928 resultados para complex computer systems
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It is argued that the truth status of emergent properties of complex adaptive systems models should be based on an epistemology of proof by constructive verification and therefore on the ontological axioms of a non-realist logical system such as constructivism or intuitionism. ‘Emergent’ properties of complex adaptive systems (CAS) models create particular epistemological and ontological challenges. These challenges bear directly on current debates in the philosophy of mathematics and in theoretical computer science. CAS research, with its emphasis on computer simulation, is heavily reliant on models which explore the entailments of Formal Axiomatic Systems (FAS). The incompleteness results of Gödel, the incomputability results of Turing, and the Algorithmic Information Theory results of Chaitin, undermine a realist (platonic) truth model of emergent properties. These same findings support the hegemony of epistemology over ontology and point to alternative truth models such as intuitionism, constructivism and quasi-empiricism.
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The development of new experimental techniques for the determination of phase equilibria in complex slag systems, chemical thermodynamic, and viscosity models is reported. The new experimental data, and new thermodynamic and viscosity models, have been combined in a custom-designed computer software package to produce limiting operability diagrams for slag systems. These diagrams are used to describe phase equilibria and physicochemical properties in complex slag systems. The approach is illustrated with calculations on the system FeO-Fe2O3-CaO-SiO-Al2O3 at metallic iron saturation, slags produced in coal slagging gasifiers, and in the reprocessing of nonferrous smelting slags. This article was presented at the Mills Symposium Molten Metals, Slags and Glasses-Characterisation of Properties and Phenomena held in London in August 2000.
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We are working on the confluence of knowledge management, organizational memory and emergent knowledge with the lens of complex adaptive systems. In order to be fundamentally sustainable organizations search for an adaptive need for managing ambidexterity of day-to-day work and innovation. An organization is an entity of a systemic nature, composed of groups of people who interact to achieve common objectives, making it necessary to capture, store and share interactions knowledge with the organization, this knowledge can be generated in intra-organizational or inter-organizational level. The organizations have organizational memory of knowledge of supported on the Information technology and systems. Each organization, especially in times of uncertainty and radical changes, to meet the demands of the environment, needs timely and sized knowledge on the basis of tacit and explicit. This sizing is a learning process resulting from the interaction that emerges from the relationship between the tacit and explicit knowledge and which we are framing within an approach of Complex Adaptive Systems. The use of complex adaptive systems for building the emerging interdependent relationship, will produce emergent knowledge that will improve the organization unique developing.
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Database query languages on relations (for example SQL) make it possible to join two relations. This operation is very common in desktop/server database systems but unfortunately query processing systems in networked embedded computer systems currently do not support this operation; specifically, the query processing systems TAG, TinyDB, Cougar do not support this. We show how a prioritized medium access control (MAC) protocol can be used to efficiently execute the database operation join for networked embedded computer systems where all computer nodes are in a single broadcast domain.
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It is a difficult task to avoid the “smart systems” topic when discussing smart prevention and, similarly, it is a difficult task to address smart systems without focusing their ability to learn. Following the same line of thought, in the current reality, it seems a Herculean task (or an irreparable omission) to approach the topic of certified occupational health and safety management systems (OHSMS) without discussing the integrated management systems (IMSs). The available data suggest that seldom are the OHSMS operating as the single management system (MS) in a company so, any statement concerning OHSMS should mainly be interpreted from an integrated perspective. A major distinction between generic systems can be drawn between those that learn, i.e., those systems that have “memory” and those that have not. These former systems are often depicted as adaptive since they take into account past events to deal with novel, similar and future events modifying their structure to enable success in its environment. Often, these systems, present a nonlinear behavior and a huge uncertainty related to the forecasting of some events. This paper seeks to portray, for the first time as we were able to find out, the IMSs as complex adaptive systems (CASs) by listing their properties and dissecting the features that enable them to evolve and self-organize in order to, holistically, fulfil the requirements from different stakeholders and thus thrive by assuring the successful sustainability of a company. Based on the revision of literature carried out, this is the first time that IMSs are pointed out as CASs which may develop fruitful synergies both for the MSs and for CASs communities. By performing a thorough revision of literature and based on some concepts embedded in the “DNA” of the subsystems implementation standards it is intended, specifically, to identify, determine and discuss the properties of a generic IMS that should be considered to classify it as a CAS.
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Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos.
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Achieving a high degree of dependability in complex macro-systems is challenging. Because of the large number of components and numerous independent teams involved, an overview of the global system performance is usually lacking to support both design and operation adequately. A functional failure mode, effects and criticality analysis (FMECA) approach is proposed to address the dependability optimisation of large and complex systems. The basic inductive model FMECA has been enriched to include considerations such as operational procedures, alarm systems. environmental and human factors, as well as operation in degraded mode. Its implementation on a commercial software tool allows an active linking between the functional layers of the system and facilitates data processing and retrieval, which enables to contribute actively to the system optimisation. The proposed methodology has been applied to optimise dependability in a railway signalling system. Signalling systems are typical example of large complex systems made of multiple hierarchical layers. The proposed approach appears appropriate to assess the global risk- and availability-level of the system as well as to identify its vulnerabilities. This enriched-FMECA approach enables to overcome some of the limitations and pitfalls previously reported with classical FMECA approaches.
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Report on selected computer systems operated by the State of Iowa for the period July 1, 1999 through June 30, 2014
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Un résumé en français est également disponible.
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Dans un contexte où les virus informatiques présentent un risque sérieux pour les réseaux à travers le globe, il est impératif de retenir la responsabilité des compagnies qui n’y maintiennent pas une sécurité adéquate. À ce jour, les tribunaux québécois n’ont pas encore été saisis d’affaires en responsabilité pour des virus informatiques. Cet article brosse un portrait général de la responsabilité entourant les virus informatiques en fonction des principes généraux de responsabilité civile en vigueur au Québec. L’auteur propose des solutions pour interpréter les trois critères traditionnels la faute, le dommage et le lien causal en mettant l’accent sur l’obligation de précaution qui repose sur les épaules de l’administrateur de réseau. Ce joueur clé pourrait bénéficier de l’adoption de dispositions générales afin de limiter sa responsabilité. De plus, les manufacturiers et les distributeurs peuvent également partager une partie de la responsabilité en proportion de la gravité de leur faute. Les entreprises ont un devoir légal de s’assurer que leurs systèmes sont sécuritaires afin de protéger les intérêts de leurs clients et des tiers.
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An enterprise is viewed as a complex system which can be engineered to accomplish organisational objectives. Systems analysis and modelling will enable to the planning and development of the enterprise and IT systems. Many IT systems design methods focus on functional and non-functional requirements of the IT systems. Most methods are normally capable of one but leave out other aspects. Analysing and modelling of both business and IT systems may often have to call on techniques from various suites of methods which may be placed on different philosophic and methodological underpinnings. Coherence and consistency between the analyses are hard to ensure. This paper introduces the Problem Articulation Method (PAM) which facilitates the design of an enterprise system infrastructure on which an IT system is built. Outcomes of this analysis represent requirements which can be further used for planning and designing a technical system. As a case study, a finance system, Agresso, for e-procurement has been used in this paper to illustrate the applicability of PAM in modelling complex systems.
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A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.