1,396 research outputs found

    Towards maintainer script modernization in FOSS distributions

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    Free and Open Source Software (FOSS) distributions are complex software systems, made of thousands packages that evolve rapidly, independently, and without centralized coordination. During packages upgrades, corner case failures can be encountered and are hard to deal with, especially when they are due to misbehaving maintainer scripts: executable code snippets used to finalize package configuration. In this paper we report a software modernization experience, the process of representing existing legacy systems in terms of models, applied to FOSS distributions. We present a process to define meta-models that enable dealing with upgrade failures and help rolling back from them, taking into account maintainer scripts. The process has been applied to widely used FOSS distributions and we report about such experiences

    Real-Time Monitoring and Fault Diagnostics in Roll-To-Roll Manufacturing Systems

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    A roll-to-roll (R2R) process is a manufacturing technique involving continuous processing of a flexible substrate as it is transferred between rotating rolls. It integrates many additive and subtractive processing techniques to produce rolls of product in an efficient and cost-effective way due to its high production rate and mass quantity. Therefore, the R2R processes have been increasingly implemented in a wide range of manufacturing industries, including traditional paper/fabric production, plastic and metal foil manufacturing, flexible electronics, thin film batteries, photovoltaics, graphene films production, etc. However, the increasing complexity of R2R processes and high demands on product quality have heightened the needs for effective real-time process monitoring and fault diagnosis in R2R manufacturing systems. This dissertation aims at developing tools to increase system visibility without additional sensors, in order to enhance real-time monitoring, and fault diagnosis capability in R2R manufacturing systems. First, a multistage modeling method is proposed for process monitoring and quality estimation in R2R processes. Product-centric and process-centric variation propagation are introduced to characterize variation propagation throughout the system. The multistage model mainly focuses on the formulation of process-centric variation propagation, which uniquely exists in R2R processes, and the corresponding product quality measurements with both physical knowledge and sensor data analysis. Second, a nonlinear analytical redundancy method is proposed for sensor validation to ensure the accuracy of sensor measurements for process and quality control. Parity relations based on nonlinear observation matrix are formulated to characterize system dynamics and sensor measurements. Robust optimization is designed to identify the coefficient of parity relations that can tolerate a certain level of measurement noise and system disturbances. The effect of the change of operating conditions on the value of the optimal objective function – parity residuals and the optimal design variables – parity coefficients are evaluated with sensitivity analysis. Finally, a multiple model approach for anomaly detection and fault diagnosis is introduced to improve the diagnosability under different operating regimes. The growing structure multiple model system (GSMMS) is employed, which utilizes Voronoi sets to automatically partition the entire operating space into smaller operating regimes. The local model identification problem is revised by formulating it into an optimization problem based on the loss minimization framework and solving with the mini-batch stochastic gradient descent method instead of least squares algorithms. This revision to the GSMMS method expands its capability to handle the local model identification problems that cannot be solved with a closed-form solution. The effectiveness of the models and methods are determined with testbed data from an R2R process. The results show that those proposed models and methods are effective tools to understand variation propagation in R2R processes and improve estimation accuracy of product quality by 70%, identify the health status of sensors promptly to guarantee data accuracy for modeling and decision making, and reduce false alarm rate and increase detection power under different operating conditions. Eventually, those tools developed in this thesis contribute to increase the visibility of R2R manufacturing systems, improve productivity and reduce product rejection rate.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146114/1/huanyis_1.pd

    Towards Practical Graph-Based Verification for an Object-Oriented Concurrency Model

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    To harness the power of multi-core and distributed platforms, and to make the development of concurrent software more accessible to software engineers, different object-oriented concurrency models such as SCOOP have been proposed. Despite the practical importance of analysing SCOOP programs, there are currently no general verification approaches that operate directly on program code without additional annotations. One reason for this is the multitude of partially conflicting semantic formalisations for SCOOP (either in theory or by-implementation). Here, we propose a simple graph transformation system (GTS) based run-time semantics for SCOOP that grasps the most common features of all known semantics of the language. This run-time model is implemented in the state-of-the-art GTS tool GROOVE, which allows us to simulate, analyse, and verify a subset of SCOOP programs with respect to deadlocks and other behavioural properties. Besides proposing the first approach to verify SCOOP programs by automatic translation to GTS, we also highlight our experiences of applying GTS (and especially GROOVE) for specifying semantics in the form of a run-time model, which should be transferable to GTS models for other concurrent languages and libraries.Comment: In Proceedings GaM 2015, arXiv:1504.0244

    Detection and Isolation of Small Faults in Lithium-Ion Batteries via the Asymptotic Local Approach

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    This contribution presents a diagnosis scheme for batteries to detect and isolate internal faults in the form of small parameter changes. This scheme is based on an electrochemical reduced-order model of the battery, which allows the inclusion of physically meaningful faults that might affect the battery performance. The sensitivity properties of the model are analyzed. The model is then used to compute residuals based on an unscented Kalman filter. Primary residuals and a limiting covariance matrix are obtained thanks to the local approach, allowing for fault detection and isolation by chi-squared statistical tests. Results show that faults resulting in limited 0.15% capacity and 0.004% power fade can be effectively detected by the local approach. The algorithm is also able to correctly isolate faults related with sensitive parameters, whereas parameters with low sensitivity or linearly correlated are more difficult to precise.Comment: 8 pages, 2 figures, 3 tables, conferenc

    Distance support in-service engineering for the high energy laser

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    The U.S. Navy anticipates moving to a shipboard high-energy laser program of record in the fiscal year 2018 and achieving an initial operational capability by 2020. The design of a distance support capability within the high-energy laser system was expected to assist the Navy in reaching this goal. This capstone project explored the current Navy architecture for distance support and applied system engineering methodologies to develop a conceptual distance support framework with application to the high-energy laser system. A model and simulation of distance support functions were developed and used to analyze the feasibility in terms of performance, cost, and risk. Results of this capstone study showed that the implementation of distance support for the high-energy laser system is feasible and would reduce the total ownership cost over the life of the program. Furthermore, the capstone shows that moving toward the team’s recommended distance support framework will address current gaps in the Navy distance support architecture and will provide a methodology tailored to modern enterprise naval systems.http://archive.org/details/distancesupporti1094545248Approved for public release; distribution is unlimited

    FTSM - fast Takagi-Sugeno fuzzy modeling

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    Automated metamorphic testing of variability analysis tools

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    Variability determines the capability of software applications to be configured and customized. A common need during the development of variability–intensive systems is the automated analysis of their underlying variability models, e.g. detecting contradictory configuration options. The analysis operations that are performed on variability models are often very complex, which hinders the testing of the corresponding analysis tools and makes difficult, often infeasible, to determine the correctness of their outputs, i.e. the well–known oracle problem in software testing. In this article, we present a generic approach for the automated detection of faults in variability analysis tools overcoming the oracle problem. Our work enables the generation of random variability models together with the exact set of valid configurations represented by these models. These test data are generated from scratch using step–wise transformations and assuring that certain constraints (a.k.a. metamorphic relations) hold at each step. To show the feasibility and generalizability of our approach, it has been used to automatically test several analysis tools in three variability domains: feature models, CUDF documents and Boolean formulas. Among other results, we detected 19 real bugs in 7 out of the 15 tools under test.CICYT TIN2012-32273CICYT IPT-2012- 0890-390000Junta de Andalucía TIC-5906Junta de Andalucía P12-TIC- 186

    Armstrong Flight Research Center Research Technology and Engineering Report 2015

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    I am honored to endorse the 2015 Neil A. Armstrong Flight Research Centers Research, Technology, and Engineering Report. The talented researchers, engineers, and scientists at Armstrong are continuing a long, rich legacy of creating innovative approaches to solving some of the difficult problems and challenges facing NASA and the aerospace community.Projects at NASA Armstrong advance technologies that will improve aerodynamic efficiency, increase fuel economy, reduce emissions and aircraft noise, and enable the integration of unmanned aircraft into the national airspace. The work represented in this report highlights the Centers agility to develop technologies supporting each of NASAs core missions and, more importantly, technologies that are preparing us for the future of aviation and space exploration.We are excited about our role in NASAs mission to develop transformative aviation capabilities and open new markets for industry. One of our key strengths is the ability to rapidly move emerging techniques and technologies into flight evaluation so that we can quickly identify their strengths, shortcomings, and potential applications.This report presents a brief summary of the technology work of the Center. It also contains contact information for the associated technologists responsible for the work. Dont hesitate to contact them for more information or for collaboration ideas
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