80,817 research outputs found
VR-PMS: a new approach for performance measurement and management of industrial systems
A new performance measurement and management framework based on value and risk is proposed. The proposed framework is applied to the modelling and evaluation of the a priori performance evaluation of manufacturing processes and to deciding on their alternatives. For this reason, it consistently integrates concepts relevant to objectives, activity, and risk in a single framework comprising a conceptual value/risk model, and it conceptualises the idea of value- and risk based performance management in a process context. In addition, a methodological framework is developed to provide guidelines for the decision-makers or performance evaluators of the processes. To facilitate the performance measurement and management process, this latter framework is organized in four phases: context establishment, performance modelling, performance assessment, and decision-making. Each phase of the framework is then instrumented with state of-the-art quantitative analysis tools and methods. For process design and evaluation, the deliverable of the value- and risk-based performance measurement and management system (VR-PMS) is a set of ranked solutions (i.e. alternative business processes) evaluated against the developed value and risk indicators. The proposed VR-PMS is illustrated with a case study from discrete parts manufacturing but is indeed applicable to a wide range of processes or systems
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
In the last few years, Model Driven Development (MDD), Component-based
Software Development (CBSD), and context-oriented software have become
interesting alternatives for the design and construction of self-adaptive
software systems. In general, the ultimate goal of these technologies is to be
able to reduce development costs and effort, while improving the modularity,
flexibility, adaptability, and reliability of software systems. An analysis of
these technologies shows them all to include the principle of the separation of
concerns, and their further integration is a key factor to obtaining
high-quality and self-adaptable software systems. Each technology identifies
different concerns and deals with them separately in order to specify the
design of the self-adaptive applications, and, at the same time, support
software with adaptability and context-awareness. This research studies the
development methodologies that employ the principles of model-driven
development in building self-adaptive software systems. To this aim, this
article proposes an evaluation framework for analysing and evaluating the
features of model-driven approaches and their ability to support software with
self-adaptability and dependability in highly dynamic contextual environment.
Such evaluation framework can facilitate the software developers on selecting a
development methodology that suits their software requirements and reduces the
development effort of building self-adaptive software systems. This study
highlights the major drawbacks of the propped model-driven approaches in the
related works, and emphasise on considering the volatile aspects of
self-adaptive software in the analysis, design and implementation phases of the
development methodologies. In addition, we argue that the development
methodologies should leave the selection of modelling languages and modelling
tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition,
self-adaptive application, context oriented software developmen
Methods of Technical Prognostics Applicable to Embedded Systems
HlavnĂ cĂlem dizertace je poskytnutĂ ucelenĂ©ho pohledu na problematiku technickĂ© prognostiky, kterĂĄ nachĂĄzĂ uplatnÄnĂ v tzv. prediktivnĂ ĂșdrĆŸbÄ zaloĆŸenĂ© na trvalĂ©m monitorovĂĄnĂ zaĆĂzenĂ a odhadu ĂșrovnÄ degradace systĂ©mu Äi jeho zbĂœvajĂcĂ ĆŸivotnosti a to zejmĂ©na v oblasti komplexnĂch zaĆĂzenĂ a strojĆŻ. V souÄasnosti je technickĂĄ diagnostika pomÄrnÄ dobĆe zmapovanĂĄ a reĂĄlnÄ nasazenĂĄ na rozdĂl od technickĂ© prognostiky, kterĂĄ je stĂĄle rozvĂjejĂcĂm se oborem, kterĂœ ovĆĄem postrĂĄdĂĄ vÄtĆĄĂ mnoĆŸstvĂ reĂĄlnĂœch aplikaci a navĂc ne vĆĄechny metody jsou dostateÄnÄ pĆesnĂ© a aplikovatelnĂ© pro embedded systĂ©my. DizertaÄnĂ prĂĄce pĆinĂĄĆĄĂ pĆehled zĂĄkladnĂch metod pouĆŸitelnĂœch pro ĂșÄely predikce zbĂœvajĂcĂ uĆŸitnĂ© ĆŸivotnosti, jsou zde popsĂĄny metriky pomocĂ, kterĂœch je moĆŸnĂ© jednotlivĂ© pĆĂstupy porovnĂĄvat aĆ„ uĆŸ z pohledu pĆesnosti, ale takĂ© i z pohledu vĂœpoÄetnĂ nĂĄroÄnosti. Jedno z dizertaÄnĂch jader tvoĆĂ doporuÄenĂ a postup pro vĂœbÄr vhodnĂ© prognostickĂ© metody s ohledem na prognostickĂĄ kritĂ©ria. DalĆĄĂm dizertaÄnĂm jĂĄdrem je pĆedstavenĂ tzv. ÄĂĄsticovĂ©ho filtrovanĂ (particle filtering) vhodnĂ© pro model-based prognostiku s ovÄĆenĂm jejich implementace a porovnĂĄnĂm. HlavnĂ dizertaÄnĂ jĂĄdro reprezentuje pĆĂpadovou studii pro velmi aktuĂĄlnĂ tĂ©ma prognostiky Li-Ion baterii s ohledem na trvalĂ© monitorovĂĄnĂ. PĆĂpadovĂĄ studie demonstruje proces prognostiky zaloĆŸenĂ© na modelu a srovnĂĄvĂĄ moĆŸnĂ© pĆĂstupy jednak pro odhad doby pĆed vybitĂm baterie, ale takĂ© sleduje moĆŸnĂ© vlivy na degradaci baterie. SouÄĂĄstĂ prĂĄce je zĂĄkladnĂ ovÄĆenĂ modelu Li-Ion baterie a nĂĄvrh prognostickĂ©ho procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.
Cost Decision Support in Product Design
The constraints addressed in decision making during product design, process planning and production planning determine the admissible solution space for the manufacture of products. The solution space determines largely the costs that are incurred in the production process. In order to be able to make economically sound decisions, costing data support must be integrated into the decision making processes. Regarding product design, the designer must be supplied with transparent costing data, that is ready for direct application. In this paper a functional architecture for costing data support during product design, as well as a corresponding data structure are presented
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
A foundation for machine learning in design
This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD
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