Optimal Set Of Components Research Articles

Validation of Software Component Selection Algorithms based on Clustering

Objectives:To search the components that provides desired functionality, from the finite set of component set by the use of software component selection process. The selection process helps in choosing the optimal set of components from the third party repository. Methods/Statistical Analysis:In order to select the optimal set of component having multiple attributes, clustering is found to most suitable technique as revealed in the literature. This paper presents the validation of clustering based algorithms used for software component selection. It mainly covers the fuzzy-c means clustering and subtractive clustering. It also includes the earlier software component selection techniques proposed by the authors, hybrid XOR based clustering technique and fuzzy relation based fuzzy clustering. Findings:The Fuzzy c-means technique requires the need of mentioning the number of clusters centers in advanceand the radii of the cluster in case of Subtractive clustering are required. The disadvantage of Hybrid XOR based clustering is its dependency on subjective judgment of the developer. The FREFCOSCO algorithm has eliminated the usage of similarity index. It is able to deal with multi-attributes component and can generate the optimal set of components. Application/Improvements:The algorithms are validated on a set of components taken from an online repository. The improvement in the FREFCOSCO algorithm can be done by using an appropriate validity mechanism.

Learning Hierarchical Features for Scene Labeling

Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a $(320\times 240)$ image labeling in less than a second, including feature extraction.

Only the Best Can Make It: Optimal Component Selection

In Component-based Software Engineering (CBSE), the construction of cost-optimal component systems is a nontrivial task. It requires not only to optimally select components and their adaptors but also to take their interplay into account. In this paper, by employing methods from the area of compiler construction and especially optimizing code generation, we present a unified approach to the construction of component systems, which allows us to first select an optimal set of components and adaptors and afterwards to create a working system by providing the necessary glue code. With our two case studies, we demonstrate that our approach is efficient and generally applicable in practical scenarios.

Comparison of Three Non-derivative Optimization Methods with a Genetic Algorithm for Component Selection

SUMMARY This paper presents a design method to automate the selection of an optimal set of components for a given layout and set of design specifications. The systems considered in this research are complex, multi-energy domain systems which have transient performance design specifications. Selecting components for such systems requires a wide range of knowledge, including knowledge of system design, modelling, dynamic analysis, computer programming and optimization. The component selection method developed in this work consists of a genetic algorithm and a generalized constraint handling procedure. A genetic algorithm is a flexible, evolutionary, combinatorial optimization technique that lends itself well to problems with discrete solution spaces. This component selection approach is compared with other approaches: a geometric simplex algorithm; an enumerative search; a discrete hill-climbing routine; and a random search. The design of a hydraulic lift system was used as the test problem. This method (th...