947 resultados para System Compositional Approach


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In order to reduce heterogeneity in schizophrenia, a system-specific approach consisting of the domains "language", "affect" and "motor behavior" has been proposed. We examined this system-specific approach for its applicability to clinical practice in the motor behavior domain, using the methodological approach of case studies, and discuss here the differences to the positive/negative concept. We analyzed eight cases with stable motor-dominant symptoms, and also quantitatively assessed motor behavior by using the Bern Psychopathology Scale (BPS), a standardized psychopathological assessment instrument, as well as actigraphic data. Characterization of cases using the positive/negative approach was not helpful. We found an overlap of the motor behavior domain with the other two domains. This complicates the application of the system-specific approach in the sense of a typology. Furthermore, we found both relapsing courses with full remission and chronic courses with deterioration within the motor-dominant subtype. Nevertheless, the system-specific approach has heuristic utility for the future.

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The satellite remote sensing missions are essential for long-term research around the condition of the earth resources and environment. On the other hand, in recent years the application of microsatellites is of interest in many space programs for their less cost and response time. In microsatellite remote sensing missions there are tight interrelations between different requirements such as orbital altitude, revisit time, mission life and spatial resolution. Also, all of these requirements can affect the whole system level design characteristics. In this work, the remote sensing microsatellite sizing process is divided into three major design disciplines; a) orbit design, b) payload sizing and c) bus sizing. Finally, some specific design cases are investigated inside the design space for evaluating the effect of different design variables on the satellite total mass. Considering the results of the work, it is concluded that applying a systematic approach at the initial design phase of such projects provides a good insight to the not clearly seen interactions inside their highly extended design space

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Includes bibliographical references (p. 55).

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This dissertation presents a system-wide approach, based on genetic algorithms, for the optimization of transfer times for an entire bus transit system. Optimization of transfer times in a transit system is a complicated problem because of the large set of binary and discrete values involved. The combinatorial nature of the problem imposes a computational burden and makes it difficult to solve by classical mathematical programming methods. ^ The genetic algorithm proposed in this research attempts to find an optimal solution for the transfer time optimization problem by searching for a combination of adjustments to the timetable for all the routes in the system. It makes use of existing scheduled timetables, ridership demand at all transfer locations, and takes into consideration the randomness of bus arrivals. ^ Data from Broward County Transit are used to compute total transfer times. The proposed genetic algorithm-based approach proves to be capable of producing substantial time savings compared to the existing transfer times in a reasonable amount of time. ^ The dissertation also addresses the issues related to spatial and temporal modeling, variability in bus arrival and departure times, walking time, as well as the integration of scheduling and ridership data. ^

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Dyslipidaemia is one of the major cardiovascular risk factors, it can be due to primary causes (i.e. monogenic, characterized by a single gene mutation, or dyslipidaemia of polygenic/environmental causes), or secondary to specific disorders such as obesity, diabetes mellitus or hypothyroidism. Monogenic patients present the most severe phenotype and so they need to be identified in early age so pharmacologic treatment can be implemented to decrease the cardiovascular risk. However the majority of hyperlipidemic patients most likely have a polygenic disease that can be mainly controlled just by the implementation of a healthy lifestyle. Thus, the distinction between monogenic and polygenic dyslipidaemia is important for a prompt diagnosis, cardiovascular risk assessment, counselling and treatment. Besides the already stated biomarkers as LDL, apoB and apoB/apoA-I ratio, other promising (yet, needing further research) biomarkers for clinical differentiation between dyslipidaemias are apoE, sdLDL, apoC-2 and apoC-3. However, none of these biomarkers can explain the complex lipid profile of the majority of these patients.

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This paper provides a critique of the Water Sensitive Urban Design (WSUD) paradigm by discussing its congruence with an established sustainable design principle called 'whole system design'. It was found that WSUD is congruent with the whole system design approach as a philosophy, but not in practice. Future improvement of WSUD practice may depend on the adoption of a front-loaded, teamwork-based design and planning process that is embedded in the principle of whole system design.

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Whole System Design is increasingly being seen as one of the most cost effective ways to both increase the productivity and reduce the negative environmental impacts of an engineered system. A focus on design is critical, as the output from this stage of the project locks-in most of the economic and environmental performance of the designed system throughout its life, which can span from a few years to many decades. Indeed, it is now widely acknowledged that all designers – particularly engineers, architects and industrial designers – need to be able to understand and implement a whole system design approach. This book provides a clear design methodology, based on leading efforts in the field, and is supported by worked examples that demonstrate how advances in energy, materials and water productivity can be achieved through applying an integrated approach to sustainable engineering. Chapters 1–5 outline the approach and explain how it can be implemented to enhance the established Systems Engineering framework. Chapters 6–10 demonstrate, through detailed worked examples, the application of the approach to industrial pumping systems, passenger vehicles, electronics and computer systems, temperature control of buildings, and domestic water systems.

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In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors.

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Behavioral models capture operational principles of real-world or designed systems. Formally, each behavioral model defines the state space of a system, i.e., its states and the principles of state transitions. Such a model is the basis for analysis of the system’s properties. In practice, state spaces of systems are immense, which results in huge computational complexity for their analysis. Behavioral models are typically described as executable graphs, whose execution semantics encodes a state space. The structure theory of behavioral models studies the relations between the structure of a model and the properties of its state space. In this article, we use the connectivity property of graphs to achieve an efficient and extensive discovery of the compositional structure of behavioral models; behavioral models get stepwise decomposed into components with clear structural characteristics and inter-component relations. At each decomposition step, the discovered compositional structure of a model is used for reasoning on properties of the whole state space of the system. The approach is exemplified by means of a concrete behavioral model and verification criterion. That is, we analyze workflow nets, a well-established tool for modeling behavior of distributed systems, with respect to the soundness property, a basic correctness property of workflow nets. Stepwise verification allows the detection of violations of the soundness property by inspecting small portions of a model, thereby considerably reducing the amount of work to be done to perform soundness checks. Besides formal results, we also report on findings from applying our approach to an industry model collection.

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This thesis presents a novel approach to building large-scale agent-based models of networked physical systems using a compositional approach to provide extensibility and flexibility in building the models and simulations. A software framework (MODAM - MODular Agent-based Model) was implemented for this purpose, and validated through simulations. These simulations allow assessment of the impact of technological change on the electricity distribution network looking at the trajectories of electricity consumption at key locations over many years.

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One of the critical issues in large scale commercial exploitation of MEMS technology is its system integration. In MEMS, a system design approach requires integration of varied and disparate subsystems with one of a kind interface. The physical scales as well as the magnitude of signals of various subsystems vary widely. Known and proven integration techniques often lead to considerable loss in advantages the tiny MEMS sensors have to offer. Therefore, it becomes imperative to think of the entire system at the outset, at least in terms of the concept design. Such design entails various aspects of the system ranging from selection of material, transduction mechanism, structural configuration, interface electronics, and packaging. One way of handling this problem is the system-in-package approach that uses optimized technology for each function using the concurrent hybrid engineering approach. The main strength of this design approach is the fast time to prototype development. In the present work, we pursue this approach for a MEMS load cell to complete the process of system integration for high capacity load sensing. The system includes; a micromachined sensing gauge, interface electronics and a packaging module representing a system-in-package ready for end characterization. The various subsystems are presented in a modular stacked form using hybrid technologies. The micromachined sensing subsystem works on principles of piezo-resistive sensing and is fabricated using CMOS compatible processes. The structural configuration of the sensing layer is designed to reduce the offset, temperature drift, and residual stress effects of the piezo-resistive sensor. ANSYS simulations are carried out to study the effect of substrate coupling on sensor structure and its sensitivity. The load cell system has built-in electronics for signal conditioning, processing, and communication, taking into consideration the issues associated with resolution of minimum detectable signal. The packaged system represents a compact and low cost solution for high capacity load sensing in the category of compressive type load sensor.

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A nonlinear adaptive system theoretic approach is presented in this paper for effective treatment of infectious diseases that affect various organs of the human body. The generic model used does not represent any specific disease. However, it mimics the generic immunological dynamics of the human body under pathological attack, including the response to external drugs. From a system theoretic point of view, drugs can be interpreted as control inputs. Assuming a set of nominal parameters in the mathematical model, first a nonlinear controller is designed based on the principle of dynamic inversion. This treatment strategy was found to be effective in completely curing "nominal patients". However, in some cases it is ineffective in curing "realistic patients". This leads to serious (sometimes fatal) damage to the affected organ. To make the drug dosage design more effective, a model-following neuro-adaptive control design is carried out using neural networks, which are trained (adapted) online. From simulation studies, this adaptive controller is found to be effective in killing the invading microbes and healing the damaged organ even in the presence of parameter uncertainties and continuing pathogen attack.