A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm

As genetic algorithm parameters vary depending on different problem types when applying genetic algorithm to reach global optimum, appropriate design value selection has significant impact on the efficiency of genetic algorithm. However, most users adjust parameters manually based on the reference values of previous literature. Such trial-and-error method is time-consuming, ineffective, and often it could not locate the optimal combination. Therefore, in flowshop scheduling problems, this research anticipates to complete optimal parameter combination design in genetic algorithm using Taguchi experimental design. According to the research results, different ways of producing initial solution have significant influence on this research topic. Consequently, confirmation experiment is conducted using the optimal parameter combination obtained from the research results. It is found that the predicted value of signal-to-noise ratio (S/N ratio) and its actual value exists deviation of 0.238%, indicating repetitiveness and robustness of the obtained parameter combination. Hence, this research method can effectively reduce time spent on parameter design using genetic algorithm and increase efficiency of algorithm.     

Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H₂/H_∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H₂/H_∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H₂/H_∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.     

The language of quarterly reports as an indicator of change in the company’s financial status

This paper adopts a multi-methodological approach to information systems research in order to produce new information through data mining. This approach is particularly suitable for mining material that consists of both qualitative and quantitative information. The contents of quarterly reports from three telecommunications companies were compared. The study focused on the years 2000–2001, a period of economic decline for many IT companies. The central quantitative data, reflected by seven financial ratios, were visualised using self-organising maps. The qualitative data, consisting of the textual contents of the reports, were visualised using collocational networks; these showed the relationships between the central concepts in the texts. As the visualisations of the contents were compared, certain patterns could be found. The results seemed to suggest that changes in the networks indicated future changes in the self-organising maps. In the cases studied, a change in the textual data usually indicated a change in the financial data in the following quarter. This may be a consequence of the fact that the texts reflected the plans and future expectations of management, whereas the financial ratios reflected the current financial situation of the company.     

An objective comparison of the cost effectiveness of three testing methods

Branch testing is a well established method for exercising software. JJ-path testing, whilst employed by some practitioners, is less popular, and the testing of JJ-pairs finds few adherents. In this paper an objective, practical study of the cost-effectiveness of these three testing methods is reported. The effectiveness of each method is assessed, in the presence of infeasible paths, not only on its ability to cover the specific structural element of code that it targets, but also on its ability to cover the structural elements targeted by the other two methods – the collateral coverage it achieves. The assessment is based on the results derived from experiments in which each of the three methods is applied to 35 units of program code.     

Forming consensus in the networks of knowledge

Information granules, such as e.g., fuzzy sets, capture essential knowledge about data and the key dependencies between them. Quite commonly, we may envision that information granules (fuzzy sets) have become a result of fuzzy clustering and therefore could be succinctly represented in the form of some fuzzy partition matrices. Interestingly, the same data set could be represented from various standpoints and this multifaceted view yields a collection of different partition matrices being reflective of the higher-order granular knowledge about the data. The levels of specificity of the clusters the data are organized into could be quite different—the larger the number of clusters, the more detailed insight into the structure of data becomes available. Given the granularity of the resulting constructs (rather than plain data themselves), one could view a collection of partition matrices as a certain type of a network of knowledge. Considering a variety of sources of knowledge encountered across the network, we are interested in forming consensus between them. In a nutshell, this leads to the construction of certain fuzzy partition matrices which “reconcile” the knowledge captured by the individual partition matrices. Given that the granularity of the sources of knowledge under consideration could vary quite substantially, we develop a unified optimization perspective by introducing fuzzy proximity matrices that are induced by the corresponding partition matrices. In the sequel, the optimization is realized on a basis of these proximity matrices. We offer a detailed algorithm and illustrate its performance using a series of numeric experiments.     

Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy.     

A two-pass approach for handling out-of-vocabulary words in a large vocabulary recognition task

This paper addresses the problem of recognizing a vocabulary of over 50,000 city names in a telephone access spoken dialogue system. We adopt a two-stage framework in which only major cities are represented in the first stage lexicon. We rely on an unknown word model encoded as a phone loop to detect OOV city names (referred to as ‘rare city’ names). We use SpeM, a tool that can extract words and word-initial cohorts from phone graphs from a large fallback lexicon, to provide an N-best list of promising city name hypotheses on the basis of the phone graph corresponding to the OOV. This N-best list is then inserted into the second stage lexicon for a subsequent recognition pass.Experiments were conducted on a set of spontaneous telephone-quality utterances; each containing one rare city name. It appeared that SpeM was able to include nearly 75% of the correct city names in an N-best hypothesis list of 3000 city names. With the names found by SpeM to extend the lexicon of the second stage recognizer, a word accuracy of 77.3% could be obtained. The best one-stage system yielded a word accuracy of 72.6%. The absolute number of correctly recognized rare city names almost doubled, from 62 for the best one-stage system to 102 for the best two-stage system. However, even the best two-stage system recognized only about one-third of the rare city names retrieved by SpeM. The paper discusses ways for improving the overall performance in the context of an application.     

Minimal communication cost software construction in the Internet environment

This paper investigates the issue of building software in the Internet environment, where local area network (LAN) based systems are interconnected by links with different bandwidth and do not share file systems. The software is modeled as a directed acyclic graph. Each node in the graph represents a logical step in processing the software while the edges describe the order of execution. The problem is to construct the software at a particular LAN with minimum Internet communication cost. An optimal polynomial algorithm, SOFTCON, with time complexity is presented, where and are the number of nodes and edges in the graph describing the software respectively, is the number of LANs in the Internet environment, and is the time complexity of the network flow algorithm on the flow network with nodes and edges transformed from the directed acyclic graph of the software. Received: 6 December 1995 / 1 May 1996     

System gains configuration and coordination of redundant degrees of freedom by genetic algorithms for multi-axis machine system in manufacturing

In multi-axis machine tool systems, the configuration of system gains and the coordination of redundant degrees of freedom are often a problem of insurmountable difficulty. This study explores the use of a nontraditional scheme, the genetic algorithm, in the configuration of system gains and exploitation of redundant degrees of freedom. The off-line gains configuration functioned as a kind of system design which may serve as a starting point for on-line adaptation. The allocation of redundant DOF was done on-line. The success in this work inspired the idea of future on-line GA application and the possibility of integration of GA with other non-traditional algorithms for manufacturing.     

Experiments and simulations of the mechanical performance of repaired panels

Firstly, the compress experiment is undertaken to investigate the efficiency of repaired panels in this paper, and then modeling of the mechanical behavior of the repaired composite panel under compressive static load is conducted by using of the finite element method. The effect of geometric non-linearity on the stress–strain response is considered in the numeric analysis. Fatherly, the user material subroutine (UMAT) is integrated with the ABAQUS package with the geometric non-linearity effect for studying the damage initiation and its progression in the composite structure, and quadrilateral, linear, thick shell elements (S8R) are adopted. Finally, the predicted strain distribution, damage evolution and strength of the laminate are compared with the test results.