This paper describes a new solution method applied to the problem initializing DAEs using the Modelica language. Modelica is primarily an object- oriented equ-tion-based modeling language that allows specification of ...
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This paper describes a new solution method applied to the problem initializing DAEs using the Modelica language. Modelica is primarily an object- oriented equ-tion-based modeling language that allows specification of mathematical models of complex natural or man-made systems. Major features of Modelica are the multidomain modeling capability and the reusability of model components corresponding to physical objects, which allow to build and simulate highly complex systems. However, initializing such models has been quite cumbersome, since initial equations have to be pro-vided at the system level, where the user needs to know details on the underlying transformation and index-reduction algorithms, that in general are applied to simulate a Modelica model.
Data tiling is an array layout transformation technique that partitions an array into smaller subarray blocks. It was originally proposed to improve the cache performance of regular loops. Recently, researchers have a...
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The use of induction motors is widespread in industry. Many researchers have studied the condition monitoring and detecting the faults of induction motors at an early stage. Early detection of motor faults results in ...
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The use of induction motors is widespread in industry. Many researchers have studied the condition monitoring and detecting the faults of induction motors at an early stage. Early detection of motor faults results in fast unscheduled maintenance. In this study, a new artificial immune based support vector machine algorithm is proposed for fault diagnosis of induction motors. Support vector machines (SVMs) have become one of the most popular classification methods in soft computing, recently. However, classification accuracy depends on kernel and penalty parameters. Artificial immune system has abilities of learning, memory and self adaptive control. The kernel and penalizes parameters of support vector machine are tuned using artificial immune system. The training data of support vector machine are extracted from three phase motor current. The new feature vector is constructed based on park's vector approach. The phase space of this feature vector is constructed using nonlinear time series analysis. Broken rotor bar and stator short circuit faults are classified in combined phase space using support vector machines. The experimental data are taken from a three phase induction motor. One, two and three broken rotor bar faults and 10% short circuit of stator faults are detected successfully.
Considering the large amounts of data that is nowadays produced in the biochemistry (functional genomics) it is difficult to extract the information from the measurements. There is currently also a great interest in t...
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ISBN:
(纸本)9788096956265
Considering the large amounts of data that is nowadays produced in the biochemistry (functional genomics) it is difficult to extract the information from the measurements. There is currently also a great interest in the development of novel analytical technologies for rapid screening of disease symptoms in pharmaceutical and clinical applications. Modeling and simulation can provide a useful help in understanding the relations of the measured substances and to minimize the need for measurements. The BioChem library presented here is the first free Modelica library available for mathematical modeling of biochemical processes. Three examples are shown to illustrate the library. First, a simple insulin model is presented. Then a simplified model of cholesterol together with simulations are shown. Next, a simple drug model together with parameter estimation in NONMEN are presented. The BioChem library allows for fast and end-user friendly modeling of biomedical systems. The graphical user interface provides graphics similar to that used in the description of metabolic pathways in biochemistry.
This paper introduces the Graphingwiki extension toMoinMoin Wiki. Graphingwiki enables the deepened analysis of the Wiki data by augmenting it with semantic data in a simple, practical and easy-to-use manner. Visualis...
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This paper introduces the Graphingwiki extension toMoinMoin Wiki. Graphingwiki enables the deepened analysis of the Wiki data by augmenting it with semantic data in a simple, practical and easy-to-use manner. Visualisation tools are used to clarify the resulting body of knowledge so that only the data essential for an usage scenario is displayed. Logic inference rules can be applied to the data to perform automated reasoning based on the data. Perceiving dependencies among network protocols presents an example use case of the framework. The use case was applied in practice in mapping effects of software vulnerabilities on critical infrastructures.
A lifetime optimal algorithm, called MC-PRE, is presented for the first time that performs speculative PRE based on edge profiles. In addition to being computationally optimal in the sense that the total number of dyn...
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Ownership types support information hiding by providing statically enforceable object encapsulation based on an ownership tree. However ownership type systems impose fixed ownership and an inflexible access policy. Th...
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Empirical studies of novice programming typically rely on code solutions or test responses as the basis of their analyses. While such data can provide insight into novice programming knowledge, they say little about t...
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ISBN:
(纸本)1595934944
Empirical studies of novice programming typically rely on code solutions or test responses as the basis of their analyses. While such data can provide insight into novice programming knowledge, they say little about the programming processes in which novices engage. For those interested in improving novice programming environments, a key research question arises: How can we collect and analyze data on novice programming that will enable us (a) to analyze and compare the programming processes promoted by alternative novice programming environments, and (b) ultimately to build better novice programming environments? To address this question, we have collected a large video corpus of novices as they construct code solutions in various versions of ALVIS Live! [17], a novice programming environment. Through detailed post-hoc analyses of our video corpus, we have developed a methodology for compiling the moment-by-moment evolution of novice code solutions. Based on an analysis of a model code solution's key semantic components, our methodology enables researchers to document, on a second-by-second basis, (a) what part of a code solution a programmer is focusing on, and (b) where the semantic feedback provided by the programming environment is helping. Although it is time and labor intensive, our methodology provides researchers with a standard set of data and representations for comparing the programming processes promoted by alternative programming environments. Copyright 2006 ACM.
Since the late 1990s, we have been developing ALVIS, a new breed of algorithm visualization software that supports a novel, "studio based" approach to teaching introductory programming. In this approach, stu...
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This paper investigates the benefits of conducting leakage energy optimisations for data caches at link time for embedded applications. We introduce an improved algorithm for identifying and constructing the traces in...
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