We introduce and examine a tool for analysing logics. This algebraic tool, coming from some ideas introduced by J. Piaget, provides condensed information about a logic (with emphasis on the behavior of a unary symbol)...
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A Dynamic Software Product Line (DSPL) allows the generation of products that can adapt dynamically according to changes in requirements or environment at runtime. This runtime adaptation is often made by the activati...
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With a growing demand for new technologies, concepts such as the Internet of Everything (IoE) - in which smart sensors (humans and machines) connect, communicate, and share information from the surrounding environment...
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The study proposes a combination of the function points model for software estimation with the ADISSA methodology for systems analysis and design. This combined approach, which is supported by a software tool, enables...
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In this paper, we first provide a new theoretical understanding of the Evidence Pre-propagated Importance Sampling algorithm (EPIS-BN) (Yuan & Druzdzel 2003;2006b) and show that its importance function minimizes t...
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In this paper, we describe the Graphics Processing Unit (GPU) implementation of our City-LES code on detailed large eddy simulations, including the multi-physical phenomena on fluid dynamics, heat absorption and refle...
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RISE is a well-known multi-strategy learning algorithm that combines rule induction and instance-based learning. It achieves higher accuracy than some state-of-the-art learning algorithms, but for large data sets it h...
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The main knowledge management challenges are to capture, store and reuse contextual knowledge generated during interactions that occur daily in an organization. In this paper, we propose an activity context-aware arch...
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We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict nume...
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ISBN:
(纸本)0780370449
We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
Many components are usually created within a Domain engineering (DE) process. These components may exchange a great number of messages among them, and many times this interaction occurs within a specific group of comp...
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