Time-dependent reliability analysis through projection outline-based adaptive Kriging

Structural and Multidisciplinary Optimization - Time-dependent reliability analysis (TRA) has drawn much attention due to its ability in measuring the probability that a system or component keeps...     

WWW-based reliability information system

This paper discusses the reliability information system developed with the concept of world wide web (WWW) and client/server architecture. An existing server and client for the system are modified to perform HTTP duties and graphically advanced reliability analysis. The technical background and the relationship of the WWW and client/server system is explained. With the developed system, the design engineers and reliability analysts can analyze the reliability of the system more quickly and conveniently.     

Quantifying stimulus discriminability: a comparison of information theory and ideal observer analysis

Performance in sensory discrimination tasks is commonly quantified using either information theory or ideal observer analysis. These two quantitative frameworks are often assumed to be equivalent. For example, higher mutual information is said to correspond to improved performance of an ideal observer in a stimulus estimation task. To the contrary, drawing on and extending previous results, we show that five information-theoretic quantities (entropy, response-conditional entropy, specific information, equivocation, and mutual information) violate this assumption. More positively, we show how these information measures can be used to calculate upper and lower bounds on ideal observer performance, and vice versa. The results show that the mathematical resources of ideal observer analysis are preferable to information theory for evaluating performance in a stimulus discrimination task. We also discuss the applicability of information theory to questions that ideal observer analysis cannot address.     

Quantifying design quality through design experiments

The authors present a model for design quality metrics, discuss its relevance, and give some examples of use. Design experiments demonstrate error data extraction and analysis. Using a model of the design process for electronic products that emphasizes the resulting quality of the design, the authors demonstrated that they can quantify design quality. They can best express the probability of an error-free design in terms of the quality of the synthesis process and the quality of the verification procedure     

Feature analysis through information granulation and fuzzy sets

Feature analysis and feature selection are fundamental pursuits in pattern recognition. We revisit and generalize an issue of feature selection by introducing a mechanism of soft (fuzzy) feature selection. The underlying idea is to consider features to be granular rather than numeric. By varying the level of granularity, we modify the level of contribution of the specific feature to the overall feature space. We admit an interval model of the features meaning that their values assume a form of numeric intervals. The intervalization of the features exhibits a clear-cut interpretation. Moreover a contribution of the features to the formation of the feature space can be easily controlled: the broader the interval, the less essential contribution of the feature to the entire feature space. In limit, when the intervals get broad enough, one may view the feature to be completely eliminated (dropped) from the feature space. The quantification of the features in terms of their importance is realized in the setting of the clustering FCM model (namely, a process of the binary or fuzzy feature selection is carried out and numerically quantified in the space of membership values generated by fuzzy clusters). As the focal point of this study concerns an interval-like form of information granules, we reveal how such feature intervalization helps approximate fuzzy sets described by any type of membership function. Detailed computations give rise to a detailed quantification of such granular features. Numerical experiments provide a comprehensive numerical illustration of the problem.     

Quantifying information security risks using expert judgment elicitation

In the information security business, 30 years of practical and theoretical research has resulted in a fairly sophisticated appreciation for how to judge the qualitative level of risk faced by an enterprise. Based upon that understanding, there is a practical level of protection that a competent security manager can architect for a given enterprise. It would, of course, be better to use a quantitative approach to risk management, but, unfortunately, sufficient quantitative data that has been scientifically collected and analyzed does not exist. There have been many attempts to develop quantitative data using traditional quantitative methods, such as experiments, surveys, and observations, but there are significant weaknesses apparent in each approach. The research described in this paper was constructed to explore the utility of applying the well-established method of expert judgment elicitation to the field of information security. The instrument for eliciting the expert judgments was developed by two information security specialists and two expert judgment analysis specialists. The resultant instrument was validated using a small set of information security experts. The final instrument was used to elicit answers to both the calibration and judgment questions through structured interviews. The data was compiled and analyzed by a specialist in expert judgment analysis. This research illustrates the development of prior distributions for the parameters of models for cyber attacks and uses expert judgment results to develop the distributions.     

Quantifying software performance,reliability and security: An architecture-based approach

With component-based systems becoming popular and handling diverse and critical applications, the need for their thorough evaluation has become very important. In this paper we propose an architecture-based unified hierarchical model for software performance, reliability, security and cache behavior prediction. We employ discrete time Markov chains (DTMCs) to model software systems and provide expressions for predicting the overall behavior of the system based on its architecture as well as the characteristics of individual components. This approach also facilitates the identification of various bottlenecks. We illustrate its use through some case studies and also provide expressions to perform sensitivity analysis.     

Estimating reliability parameters under insufficient information

The paper presents an algorithm to search for the lower bound of the Bayesian estimate of the parameter of exponential distribution in the case where it is known that a priori distribution belongs to the class of all distribution functions with two equal quantiles. This problem arises in sensivity analysis of Bayesian estimates of failure rates to the choice of a priori distribution in the exponential failure model.     

Stress-strength reliability models under incomplete information

A new approach to compute structural reliability is proposed. The novelty of the approach is that initial statistical information concerning the stress and strength of engineered structures is partial. Different numerous cases of state of knowledge about the stress and strength are analysed. A set of canonical analytical expressions for computing imprecise structural reliability has been obtained and a few examples are presented. The reliability models developed are generalisations of a conventional one.     

Quantifying worker interactions and characterizing worker performance through ubiquitous IoT

Cloud technologies and artificial intelligence are transforming call centers into intelligent relationship hubs. Such transformation requires call-center employees to be strategically connected based on the distribution of expertise. However, today’s organizations lack a real-time method to agilely and pervasively map knowledge-distribution and optimize information flow. We present a pilot study showing that the interactions captured by Zigbee and infrared (IR) Internet of things (IoT) sensors on sociometric badges (a business-card-sized printed circuit board) worn by 36 employees in a call center can be used to characterize interactions and their impact on employee performance. Specifically, we quantify an employee’s centrality, weak ties, and strong ties from sensor network data and analyze the effects on average task-processing time. Further analysis reveals insights into interactions among workers that were previously limited by coarse qualitative data in survey studies. This study points to the potential of a “living lab” approach for investigating the effects of employee interactions and behaviors on their performance quantitatively in the real word using ubiquitous IoT.     

Improving chip multiprocessor reliability through code replication

Chip multiprocessors (CMPs) are promising candidates for the next generation computing platforms to utilize large numbers of gates and reduce the effects of high interconnect delays. One of the key challenges in CMP design is to balance out the often-conflicting demands. Specifically, for today’s image/video applications and systems, power consumption, memory space occupancy, area cost, and reliability are as important as performance. Therefore, a compilation framework for CMPs should consider multiple factors during the optimization process. Motivated by this observation, this paper addresses the energy-aware reliability support for the CMP architectures, targeting in particular at array-intensive image/video applications. There are two main goals behind our compiler approach. First, we want to minimize the energy wasted in executing replicas when there is no error during execution (which should be the most frequent case in practice). Second, we want to minimize the time to recover (through the replicas) from an error when it occurs. This approach has been implemented and tested using four parallel array-based applications from the image/video processing domain. Our experimental evaluation indicates that the proposed approach saves significant energy over the case when all the replicas are run under the highest voltage/frequency level, without sacrificing any reliability over the latter.     

Urban parking space reservation through bottom-up information provision: An agent-based analysis

The aim of this paper is to study the impacts of a reservation system for on-street parking. Such a system provides drivers looking for on-street parking with information on available parking spaces, thereby possibly reducing the need to cruise for parking and the accompanying negative externalities. The performance of the proposed system is studied using a highly detailed spatial agent-based simulation. The results of the simulations show that users of a reservation system benefit in terms of reduced search time and reduced walking distance under virtually all simulated circumstances. However, societal benefits are not as clear-cut. The benefit in search time for the users of the system comes at a cost to the regular drivers, which see a nearly identical increase in search time. In contrast, the positive impact on walking distance hardly influences walking distance for regular drivers. Hence, we conclude that the introduction of a reservation system for on-street parking results in a more efficient distribution of available parking spaces among drivers searching for parking.     

Software reliability analysis and choice of software reliability criteria

Functional reliability of computer software is considered using fuzzy automaton representation of software systems.Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 46–60, March–April, 1992.     

Transforming business through information technology

Information technology has made major strides this past decade, improving significantly the process of doing business. These changes have permeated every aspect of our lives from grocery shopping to banking to manufacturing to managing a profitable business. And the trend is expected to accelerate as we move into the new millennium     

Friendly information retrieval through adaptive restructuring of information space

Although a technique of relevance feedback is common in the field of information retrieval (IR), the feedback is usually done by means of query refinement; restructuring of the information space has not been attempted yet. The restructuring not only allows useful applications such as clustering but also is indispensable for IR if a modeling function employs correlation of terms. In this paper we present a new method of relevance feedback through the restructuring of the information space. Our method adapts document space to the user’s mental model by manipulating a dictionary vector. Therefore, user’s viewpoint is preserved after a series of retrieval processes and reused for retrieval performed later. We show its effectiveness through the retrieval experiments on FAQ (Frequntly Asked Questions) documents. Tomoko Murakami: She obtained her bachelor’s degree in Engineering from Aoyama Gakuin University in 1996, and her master’s degree in Media and Governance from Keio University in 1998. In 1998 she joined Human Interface Labolatory, Corporate Research & Development Center, Toshiba Corporation, Kawasaki, Japan. Her research interests are in Machine Learning, especially Inductive Logic Programming. She is a member of JSAI. Ryohei Orihara, Ph.D.: He is a research scientist at Human Interface Laboratory, Corporate Research & Development Center, Toshiba Corporation. He obtained his bachelor’s degree and master’s degree in Engineering and Ph.D. from University of Tsukuba in 1986, 1988 and 1999 respectively. His current research interests include machine learning, creativity support system, analogical reasoning and metaphor understanding. He was a visiting researcher at University of Toronto from 1993 to 1995. He is a member of IPSJ, JSAI and JSSST. He is presently on the editorial committee of the Journal of JSAI.     

基于可信度信息的改进加权比特翻转算法

在研究几种加权比特翻转算法的基础上,提出了一种新的针对LDPC码的改进加权比特翻转算法。加权比特翻转（WBF）算法中的错误度量考虑了校验节点的可信度信息,在此基础上,相关的改进WBF（IWBF）算法考虑了消息本身对符号判决的影响,进一步提高了性能。但是在IWBF算法中,必须通过仿真,才能获得使译码性能较优的符号可信度加权参数。提出了一种同时考虑符号可信度和校验可信度的算法,不需要调整加权参数,即可获得较优性能。仿真显示提出的加权比特翻转算法是可行且有效的。     

基于遗传算法的信息系统可靠性优化设计

设备冗余是信息系统进行可靠性优化设计的常用策略之一,其主要问题在于冗余设备的选择和配置,以达到满足一定可靠性要求下实现成本最小化的目的。这是一类结构复杂的规划问题,很难采用传统的数值算法进行求解,遗传算法提供了有效的解决方法。首先运用信息系统Petri网模型的层次结构分析结果,给出区分结点重要度的系统可靠性度量公式。在此基础上提出优化模型,给出遗传算法求解优化问题的步骤,并通过实例证明了方法的有效性及实用性。