Model‐based code generation for HLA federates

This paper addresses the problem of automated code generation for a High Level Architecture compliant federate application given its behavior model. The behavior model is a part of the architectural model of a federation that the federate can participate in. The federate behavior model is based on Live Sequence Charts, adopted as the behavioral specification formalism in the Federation Architecture Metamodel (FAMM). FAMM serves as a formal language for describing federation architectures. An objective is to help the testing of federation architecture by means of its prototype implementation early in the development lifecycle. A further objective is to help developers construct complete federate applications that are well modularized. The approach to achieve these objectives is aspect‐oriented in that the generated code, which handles the federate's interactions with a federation execution, serves as the base code, where the computation logic is to be weaved as an aspect. Copyright © 2009 John Wiley & Sons, Ltd.     

Fast computation of the prime implicants by exact direct-cover algorithm based on the new partial ordering operation rule

In this study, a novel OFF-set based direct-cover Exact Minimization Algorithm (EMA) is proposed for single-output Boolean functions represented in a sum-of-products form. To obtain the complete set of prime implicants covering the given Target Minterm (ON-minterm), the proposed method uses OFF-cubes (OFF-minterms) expanded by this Target Minterm. The amount of temporary results produced by this method does not exceed the size of the OFF-set. In order to achieve the goal of this study, which is to make faster computations, logic operations were used instead of the standard operations. Expansion OFF-cubes, commutative absorption operations and intersection operations are realized by logic operations for fast computation. The proposed minimization method is tested on several classes of benchmarks and then compared with the ESPRESSO algorithm. The results show that the proposed algorithm obtains more accurate and faster results than ESPRESSO does.     

Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line

Product recovery involves the recovery of materials and components from returned or end-of-life products. Disassembly, an element of product recovery, is the systematic separation of an assembly into its components, subassemblies or other groupings. Stricter environmental regulations together with dramatic decrease in natural resources and landfills have increased the importance of disassembly as all product recovery options require some level of disassembly. Due to changes made during the lifetime of a product by customers or service personnel, the number and the version of components prior to disassembly is unknown. Customers may also discriminate between and demand different versions of components. The existence of non-functional components further adds to the uncertainty associated with disassembly yield. Sensors implanted into products during their production can address this uncertainty by providing information on the number, condition and version of components prior to disassembly. In this study, we evaluate the impact of sensor embedded products (SEPs) on the various performance measures of a washing machine (WM) disassembly line controlled by a multi-kanban system, which takes into consideration the highly stochastic behavior of the line while managing material and kanban flows. First, separate design of experiments studies based on orthogonal arrays are performed for conventional products and SEPs. In order to observe the response of each experiment, detailed discrete event simulation (DES) models for both types of products are developed considering the precedence relationships among the components of a WM. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. According to the results, SEPs provide significant reductions in all costs (viz., backorder, holding, disassembly, disposal, testing and transportation) while increasing revenue and profit.     

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

Abstract: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.     

Prediction of deflection of reinforced concrete shear walls

Reinforced concrete shear walls are used in tall buildings for efficiently resisting lateral loads. Due to the low tensile strength of concrete, reinforced concrete shear walls tend to behave in a nonlinear manner with a significant reduction in stiffness, even under service loads. To accurately assess the lateral deflection of shear walls, the prediction of flexural and shear stiffness of these members after cracking becomes important. In the present study, an iterative analytical procedure which considers the cracking in the reinforced concrete shear walls has been presented. The effect of concrete cracking on the stiffness and deflection of shear walls have also been investigated by the developed computer program based on the iterative procedure. In the program, the variation of the flexural stiffness of a cracked member has been evaluated by ACI and probability-based effective stiffness model. In the analysis, shear deformation which can be large and significant after development of cracks is also taken into account and the variation of shear stiffness in the cracked regions of members has been considered by using effective shear stiffness model available in the literature. Verification of the proposed procedure has been confirmed from series of reinforced concrete shear wall tests available in the literature. Comparison between the analytical and experimental results shows that the proposed analytical procedure can provide an accurate and efficient prediction of both the deflection and flexural stiffness reduction of shear walls with different height to width ratio and vertical load. The results of the analytical procedure also indicate that the percentage of shear deflection in the total deflection increases with decreasing height to width ratio of the shear wall.     

A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems.     

Analyzing IT maintenance outsourcing decision from a knowledge management perspective

IT outsourcing is a complex and opaque decision problem. Managers facing a decision about IT outsourcing have difficulty in framing what needs to be thought about further in their discourses. Framing is one of the most crucial steps of human decision making and needs to be assisted to better understand a decision situation. In this research, we examine a number of decision primitives in the context of an IT outsourcing decision situation. We demonstrate how the decision primitives can be employed so that managers can probe deep to better understand a decision situation and to establish a decision basis. In the organizational setting, we exemplify the use of the decision primitives in relation to the perceived outsourcing implications for the managers looking for assistance in accommodating a knowledge management perspective on IT outsourcing. Consequently, we induce insight and a guideline on how to use knowledge management for effective outsourcing in one of the leading financial institutes in Europe.

An incremental neural network for tissue segmentation in ultrasound images

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.