in similar patterns in different parts of the maze. We discuss the impact of aliasing clones and other types of aliasing on learning algorithms.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Applying ergonomics to systems: some documented "lessons learned"

Based on evidence accumulated during the author's 45 years of professional experience, the author presents 23 important “lessons learned” regarding applying ergonomics to systems. Documented results from reported cases or other evidence are presented to validate each of these practical learning points.     

Stability analysis of a class of digital filters utilizing single saturation nonlinearity

A novel criterion for the global asymptotic stability of a class of digital filters utilizing single saturation nonlinearity is presented. An example showing the effectiveness of the present criterion is given.     

外语类院校《操作系统》课程设置及教学探索

由于学生的生源特征、知识背景和所处大环境的不同,外语类学校中<操作系统>的教学并非易事,是专业教学中的难点.为了能够让学生掌握操作系统的有关知识,应正视与理工院校存在的差异和距离,在课程设王、教学方法教学组织、实践环节等方面进行了探索,取得了一定效果.     

汽车修理信息管理系统设计

在面向对象分析设计的实际项目案例中,针对在实际应用中仍是以关系型数据库为主要存储管理方式的状况,提出从对象类模型导出关系数据库表模型的4条原则,并应用在修车管理信息系统的研发中,取得较好的效果.     

Ruminations of an Editor-in-Chief

The Journal of Strategic Information Systems (JSIS) was first published December 1991 by Robert (Bob) D. Galliers, its Founding Editor-in-Chief (EiC). Early on Bob invited me to join the JSIS International Editorial Board¹ (the Board then being more operational, with members serving as quasi-AEs, an arrangement we’ve reinstituted more recently). I became Pacific Asia Region Editor in 2003, with JSIS shifting from region editors to Senior Editors in 2007, in which role I served the journal through the end of 2018. Mid-2018 Bob and Sirkka (Sirkka Jarvenpaa joined as co-EiC in 2000) jointly decided it was time; and announced they would both step down from their co-EiC roles at the end of that year. In September 2018, I was endorsed by Bob and Sirkka, the Senior Editors and Elsevier, to succeed Bob and Sirkka and assume the role of Editor-in-Chief of JSIS commencing 1 January 2019. I served as Editor-in-Chief through 1 July 2021 after which Yolande Chan and I served as co-Editors-in-Chief. Now the end of 2022, I am stepping down from operational involvement with the journal. In this my final editorial I focus on my time as EiC, also reflecting on my almost 3 decades with the journal. I much appreciate being invited to write these brief ruminations and am honoured to be included amongst the journal’s Emeritus, along with the esteemed Robert Galliers and Sirkka Jarvenpaa.     

一种新型高速动态液压加载装置

介绍了一种自行设计的用于高速齿轮试验台的新型液压加载装置,阐明了该装置的结构及工作原理．对影响加载装置的转矩和效率的几个主要参数进行了公式推导和分析．