39791908
Optimal Power Flow Method Using Evolutionary Programming
This paper reports on an evolutionary programming based method for solving the optimal power flow problem. The method incorporates an evolutionary programming based load flow solution. To demonstrate the global optimisation power of the new method it is applied to the IEEE30 bus test system with highly non-linear generator input/output cost curves and the results compared to those obtained using the method of steepest descent. The results demonstrate that the new method shows great promise for solving the optimal power flow problem when it contains highly non-linear devices.
33091738
SHAPES: A Novel Approach for Learning Search Heuristics in Under-Constrained Optimization Problems
Although much research in machine learning has been carried out on acquiring knowledge for problem-solving in many problem domains, little effort has been focused on learning search-control knowledge for solving optimization problems. This paper reports on the development of SHAPES, a system that learns heuristic search guidance for solving optimization problems in intractable, under-constrained domains based on the Explanation-Based Learning (EBL) framework. The system embodies two new and novel approaches to machine learning. First, it makes use of explanations of varying levels of approximation as a mean for verifying heuristic-based decisions, allowing heuristic estimates to be revised and corrected during problem-solving. The provision of such a revision mechanism is particularly important when working in intractable and under-constrained domains, where heuristics tend to be highly over-generalized, and hence at times will give rise to incorrect results. Second, it employs a new linear and quadratic programming-based weight-assignment algorithm formulated to direct search toward optimal solutions under best-first search. The algorithm offers a direct method for assigning rule strengths and, in so doing, avoids the need to address the credit-assignment problem faced by other iterative weight-adjustment methods.
6330846
Component-based software engineering: technologies, development frameworks, and quality assurance schemes
Component-based software development approach is based on the idea to develop software systems by selecting appropriate off-the-shelf components and then to assemble them with a well-defined software architecture. Because the new software development paradigm is very different from the traditional approach, quality assurance (QA) for component-based software development is a new topic in the software engineering community. In this paper, we survey current component-based software technologies, describe their advantages and disadvantages, and discuss the features they inherit. We also address QA issues for component-based software. As a major contribution, we propose a QA model for component-based software which covers component requirement analysis, component development, component certification, component customization, and system architecture design, integration, testing and maintenance.
31629768
Belief revision and possibilistic logic for adaptive information filtering agents
Prototypes of adaptive information agents have been developed to alleviate the problem of information overload on the Internet. However, the explanatory power and the learning autonomy of these agents are weak. A logic based framework for the development of information agents is appealing since semantic relationships among information objects can be captured and reasoned about. This sheds light on better explanatory power and higher learning autonomy of information agents. The paper illustrates how the AGM belief revision and possibilistic logic can be applied to develop the learning and the filtering components of adaptive information filtering agents. Their impact on the agents' learning autonomy and explanatory power is also discussed.
8168660
Towards an integrated toolset for program understanding
This paper describes some early results of a three-year project to develop an integrated toolset for program understanding. The implemented integration architecture involves both a global repository for all tools serviced by the architecture and a software bus serving communications among tools.
15591165
Using an integrated toolset for program understanding
This paper demonstrates the use of an integrated toolset for program understanding. By leveraging the unique capabilities of individual tools, and exploiting their power in combination, the resultant toolset is able to facilitate specific reverse engineering tasks that would otherwise be difficult or impossible. This is illustrated by applying the integrated toolset to several typical reverse engineering scenarios, including code localization, data flow analysis, pattern matching, system clustering, and visualization, using a mid-size production program as the reference system.
11030217
The open network laboratory
The Open Network Laboratory (ONL) is a remotely accessible network testbed of high performance routers which has been designed with an eye towards ease of use for users from the naïve to the expert. The system is built around a set of high-performance routers that are extendible and easily configurable through the Remote Laboratory Interface (RLI), an intuitive graphical interface. The RLI also makes it easy to configure packet filters in the routers, assign flows or flow aggregates to separate queues with configurable QoS and attach hardware monitoring points to real-time charts. The RLI's real-time charts and user data facility make it easy to directly view the effects of traffic as it moves through a router, allowing the user to gain better insight into system behavior and create compelling demonstrations. Each port of the router is equipped with an embedded processor that supports software plugins which allow users to extend the system's functionality. This paper describes the ONL and how it can be used in networking education. Our web site onl.arl.wustl.edu includes a short video and a tutorial.