42199214
A Technique for Improving the Convergence Characteristic of Genetic Algorithms and Its Application to a Genetic-Based Load Flow Algorithm
This paper is devoted to the development of a technique for the enhancement of the convergence of genetic algorithms. Based on the concept of solution acceleration, a technique is proposed and applied to a constrained-genetic-algorithm load-flow algorithm CGALF recently developed for solving the problem of evaluating the voltage profile and power flow in electric power networks. The enhanced CGALF algorithm is applied to a practical power system to illustrate the effectiveness of the developed method.
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.
34471388
Compressing and Restoring Multiple Images Using Variable Threshold Values
3178984
Work-conserving distributed schedulers for Terabit routers
Buffered multistage interconnection networks offer one of the most scalable and cost-effective approaches to building high capacity routers. Unfortunately, the performance of such systems has been difficult to predict in the presence of the extreme traffic conditions that can arise in the Internet. Recent work introduced distributed scheduling, to regulate the flow of traffic in such systems. This work demonstrated, using simulation and experimental measurements, that distributed scheduling can deliver robust performance for extreme traffic. Here, we show that distributed schedulers can be provably work-conserving for speedups of 2 or more. Two of the three schedulers we describe were inspired by previously published crossbar schedulers. The third has no direct counterpart in crossbar scheduling. In our analysis, we show that distributed schedulers based on blocking flows in small-depth acyclic flow graphs can be work-conserving, just as certain crossbar schedulers based on maximal bipartite matchings have been shown to be work-conserving. We also study the performance of practical variants of these schedulers when the speedup is less than 2, using simulation.
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.
1578831
Pose estimation for augmented reality applications using genetic algorithm
This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.
18706172
Connecting technical communicators with technical developers
Most program documentation is written by developers for developers. This often results in documentation that is less than satisfactory in terms of quality, relevance, and longevity of information to developers who will be responsible for subsequent enhancements to the product. This paper outlines an approach to producing effective program documentation by connecting technical communicators and technical developers in a pair-wise manner. This approach is modeled on the development methodology advocated by eXtreme Programming (XP). Programmers who adopt XP work in pairs, and thus are in constant communication with one another. By pairing a technical communicator with a technical developer, many of the same benefits can be gained. Rather than eschewing program documentation, the paired communicator and developer instead produce high-quality documentation during the development process, with little extra effort, resulting in a more maintainable software application. The paper also describes some recent experiences in pairing a senior person and a junior person in communicator and developer roles to enhance program documentation of a long-lived and popular research application.