Top PDF Program Analysis for Performance and Reliability

Program Analysis for Performance and Reliability

Program Analysis for Performance and Reliability

Fuzzy sets was introduced to model vaguely defined concepts such as “tall”, “warm” etc. In contrast to classical sets, element membership is gradual in [0,1] rather than bi- nary in {0,1}. Fuzzy logic define logical connectives on fuzzy sets to reason about truth in the presence of vagueness. Our fuzzy program analysis framework compute the fix-point of a system of fuzzy logic formulas. By using weighted average as collector function, to weight information from different control paths, the results represent the common value. Here the weights are constants and can be deduced from profile runs or provided from other source. This approach allowed us to find opportunities classical frameworks would miss, e.g. statements that very likely were loop invariant because their operands was very unlikely to update inside the loop. Furthermore using an anal- ysis on second-order fuzzy sets (i.e., fuzzy sets of fuzzy sets) we showed that we can separate inaccuracies introduced due to the framework from that which is given to the framework. Measuring the uncertainty of the analysis results makes it easier to know when to rely on the results and justify a speculative optimization. Finally the result from the fuzzy program analysis framework can be used in a fuzzy regulator to improve the accuracy of the analysis online. The adaptive neuro fuzzy inference system (AN- FIS) regulator consists of a set of fuzzy IF-THEN rules which are weighted to produce classifications. The antecedent of the rules denotes the fuzzy regions where the conse- quent applies and is the result from our static analysis. The consequent is a polynomial function that initially is set to a default value but can be updated dynamically using polynomial least square regression. As far as we know this is the first program analysis approach that rely on fuzzy classifiers.
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A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

Steps 1 and 2 were performed in a previous study (Di Pasquale et al., 2017b), where a systematic literature review in the field of human error in maintenance was conducted following the guidelines defined by Pires, Sénéchal, Deschamps, Loures and Perroni (2015) and Neumann, Kolus and Wells (2016). The aim was to identify and select peer-reviewed papers that provided evidence on the relationship between maintenance activities and human performance, addressing several research questions: (1) What are the industrial sectors mainly investigated in the field of interest? (2) What are the main causes and contributing factors that lead to HEs in maintenance? (3) What are the main HEM consequences? (4) How is HE evaluated and integrated within the maintenance management? A set of keywords structured in Group A, which includes ‘human error’, ‘human reliability analysis’, ‘human reliability assessment’ and ‘human error probability’, and in Group B, which includes ‘maintenance’, was prepared and used to search all the papers in two scientific databases (Scopus and Web of Science). In order to achieve the final list of keywords used in the search, the keywords of each group were linked with the Boolean operator OR, whereas all groups were linked to each other with the Boolean operator AND to make the relationship among groups.
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Performance and Reliability Analysis of a Mobile Robot Using Cara Fault Tree

Performance and Reliability Analysis of a Mobile Robot Using Cara Fault Tree

Reliability engineering has a wide scope and thus can be used in almost all fields. Be it, Civil engineering, computer science, electrical or mechanical, it’s found a unique place in almost all the fields. Reliability engineers check the durability and lifelong capacity of systems. It also helps engineers and business investors to think of different parameters which can lead to system failure and the ways from which those failures can be eliminated to certain extent. It’s a discipline associated with planning and coordinating to achieve a technological objective. It’s a measure of performance of many systems, subsystems and components. Reliability is a fundamental attribute for the safe operation of any modern technological system. Focusing on safety, reliability analysis aims at the quantification of the probability of failure of the system and its protective barriers.
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Reliability Analysis for Seismic Performance Assessment of Concrete Reinforced Buildings

Reliability Analysis for Seismic Performance Assessment of Concrete Reinforced Buildings

Abstract: Evaluation of seismic performance of buildings is considered in this work. Surface response concept is used to derive explicitly the failure function in order to assess reliability of seismic design durability of reinforced concrete buildings. The failure function is considered to be the building maximum inter story displacement. Finite element computations were performed by using Etabs software package. Two random variables defining respectively material resistance variations of concrete and reinforcement steel were introduced. A third hidden variable of the reinforced concrete building termed ductility is also considered as a problem factor. A complete factorial design table was used to build a finite set of data points where the failure function is evaluated in order to identify the building surface response model via a polynomial regression. Then a parametric study regarding seismic performance reliability is conducted as function of building ductility.
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Application of reliability analysis for performance assessments in railway infrastructure asset management

Application of reliability analysis for performance assessments in railway infrastructure asset management

Reliable railway infrastructure systems guarantee the safety of operations and the availability of train services. With an increase in mobility demands, it is increasingly becoming a challenge to deliver railway infrastructure systems with a sustainable functionality that meets the various dependability attributes such as reliability, availability, and maintainability. Decisions related to infrastructure asset management in the railway industry focus on the maintenance, enhancement, and renewal of assets. This is to ensure that the infrastructure assets meet the required level of dependability and quality of service at the lowest life cycle costs. The success of these decisions depends on the effective management of individual assets over their lifetime from the perspective of a whole systems approach. A whole systems approach offers greater advantages over the traditional silo approach which lacks integration and coordination in the maintenance and management of complex cross-functional multi-asset systems. Reliability, when applied to infrastructure asset management, is a mathematical concept associated with dependability in which engineering knowledge is applied to identify and reduce the likelihood or frequency of failures within a system. In addition, it enables a systematic analysis to be performed at various levels of the railway network to quantify the various dependability attributes of individual infrastructure assets and their impact on the overall performance of the infrastructure system. The objective of this study is to develop a scientific approach to model and evaluate the reliability performance of railway infrastructure systems. This paper presents the development and application of a holistic reliability model for multi-asset systems that can facilitate and improve infrastructure maintenance management processes in railway environments. The model is applied and validated using a practical case study in the context of the Passenger Rail Agency of South Africa (PRASA). The case study applied to PRASA`s Metrorail network concluded that a holistic performance assessment method using reliability analysis can assist in improving the maintenance and management of railway infrastructure assets to guarantee high quality of service.
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Performance and Reliability of DSRC Vehicular Safety Communication: A Formal Analysis

Performance and Reliability of DSRC Vehicular Safety Communication: A Formal Analysis

In this paper, we first introduce and justify an e ff ective solution to the design of the control channel in DSRC with two levels of safety services covering most of the possible safety applications. Then, we construct an analytical model based on Markov chain method in [14] to evaluate perfor- mance and reliability indices such as channel throughput, transmission delay, and packet reception rates of a typical network solution for DSRC-based safety-related communi- cation under highway wireless communication environment. We apply our proposed model to evaluate the impact of message arrival interval, channel access priority scheme, hidden terminal problem, fading transmission channel, and highly mobile vehicles on the performance and reliability. Based on the observations of numerical results under typical DSRC environment, some enhancement schemes are suggested or validated accordingly.
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A Practical Approach to the Performance Analysis of Software Components using Calibrated Software Reliability Growth Models

A Practical Approach to the Performance Analysis of Software Components using Calibrated Software Reliability Growth Models

Many software tools have been proposed for the purpose of performance analysis and measurement on software executables. The results produced by such tools are visually displayed based on run-time characteristics of software executables without suggesting the fitness of executables at the operational environment. This is because run-time characteristics of an executable are not static for every running instance even in the same platform and same machine configuration. In this paper, an efficient method has been introduced to estimate fitness of software executables to the operational environment by incorporating Software Reliability Growth Models. The objective of this new method is to suggest the level of fitness of software applications based on reliability measures. For this purpose, existing reliability growth models are calibrated and run-time attributes of executables have been employed instead of failure data. The estimation of fitness at the operational environment of software executables will reduce the complexities in both performance analysis and maintenance.
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A Model-based approach to performance and reliability prediction

A Model-based approach to performance and reliability prediction

The KLAPER meta model (the static part plus the dynamic one) is based on the concepts of resources and services that perform activities and interact among them; however, KLAPER does not model the semantics of these services and interactions (i.e. the function they perform). KLAPER is just interested in modeling how long it takes to complete an operation, or if it can fail before completing. This is only one of the possible ways to consider a system. The advantage of such a view is that we can concentrate on the structure of the system and on the interaction between its components without any knowledge of the expected functions of the system itself. But now that we are able to model a system independently of its functional requirements, how can we use it to analyze performance and reliability? To answer to this question we need to recall that KLAPER is an “intermediate” language created to link two worlds: the software design one and the software analysis one.
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Performance, reliability, real cost effectiveness

Performance, reliability, real cost effectiveness

On the strength of 50 years experience in silicones technology, Bluestar Silicones offers it’s industrial customers an extensive range of products to meet their increasingly demanding requirements, both in terms of performance and reliability, as well as value for money.

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Security. Reliability. Performance. Flexibility. Scalability

Security. Reliability. Performance. Flexibility. Scalability

By leveraging an industry-proven ICP in VCE Vblock Systems and combining it with EMC Isilon and VMware vSphere Big Data Extensions, organizations get a fully integrated platform that meets and grows with their big data and analytics requirements. BDE makes deploying Hadoop clusters quick and easy, while Vblock Systems and Isilon deliver a scale-out infrastructure that achieves higher levels of security, reliability, availability, and performance for unstructured data analytics in production environments. With native HDFS support, the flexible, single file system architecture of Isilon eliminates the need to move data into a Hadoop cluster and instead brings Hadoop to the data. And by separating Hadoop compute functionality from Hadoop storage functionality, resources can easily scale to meet big data capacity or performance demands without having to overprovision.
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Performance and Reliability Analysis of Web Server Software Architectures

Performance and Reliability Analysis of Web Server Software Architectures

Our increasing reliance on the information and services provided by modern Web servers mandates that these ser- vices be offered with superior performance and reliability. The architecture of a Web server has a profound impact on its performance and reliability. One of the dimensions used to characterize the architecture of a Web server is the processing model employed in the server, which describes the type of process or threading model used to support a Web server operation. The main options for a process- ing model are process-based, thread-based or a hybrid of the process-based and the thread-based models. These op- tions have unique advantages and disadvantages in terms of their performance and reliability tradeoffs. In this paper we propose an analysis methodology based on the Stochastic Reward Net (SRN) modeling paradigm to quantify the per- formance and the reliability tradeoffs in the process-based and the thread-based Web server software architectures. We demonstrate the capability of the methodology to facilitate systematic, quantitative tradeoffs using several examples.
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A High Performance, High Reliability Perforce Server

A High Performance, High Reliability Perforce Server

implement hot swap redundant controllers: active-passive and active-active. The active- passive configuration is when one controller is active and handles all of the workload. The passive controller monitors the active controller for failures and takes over the workload in a failure situation. The passive controller should also keep a mirrored copy of the active controller's cache. This assures that no data in cache is lost when the passive controller takes over. The active- active configuration is when both controllers are active at the same time. They split the workload and maintain mirrored copies of each other's cache. As can be imagined, active-active, high-performance RAID controllers will out perform an active-passive configuration. However, it should be noted that redundant controllers are a definite performance vs. reliability tradeoff. Cache consistency is typically performed over the drive loop, resulting in reduced performance compared to a single non-redundant controller.
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The Strength of Corporate Culture and the Reliability of Firm Performance

The Strength of Corporate Culture and the Reliability of Firm Performance

I operationalize the reliability of firm performance as the degree of variation about a predicted mean performance level. This definition is consistent with previous studies of risk (e.g., Armour and Teece, 1978; Bowman, 1980). Past studies, however, typically measure variance about the sample mean. This is an unsatisfactory measure of reliability for a simple reason. If one uses the simple variance, firms that have improved their performance over time will appear to have unreliable performance simply because the mean is a poor estimate of the time trend in performance. (So will firms whose performance has consistently declined.) A measure of reliability in performance should also allow for changes in performance levels due to changes in firm characteristics. I therefore first try to account for firm differences in the mean performance level using firm characteristics. The variability in performance is then defined as the residual variance about the predicted regression line.
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Analysis of some engineering parameters 
		relevant to the performance and reliability of Hollow Fiber spinning 
		system

Analysis of some engineering parameters relevant to the performance and reliability of Hollow Fiber spinning system

The dry - wet spinning process involves numerous spinning parameters such as dope and bore compositions, flow rates, temperatures, take-up speed,…etc., making it extremely tedious to reproduce HF with a specified morphology and separation performance. Accordingly, it is vital to elucidate the effects of these parameters for optimizing membrane morphology and performance [Aptel et al. (1985), Chou et al. (2005), (2007), Chung et al. (1999),), Ehsan & Toraj (2009) Feng et al. (2013), Hao et al. (1996), Idris et al. (2002), Khayet (2003), Miyano et al. (1993), Ren et al (2008), Qin et al. (1999) (2003), (2005a,b)]. Higher dope flow rate leads to the formation of a hollow fiber with inner porous surface and dense selective layer on outer side which shows reduced water permeability but improved separation and Young ’ s Modulus [Qin et al. (1999)]. Air-gap distance plays a very important role on the cross-section area of nascent fibers. Both the inner and outer diameters (Di and Do) of dry wet-spun fibers decrease with increase in air gap distance [Khayet (2003), Chung et al. (1999)]. An important controlling factor in HF preparation is the additives such as polyvinyl pyrrolidone (PVP) and
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Applying Forgotten Lessons in Field Reliability Data Analysis to Performance based Support Contracts

Applying Forgotten Lessons in Field Reliability Data Analysis to Performance based Support Contracts

This work provides insight to practicing engineering managers in terms of obtaining “actionable knowledge” by making intelligent use of existing reliability data. For example, based on the authors’s experience on a case study in the defense avionics industry, to successfully execute PBL or availability type contracts, engineering managers across partner organizations are constantly searching for insight to facilitate the provision of equipment availability over time. They look at the data, attempt to detect trends and “see things” happening for a population of items deployed in the field. The analytics chosen for this purpose have practical repercussions on how organizations work with their suppliers and customers to face day-to-day issues that affect availability and end customer satisfaction, as well as long-term issues. The strategy for field reliability data described in this work helps engineering managers to navigate different options, from data exploration to the implementation of data analytics, and to place the focus on computational devices that are appropriate to the real-life situation engineering managers face. This is becoming particularly useful in maintenance planning and prioritization for engineering managers.
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Creating high reliability crews within the asphalt sector : introducing the high reliability performance index

Creating high reliability crews within the asphalt sector : introducing the high reliability performance index

Tevens is het binnen de gestelde tijd onrealistisch om voor elk type mengsel asfalt een standaard werkwijze te ontwikkelen en zal er daarom een keuze moeten worden gemaakt voor een bepaa[r]

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A reliability and validity study of the Palliative Performance Scale

A reliability and validity study of the Palliative Performance Scale

The data obtained from the reliability portion of the study was analyzed using SPSS version 15.0 and R version 2.5 software. The reliability of PPS was evaluated using the single rating intraclass correlation coefficient (ICC) for absolute agreement and for consistency using two-way random-effects models. Single rating measures were used because our interest was in ICC measures for individual participants rather than averages of independent meas- ures. The ICCs were obtained for each time period. Participant reliability was evaluated using Cohen's kappa, which is a chance-corrected measure of agreement between two participants. It ranges from 0 (chance agree- ment) to 1 (perfect agreement), and generally a kappa > 0.7 is considered satisfactory. An interpretation by Landis
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1 Improved Reliability and Mechanical Performance of

1 Improved Reliability and Mechanical Performance of

microstructure and properties of Sn-Bi-based solder alloy," Journal of Electronic Materials, vol. Jin, "Significantly improved mechanical properties of 271[r]

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Performance Analysis of Hybrid Mechanism to Improve Reliability in Wireless Sensor Network

Performance Analysis of Hybrid Mechanism to Improve Reliability in Wireless Sensor Network

Packet loss in wireless sensor networks is usually due to the quality of the wireless channel, sensor failure, and congestion. Most of the applications need reliable transmission of each packet, and thus packet-level reliability is required. Almost every transport layer protocol for wireless sensor networks offers unidirectional reliable message delivery, but bidirectional reliability is also required in certain applications.

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A fuzzy approach to reliability analysis

A fuzzy approach to reliability analysis

Reliability analysis is one of the most challenging topics for the system analysts or plant personnel such that the system should run for a longer period of time without failures. Unfortunately, failure is an unavoidable phenomenon in an industrial system, and hence the role of the system analyst becomes critical for maintaining the performance of the industrial systems so as to increase the performance and productivity of the system. As the complexity of a systems grows, the uncertainty of information about the reliability increases. To overcome this issue, Garg (2015) proposed the nth-order fuzzy Kolmogorovs differential equations, by using a fuzzy Markov model of a repairable industrial system. He evaluated the fuzzy reliability of the system both in transient and steady state, using Runge-Kutta method.
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