TTP/C协议的关键特性研究

数据总线是构建分布式实时容错系统的关键技术之一,而基于TTA架构定义的TTP/C协议是面向安全关键领域的最新总线规范;首先介绍了TTP/C协议的基本架构和运行机制,然后深入研究了基于Welch-Lynch算法的分布式容错同步时钟、基于GMP的容错一致性策略和上电启动过程等TTP/C协议的关键特性;研究结果表明TTP/C协议具有严格的时间确定性和完备的容错策略,十分适合作为航空、汽车、铁路等安全关键领域的通用底层解决方案。     

基于实时通讯的入侵检测系统设计

实时通讯软件可说是现今最热门的网络商品之一,也因此成为网络攻击的主要标的。攻击者利用恶作剧的社交工程手段来欺骗计算机使用者下载能自我快速复制的蠕虫,开启病毒感染的档案或网络,或是在毫无防备的情况下安装恶意程序。因此该研究运用MSN Sniffer与入侵检测系统来防护实时通讯上蠕虫的攻击。     

Low‐voltage and high‐transmittance blue‐phase liquid‐crystal device with slanted electrodes

Abstract— A low‐voltage (～10 V_rms) and high‐transmittance (～90%) polymer‐stabilized blue‐phase liquid‐crystal (BPLC) device with a slanted‐electrodes structure is proposed. Unlike the vertical‐field‐switching (VFS) mode in which oblique incident light and a vertical field are employed, the proposed device utilizes normal incident light and an oblique field. The slanted electrodes generate a strong and uniform oblique electric field, which contributes in obtaining low voltage and high transmittance. Moreover, no couple films or prism sheets are needed, which helps to enhance the optical efficiency and simplify of the device structure. This device has great potential application for emerging BPLC displays and photonic devices.     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.     

Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things

The effective strategy of Internet of Things (IoT) can help firms to grasp the emerging opportunities from the IoT and then improve their competitive advantage. In this article, based on organizational capability perspective, we provide a theoretical framework which classifies IoT strategies into four archetypes from two dimensions of managers’ strategic intent and industrial driving force, and propose that market-based exploratory capabilities play a more important role for firms adopting get-ahead strategy, and market-based exploitative capabilities play a more important role for firms adopting catch-up strategy in market. The technology-based exploratory capabilities play a more important role for firms adopting get-ahead strategy in technology, and technology-based exploitative capabilities play a more important role for firms adopting catch-up strategy in technology. Especially, external industry information sharing more efficiently contributes to the enhancement of both market-based and technology-based exploratory capabilities, and internal industry information sharing more efficiently contributes to the enhancement of both market-based and technology-based exploitative capabilities.     

Learning-enhanced differential evolution for numerical optimization

Differential evolution (DE) is a simple and powerful population-based search algorithm, successfully used in various scientific and engineering fields. However, DE is not free from the problems of stagnation and premature convergence. Hence, designing more effective search strategies to enhance the performance of DE is one of the most salient and active topics. This paper proposes a new method, called learning-enhanced DE (LeDE) that promotes individuals to exchange information systematically. Distinct from the existing DE variants, LeDE adopts a novel learning strategy, namely clustering-based learning strategy (CLS). In CLS, there are two levels of learning strategies, intra-cluster learning strategy and inter-cluster learning strategy. They are adopted for exchanging information within the same cluster and between different clusters, respectively. Experimental studies over 23 benchmark functions show that LeDE significantly outperforms the conventional DE. Compared with other clustering-based DE algorithms, LeDE can obtain better solutions. In addition, LeDE is also shown to be significantly better than or at least comparable to several state-of-art DE variants as well as some other evolutionary algorithms.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

Design optimization of supersonic jet pumps using high fidelity flow analysis

Supersonic jet pumps are simple devices with no moving parts, where a high velocity (primary) flow is used to pump a second fluid. In this paper, Computational Fluid Dynamics (CFD) is combined with an optimization framework in order to develop a tool for the rapid generation of jet pump designs. A key feature of the problem formulation is the transformation of the jet pump design parameters in terms of geometric ratios. This approach dramatically reduces the number of unrealistic designs covered by the Design of Experiments. Optimal Latin Hypercubes for surrogate model building and model validation points are constructed using a permutation genetic algorithm and design points are evaluated using CFD. Surrogate models of primary and entrained flow rates are built using a Moving Least Squares approach. A series of optimizations for various pump sizes are performed using a genetic algorithm and Sequential Quadratic Programming, with responses calculated from the surrogates. This approach results in a set of optimized designs, from which pumps for a wide range of flow rates can be interpolated.     

XVM虚拟环境下并行程序的性能评测

该文引入speedup作为并行程序的性能评测指标,分析了并行程序在不同类型和不同数量的客户虚拟机中运行的性能差异,实验表明,MPI并行程序在xVM虚拟化环境中的运行性能接近非虚拟化本地主机的性能,在半虚拟化环境中的并行程序性能超过全虚拟化环境中的并行程序性能。     

基于虚拟仪器的传感器静态特性测试系统研究

采用虚拟仪器技术,对CSY10B型传感器的输出进行了数据采集和处理,设计了传感器静态性能测试系统。在Windows系统环境下,利用LabVIEW软件编程,构建虚拟仪器,实现了对传感器输出电压的实时测量、显示、记录;同时对其输出电压数据进行数据拟合,确定了其最佳拟合曲线,实现了对传感器的静态特性的自动分析与测定。所提出的传感器静态性能测试系统具有良好的人机交互界面,简单实用。