The semantic Web technology and the Web services description language extensibility may be combined to describe services in an unambiguous and machine interpretable way, automating Web services discovery, selection an...
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The semantic Web technology and the Web services description language extensibility may be combined to describe services in an unambiguous and machine interpretable way, automating Web services discovery, selection and invocation. In this paper, we present an algorithm and a prototype for the automatic composition of Web services that implement workflows described in a high level language. Our approach has many advantages comparing to the manual creation of a simple program composition, such as smaller implementation time and cost, reliability with the generation of contingency plans, greater capacity to evolve with the dynamic service discovery, and faster execution time with the use of heuristics. We use the OWLS ontology to semantically describe Web services metadata and indexes to help selecting them. The proposed algorithm considers that equivalent services may have different interfaces and also respects preferences of the users.
We present the WebComposer tool for the automatic composition and execution of Web service-based workflows. We use ontologies to describe and browse workflows. We associate messages and operations with workflow domain...
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We present the WebComposer tool for the automatic composition and execution of Web service-based workflows. We use ontologies to describe and browse workflows. We associate messages and operations with workflow domain concepts using WSDL extensibility. The automatic workflow implementation through WebComposer enables the full separation of the workflow logic and the implementation technology. WebComposer provides the execution of ad-hoc programs by users and the automatic maintenance of these programs, as the available Web services are altered.
In this paper we consider a method for finding several eigenvalues and corresponding eigenvectors of large-scale generalized eigenvalue problems. In this method, a small matrix pencil that has only the desired eigenva...
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In this work, we present and analyze the use of a reconfigurable job scheduling simulator called RJSSim as an aid tool for parallel processing learning. This software is a functional and performance Java-based simulat...
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Dynamic programming (DP) is a principled way to design optimal controllers for certain classes of nonlinear systems;unfortunately, DP is computationally very expensive. The Reinforcement Learning methods known as Adap...
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Dynamic programming (DP) is a principled way to design optimal controllers for certain classes of nonlinear systems;unfortunately, DP is computationally very expensive. The Reinforcement Learning methods known as Adaptive Critics (AC) provide computationally feasible means for performing approximate Dynamic programming (ADP). The term 'adaptive ' in A C refers to the critic 's improved estimations of the Value Function used by DP. To apply DP, the user must craft a Utility function that embodies all the problem-specific design specifications/criteria. Model Reference Adaptive Control methods have been successfully used in the control community to effect on-line redesign of a controller in response to variations in plant parameters, with the idea that the resulting closed loop system dynamics will mimic those of a Reference Model. The work reported here 1) uses a reference model in ADP as the key information input to the Utility function, and 2) uses ADP off-line to design the desired controller. Future work will extend this to on-line application. This method is demonstrated for a hypersonic shaped airplane called LoFL YTE®;its handling characteristics are natively a little "hotter" than a pilot would desire. A control augmentation subsystem is designed using ADP to make the plane "feel like " a better behaved one, as specified by a Reference Model. The number of inputs to the successfully designed controller are among the largest seen in the literature to date.
Machine Learning has traditionally been a topic of research and instruction in computerscience and computerengineeringprograms. Yet, due to its wide applicability in a variety of fields, its research use has expand...
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Machine Learning has traditionally been a topic of research and instruction in computerscience and computerengineeringprograms. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and scienceprogramming courses through appropriately designed programming projects.
This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA's) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then prov...
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This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA's) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then provides predictions about the process behavior, based on control actions applied to the system. These predictions are used by the fuzzy controller, in order to accomplish a better control of an alcoholic fermentation process from chemical industry. This problem has been chosen due to its non-linearity and large accommodation time, that make it hard to control by standard controllers. Comparison of performance is made with non-predictive approaches(PID and Fuzzy-PD), and also with another predictive approach, GPC(Generalized Predictive Control).
We propose a method for process monitoring of a semiconductor manufacturing process. Independent component analysis (ICA) is applied to characterize E-test parameter data. We calculate angular confidence intervals for...
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We propose a method for process monitoring of a semiconductor manufacturing process. Independent component analysis (ICA) is applied to characterize E-test parameter data. We calculate angular confidence intervals for the model, eliminate marginally significant components and implement control charts for significant components of interest. Alarms are generated off of deviations in the charted components. Alarms are easily used in process diagnosis based on the interpretation of the independent components.
Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circuit implementations of synapse function e...
Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circuit implementations of synapse function exist with different computational goals in mind. In this paper we describe a new CMOS synapse design that separately controls quiescent leak current, synaptic gain, and time-constant of decay. This circuit implements part of a commonly-used kinetic model of synaptic conductance. We show a theoretical analysis and experimental data for prototypes fabricated in a commercially-available 1.5µm CMOS process.
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