Lexical state analyzer for JavaCC grammars

Lexical states in JavaCC provide a powerful mechanism to scan regular expressions in a context sensitive manner. But lexical states also make it hard to reason about the correctness of the grammar. We first categorize the related correctness issues into two classes: errors and warnings. We then extend the traditional context sensitive and a context insensitive analysis to identify errors and warnings in context‐free grammars. We have implemented these analyses as a standalone tool (LSA ), the first of its kind, to identify errors and warnings in JavaCC grammars. The LSA tool outputs a graph that depicts the grammar and the error transitions. Importantly, it can also generate counter example strings that can be used to establish the errors. We have used LSA to analyze a host of open‐source JavaCC grammar files to good effect. Copyright © 2015 John Wiley & Sons, Ltd.     

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

Current research on design knowledge capture and reuse has predominantly focused on either the codification view of knowledge or the personalisation view of knowledge, resulting in a failure to address designers’ knowledge needs caused by a lack of context of information and insufficient computational support. Precisely motivated by this gap, this work aims to address the integration of these two views into a complete, contextual and trustworthy knowledge management scheme enabled by the emerging collaborative technologies. Specifically, a knowledge model is developed to represent an integrated knowledge space, which can combine geometric model, knowledge-based analysis codes and problem-solving strategies and processes. On this basis, a smart collaborative system is also designed and developed to streamline the design process as well as to facilitate knowledge capture, retrieval and reuse as users with different roles are working on various tasks within this process. An engineering case study is undertaken to demonstrate the idea of collaborative knowledge creation and sharing and evaluate the effectiveness of the knowledge representation model and the collaborative technologies employed. As evidenced in the development and evaluation, the methods proposed are effective for capturing an integrated knowledge space and the collaborative knowledge management system not only facilitates problem-solving using knowledge-based analysis but also supplies in-context tacit knowledge captured from the communications between users throughout the design process.     

A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications

Human context recognition (HCR) from on-body sensor networks is an important and challenging task for many healthcare applications because it offers continuous monitoring capability of both personal and environmental parameters. However, these systems still face a major energy issue that prevent their wide adoption. Indeed, in healthcare applications, sensors are used to capture data during daily life or extended stays in hospital. Thus, continuous sampling and communication tasks quickly deplete sensors’ battery reserves, and frequent battery replacement is not convenient. Therefore, there is a need to develop energy-efficient solutions for long-term monitoring applications in order to foster the acceptance of these technologies by the patients. In this paper, we survey existing energy-efficient approaches designed for HCR based on wearable sensor networks. We propose a new classification of the energy-efficient mechanisms for health-related human context recognition applications and we review the related works in detail. Moreover, we provide a qualitative comparison of these solutions in terms of energy-consumption, recognition accuracy and latency. Finally, we discuss open research issue and give directions for future works.     

Bounded H∞ synchronization for time‐varying networks with probability‐dependent information

This paper addresses the bounded H_∞ synchronization problem for the time‐varying coupled networks with stochastic noises and randomly occurring nonlinearities over a finite horizon. The bounded H_∞ synchronization performance constraint is proposed to quantify the degree of the synchronization regarding the exogenous disturbances. The nonlinearities considered in this paper are assumed to satisfy the sector‐like conditions and characterized by a time‐varying Bernoulli distribution with measurable probability in real time. Based on the Kronecker product and the Hadamard product, a sufficient condition is established firstly to ensure the bounded H_∞ synchronization of the network by utilizing the probability‐dependent method. Then the obtained criterion is further converted into a computationally available one by transforming the time‐varying probability into a polytopic form, which is presented in terms of matrix inequalities and hence can be verified easily by applying the Matlab toolbox. Finally, simulation examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.     

面向普适计算的动态自适应中间件模型

为了提高普适计算系统的开发效率, 设计了一个基于OSGi框架的动态普适计算中间件模型. 该中间件模型以OSGi框架为基础, 建立移动管理器管理用户和服务的移动, 利用上下文管理器来管理上下文, 动态调整自己的行为, 支持上下文感知应用. 通过标准的接口实现各种异构普适设备间的互操作性. 实验结果表明该中间件能够满足通用普适计算环境的要求, 对于普适计算系统的开发具有一定的借鉴意义.     

考虑切换时间的多品种印刷电路板表面贴装生产线调度优化

考虑了多品种小批量生产环境下的印刷电路板生产调度优化问题,建立了以包括切换时间在内的多品种平均流程时间最小化为目标的调度优化模型.针对电了产品组装独特的切换后效性,开发了主动禁忌搜索算法,分组间搜索和组内搜索2个层次对印刷电路板的排序问题进行优化.通过与理论下界值比较的仿真实验显示了模型与算法的有效性及现实应用的可行性...     

序列模糊概念格模型及其渐进式构造

传统的序列模式挖掘主要是挖掘满足最小支持度的频繁序列,没有考虑序列在实际中的重要度。为了能够有效地挖掘重要的序列模式,提出了一种序列模糊概念格模型,对所有序列的项目引入了重要度权值,定义了序列的重要度及可以动态调整最小支持度minsup的自适应系数;扩展了模糊形式背景,使其能够方便地表示序列,定义了概念的Galois闭包连接、序列模糊概念及序列模糊格结构,并给出了序列模糊概念格的渐进式构造算法 ScqFuzCL。实验表明,序列模糊概念格模型可以方便有效地组织自适应序列模式,在时间与空间上都具有良好的性能,并为进一步挖掘自适应序列模式提供了理论支持。     

基于个性化情境和项目类别的资源推荐研究

传统的协同过滤推荐技术没有考虑影响用户评分的用户情境信息,但最近研究发现用户个性化情境信息直接影响着用户评分,因此在传统的协同过滤技术基础上引入用户个性化情境后推荐效果有所提高。此外可以将用户个性化情境和项目类别相结合起来。先对项目进行分类,然后再确定用户在每个项目类别下的个性化情境,同一项目类别下所有项目的用户个性化情境是相同的。在为目标项目预测评分时,先确定目标项目所在的类别,进而确定计算目标项目预测评分所用到的用户个性化情境。实验结果表明,改进后的算法较Slope one有较大提高。     

上下文无关语言分析树的一种表示形式

介绍了上下文无关语言（ＣＦＬ）的句子的一种分析树表示,它适用用于一类与以往不同的ＣＦＬ的应用,即对分析树空间效率要求较高且不需标记分析树的应用,典型的就是把ＣＦＬ的句子用作算法加工对象,这种表示比传统分析树不仅空间较小,而且进行结构匹配的快速快,还介绍了这种分析树表示的实现技术。     

面向上下文无关语言的测试工具的设计和实现

软件测试是软件开发生命周期的重要组成部分,也是目前和今后相当长一段时间内保证软件质量和可靠性的主要手段,首先以两个已经实现了的分别面向VB和C/C 语言的计算机辅助软件测试工具为基础,将该系列测试工具的系统结构推广至适用于任何一种上下文无关语言。而该系列测试式工具高度的模块可重用性特征为这种推广提供了必要的基础。为实现这种高度的模块可重用性特征。从各个角度论述了增强该特征的设计,提出了基于块的程序划分机制。这种机制从根本上保证了对多种上下文无关语言的测试语义的一致性,最后,还给出了两个不同语言写就的实例的运行结果,以说明这种一致性。