Showing papers in "Expert Systems With Applications in 2010"
TL;DR: In this work, a versatile signal processing and analysis framework for Electroencephalogram (EEG) was proposed and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients.
Abstract: In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.
1,010 citations
TL;DR: The simulation results show that modified grey models have higher performances not only on model fitting but also on forecasting, and the modified GM(1,1) using Fourier series in time is the best in model fitting and forecasting.
Abstract: Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, the lack of ability of conventional analysis methods to forecast time series that are not smooth leads the scientists and researchers to resort to various forecasting models that have different mathematical backgrounds, such as artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, the accuracies of different grey models such as GM(1,1), Grey Verhulst model, modified grey models using Fourier Series is investigated. Highly noisy data, the United States dollar to Euro parity between the dates 01.01.2005 and 30.12.2007, are used to compare the performances of the different models. The simulation results show that modified grey models have higher performances not only on model fitting but also on forecasting. Among these grey models, the modified GM(1,1) using Fourier series in time is the best in model fitting and forecasting.
720 citations
TL;DR: The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks, and can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Abstract: Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
663 citations
TL;DR: An evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS is developed to help the industrial practitioners for the performance evaluation in a fuzzy environment.
Abstract: Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool.
619 citations
TL;DR: A hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system.
Abstract: During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system. A numerical example is proposed to illustrate an application of the proposed model.
531 citations
TL;DR: It has been detected that coupling emergent results in different areas, like those of ABC and complex dynamics, can improve the quality of results in some optimization problems.
Abstract: Artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes new ABC algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical ABC algorithm. Seven new chaotic ABC algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of ABC and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.
501 citations
TL;DR: Experimental results on the KDD CUP 1999 dataset show that the proposed new approach, FC-ANN, outperforms BPNN and other well-known methods such as decision tree, the naive Bayes in terms of detection precision and detection stability.
Abstract: Many researches have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS) when compared with traditional methods. However for ANN-based IDS, detection precision, especially for low-frequent attacks, and detection stability are still needed to be enhanced. In this paper, we propose a new approach, called FC-ANN, based on ANN and fuzzy clustering, to solve the problem and help IDS achieve higher detection rate, less false positive rate and stronger stability. The general procedure of FC-ANN is as follows: firstly fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy aggregation module, is employed to aggregate these results. Experimental results on the KDD CUP 1999 dataset show that our proposed new approach, FC-ANN, outperforms BPNN and other well-known methods such as decision tree, the naive Bayes in terms of detection precision and detection stability.
489 citations
TL;DR: The proposed method provides a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of the attributes.
Abstract: Type-2 fuzzy sets involve more uncertainties than type-1 fuzzy sets. They provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real world. In this paper, we present an interval type-2 fuzzy TOPSIS method to handle fuzzy multiple attributes group decision-making problems based on interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision-making process of the proposed method. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of the attributes.
456 citations
TL;DR: A new methodology is proposed to provide a simple approach to assess alternative projects and help the decision-maker to select the best one for National Iranian Oil Company by using six criteria of comparing investment alternatives as criteria in an AHP and fuzzy TOPSIS techniques.
Abstract: The evaluation and selection of projects before investment decision is customarily done using, technical and information. In this paper, proposed a new methodology to provide a simple approach to assess alternative projects and help the decision-maker to select the best one for National Iranian Oil Company by using six criteria of comparing investment alternatives as criteria in an AHP and fuzzy TOPSIS techniques. The AHP is used to analyze the structure of the project selection problem and to determine weights of the criteria, and fuzzy TOPSIS method is used to obtain final ranking. This application is conducted to illustrate the utilization of the model for the project selection problems. Additionally, in the application, it is shown that calculation of the criteria weights is important in fuzzy TOPSIS method and they could change the ranking. The decision-maker can use these different weight combinations in the decision-making process according to priority.
409 citations
TL;DR: This work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution employed in well-studied continuous optimization problems of engineering design and indicates that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence.
Abstract: Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature.
405 citations
TL;DR: In this paper, an intuitionistic fuzzy Choquet integral operator is proposed for multiple criteria decision making, where interactions phenomena among the decision making criteria are considered.
Abstract: For the real decision making problems, most criteria have inter-dependent or interactive characteristics so that it is not suitable for us to aggregate them by traditional aggregation operators based on additive measures. Thus, to approximate the human subjective decision making process, it would be more suitable to apply fuzzy measures, where it is not necessary to assume additivity and independence among decision making criteria. In this paper, an intuitionistic fuzzy Choquet integral is proposed for multiple criteria decision making, where interactions phenomena among the decision making criteria are considered. First, we introduced two operational laws on intuitionistic fuzzy values. Then, based on these operational laws, intuitionistic fuzzy Choquet integral operator is proposed. Moreover, some of its properties are investigated. It is shown that the intuitionistic fuzzy Choquet integral operator can be represented by some special t-norms and t-conorms, and it is also a generalization of the intuitionistic fuzzy OWA operator and intuitionistic fuzzy weighted averaging operator. Further, the procedure and algorithm of multi-criteria decision making based on intuitionistic fuzzy Choquet integral operator is given under uncertain environment. Finally, a practical example is provided to illustrate the developed approaches.
TL;DR: A ranking model is established that provides decision makers to assessing the prior order of regenerative technologies and indicates that the ''Proper scale'' is the most important evaluation criterion considered in overall experts.
Abstract: Due to the funding scale and complexity of lubricant regenerative technology, the selection of recycling technology and policy for waste lubricant oil can be viewed as a multiple-attribute decision process that is normally made by a review committee with experts from academia, industry, and the government. This study aims to provide a systematic approach towards the technology selection, in which two phase procedures are proposed. The first stage utilizes Fuzzy Delphi Method to obtain the critical factors of the regenerative technologies by interviewing the foregoing experts. In the second stage, Fuzzy Analytic Hierarchy Process is applied to find the importance degree of each criterion as the measurable indices of the regenerative technologies. This study considers eight kinds of regenerative technologies which have already been widely used, and establishes a ranking model that provides decision makers to assessing the prior order of regenerative technologies. The empirical study indicates that the ''Proper scale'' is the most important evaluation criterion considered in overall experts. The demonstration of how the prior order of regenerative technologies changes under various domains of experts is addressed as well.
TL;DR: This paper attempts to model and predict the return on stock price index of the Istanbul Stock Exchange (ISE) with ANFIS and reveals that the model successfully forecasts the monthly return of ISE National 100 Index with an accuracy rate of 98.3%.
Abstract: Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm is capable of accurately predicting stock market return. We attempt to model and predict the return on stock price index of the Istanbul Stock Exchange (ISE) with ANFIS. We use six macroeconomic variables and three indices as input variables. The experimental results reveal that the model successfully forecasts the monthly return of ISE National 100 Index with an accuracy rate of 98.3%. ANFIS provides a promising alternative for stock market prediction. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.
TL;DR: This paper presents an artificial bee colony clustering algorithm to optimally partition N objects into K clusters, using the Deb's rules to direct the search direction of each candidate.
Abstract: Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb's rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K-NM-PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required.
TL;DR: A new feature selection mechanism based on ant colony optimization is proposed in an attempt to combat the aforemention difficulties and denotes that the SVM-learning system has advantage when the information preprocessing is based on data mining technology.
Abstract: This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate redundant information. The system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method, we reduced SVM training data and overcame the disadvantage of very large data and slow processing speed when constructing SVM model. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat the aforemention difficulties. The method is then applied to find optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring, the ant colony optimization can mine the data more overall and accurate than the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It denotes that the SVM-learning system has advantage when the information preprocessing is based on data mining technology.
TL;DR: This research analyzes the relationship among knowledge management, as well as organizational learning and organizational innovation utilizing structural equation modeling and shows that organizational learning is the mediating variable between knowledge management andizational innovation.
Abstract: In knowledge economics, enterprises need to adapt and update its knowledge to keep their capability of innovation. Therefore, the relationship between knowledge management and organizational innovation is getting an important issue in research and in practical areas. However, without good capability of organizational learning, one organizational cannot retain some important knowledge management practices in it. This study selects samples based on Common Wealth Magazine's Top 1000 manufacturers and Top 100 financial firms in 2007 by mails. A questionnaire survey was conducted and 327 valid replies were received. This research analyzes the relationship among knowledge management, as well as organizational learning and organizational innovation utilizing structural equation modeling. The results show that organizational learning is the mediating variable between knowledge management and organizational innovation. Just like a system, knowledge management is an important input, and organizational learning is a key process, then organizational innovation is a critical output.
TL;DR: Four independent classification schemas were applied and a comparative study was carried out for effective diagnosis of Parkinson's diseases, finding neural networks classifier yields the best results.
Abstract: In this paper, different types of classification methods are compared for effective diagnosis of Parkinson's diseases. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approaches described in this paper purpose to efficiently distinguish healthy individuals. Four independent classification schemas were applied and a comparative study was carried out. These are Neural Networks, DMneural, Regression and Decision Tree respectively. Various evaluation methods were employed for calculating the performance score of the classifiers. According to the application scores, neural networks classifier yields the best results. The overall classification score for neural network is 92.9%. Moreover, we compared our results with the result that was obtained by kernel support vector machines [Singh, N., Pillay, V., & Choonara, Y. E. (2007). Advances in the treatment of Parkinson's disease. Progress in Neurobiology, 81, 29-44]. To the best of our knowledge, our correct classification score is the highest so far.
TL;DR: The model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface Roughness performance measure.
Abstract: This paper presents the ANN model for predicting the surface roughness performance measure in the machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring surface roughness. A revision of several previous studies associated with the modelling issue is carried out to assess how capable ANN is as a technique to model the problem. Based on the studies conducted by previous researchers, the abilities and limitations of the ANN technique for predicting surface roughness are highlighted. Utilization of ANN-based modelling is also discussed to show the required basic elements for predicting surface roughness in the milling process. In order to investigate how capable the ANN technique is at estimating the prediction value for surface roughness, a real machining experiment is referred to in this study. In the experiment, 24 samples of data concerned with the milling operation are collected based on eight samples of data of a two-level DOE 2^k full factorial analysis, four samples of centre data, and 12 samples of axial data. All data samples are tested in real machining by using uncoated, TiAIN coated and SN"T"R coated cutting tools of titanium alloy (Ti-6A1-4V). The Matlab ANN toolbox is used for the modelling purpose with some justifications. Feedforward backpropagation is selected as the algorithm with traingdx, learngdx, MSE, logsig as the training, learning, performance and transfer functions, respectively. With three nodes in the input layer and one node in the output layer, eight networks are developed by using different numbers of nodes in the hidden layer which are 3-1-1, 3-3-1, 3-6-1, 3-7-1, 3-1-1-1, 3-3-3-1, 3-6-6-1 and 3-7-7-1 structures. It was found that the 3-1-1 network structure of the SN"T"R coated cutting tool gave the best ANN model in predicting the surface roughness value. This study concludes that the model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.
TL;DR: A fuzzy multicriteria decision-making methodology is suggested for the selection among energy policies, based on the analytic hierarchy process (AHP) under fuzziness, which determines the best energy policy for Turkey.
Abstract: Since the correct energy policy affects economic development and environment, the most appropriate energy policy selection is excessively important. Recently some studies have concentrated on selecting the best energy policy and determining the best energy alternatives. In most of these studies, multicriteria and fuzzy approaches to energy policy making are frequently used. The fuzzy set theory is a powerful tool to treat the uncertainty in case of incomplete or vague information. In this paper, a fuzzy multicriteria decision-making methodology is suggested for the selection among energy policies. The methodology is based on the analytic hierarchy process (AHP) under fuzziness. It allows the evaluation scores from experts to be linguistic expressions, crisp or fuzzy numbers. In the application of the proposed methodology, the best energy policy is determined for Turkey.
TL;DR: This study developed a way to increase recommendation effectiveness by incorporating social network information into collaborative filtering, and indicated that more accurate prediction algorithms can be produced by incorporates social network Information into CF.
Abstract: When people make decisions, they usually rely on recommendations from friends and acquaintances Although collaborative filtering (CF), the most popular recommendation technique, utilizes similar neighbors to generate recommendations, it does not distinguish friends in a neighborhood from strangers who have similar tastes Because social networking Web sites now make it easy to gather social network information, a study about the use of social network information in making recommendations will probably produce productive results In this study, we developed a way to increase recommendation effectiveness by incorporating social network information into CF We collected data about users' preference ratings and their social network relationships from a social networking Web site Then, we evaluated CF performance with diverse neighbor groups combining groups of friends and nearest neighbors Our results indicated that more accurate prediction algorithms can be produced by incorporating social network information into CF
TL;DR: The steps of fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are considered, incorporating a new concept for the ranking of the alternatives based on the veto threshold.
Abstract: Selection of qualified human resources is a key success factor for an organization. The complexity and importance of the problem call for analytical methods rather than intuitive decisions. The aim of this paper is to support adequately the decision making process. The steps of fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are considered, incorporating a new concept for the ranking of the alternatives. This is based on the veto threshold, a critical characteristic of the main outranking methods. The ultimate decision criterion is not the similarity to the ideal solution but the distance of the alternatives from the veto set by the decision makers. Additionally, a real life application on the selection of a top management team member shows the practical implications.
TL;DR: Experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity, so the approach has possibility for bearing incipient fault diagnosis.
Abstract: A bearing fault diagnosis method has been proposed based on multi-scale entropy (MSE) and adaptive neuro-fuzzy inference system (ANFIS), in order to tackle the nonlinearity existing in bearing vibration as well as the uncertainty inherent in the diagnostic information. MSE refers to the calculation of entropies (e.g. appropriate entropy, sample entropy) across a sequence of scales, which takes into account not only the dynamic nonlinearity but also the interaction and coupling effects between mechanical components, thus providing much more information regarding machinery operating condition in comparison with traditional single scale-based entropy. ANFIS can benefit from the decision-making under uncertainty enabled by fuzzy logic as well as from learning and adaptation that neural networks provide. In this study, MSE and ANFIS are employed for feature extraction and fault recognition, respectively. Experiments were conducted on electrical motor bearings with three different fault categories and several levels of fault severity. The experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity. Thus, the proposed approach has possibility for bearing incipient fault diagnosis.
TL;DR: The consciousness is used to automatically adjust parameter values and the pseudo-random number generator is also replaced by the low-discrepancy sequences for initialization of the harmony memory.
Abstract: Recently, a new meta-heuristic optimization algorithm - harmony search (HS) with continuous design variables was developed. This algorithm is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to select suitable parameter values. In this paper, we used the consciousness (i.e., harmony memory) to automatically adjust parameter values. In addition, the pseudo-random number generator is also replaced by the low-discrepancy sequences for initialization of the harmony memory. Finally, the experimental results revealed the superiority of the proposed method to the original HS and recently developed variants.
TL;DR: The proposed method provides a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of attributes.
Abstract: In this paper, we present a new method to handle fuzzy multiple attributes group decision-making problems based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. First, we present the arithmetic operations between interval type-2 fuzzy sets. Then, we present a fuzzy ranking method to calculate the ranking values of interval type-2 fuzzy sets. We also make a comparison of the ranking values of the proposed method with the existing methods. Based on the proposed fuzzy ranking method and the proposed arithmetic operations between interval type-2 fuzzy sets, we present a new method to handle fuzzy multiple attributes group decision-making problems. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of attributes.
TL;DR: Fault diagnostics of spur bevel gear box is treated as a pattern classification problem and the use of discrete wavelets for feature extraction and artificial neural network for classification is investigated.
Abstract: An efficient predictive plan is needed for any industry because it can optimize the resources management and improve the economy plant, by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in the productive processes are caused for gear box, they began its deterioration from early stages, also called incipient level. The extracted features from the DWT are used as inputs in a neural network for classification purposes. The results show that the developed method can reliably diagnose different conditions of the gear box. The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This paper investigates the use of discrete wavelets for feature extraction and artificial neural network for classification.
TL;DR: The proposed method alleviates the information overload effect that is inherent in the environment of electronic marketplaces, facilitates an easier elicitation of user preferences through the reduction of necessary user input and reduces computational complexity, in terms of the number of linear programs to be solved, in comparison with the original FPP method.
Abstract: Supplier selection is a critical and demanding task for companies that participate in electronic marketplaces to find suppliers and to execute electronically their transactions This paper is aimed to suggest a fresh approach for decision support enabling effective supplier selection processes in electronic marketplaces We introduce an evaluation method with two stages: initial screening of the suppliers through the enforcement of hard constraints on the selection criteria and final supplier evaluation through the application of a modified variant of the Fuzzy Preference Programming (FPP) method The proposed method alleviates the information overload effect that is inherent in the environment of electronic marketplaces, facilitates an easier elicitation of user preferences through the reduction of necessary user input (ie pairwise comparisons) and reduces computational complexity, in terms of the number of linear programs to be solved, in comparison with the original FPP method The FPP method is adopted and modified accordingly in order to tackle the issue of inconsistency/uncertainty of human preference models Our approach is demonstrated with the example of a hypothetical metal manufacturing company that finds and selects suppliers in the environment of an electronic marketplace
TL;DR: An automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing and shows that although Daubechies 44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelets-based processing.
Abstract: This paper introduces an automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing. Vibration signals recorded from two experimental set-ups were processed for gears and bearing conditions. Four statistical features were selected: standard deviation, variance, kurtosis, and fourth central moment of continuous wavelet coefficients of synchronized vibration signals (CWC-SVS). In this research, the mother wavelet selection is broadly discussed. 324 mother wavelet candidates were studied, and results show that Daubechies 44 (db44) has the most similar shape across both gear and bearing vibration signals. Next, an automatic feature extraction algorithm is introduced for gear and bearing defects. It also shows that the fourth central moment of CWC-SVS is a proper feature for both bearing and gear failure diagnosis. Standard deviation and variance of CWC-SVS demonstrated more appropriate outcome for bearings than gears. Kurtosis of CWC-SVS illustrated the acceptable performance for gears only. Results also show that although db44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelet-based processing.
TL;DR: A method based on the ET-WG and ET-OWG operators for multiple attribute group decision-making is presented and the ranking of alternative or selection of the most desirable alternative(s) is obtained by the comparison of 2-tuple linguistic information.
Abstract: With respect to multiple attribute group decision-making problems with linguistic information of attribute values and weight values, a group decision analysis is proposed. Some new aggregation operators are proposed: the extended 2-tuple weighted geometric (ET-WG) and the extended 2-tuple ordered weighted geometric (ET-OWG) operator and properties of the operators are analyzed. Then, A method based on the ET-WG and ET-OWG operators for multiple attribute group decision-making is presented. In the approach, alternative appraisal values are calculated by the aggregation of 2-tuple linguistic information. Thus, the ranking of alternative or selection of the most desirable alternative(s) is obtained by the comparison of 2-tuple linguistic information. Finally, a numerical example is used to illustrate the applicability and effectiveness of the proposed method.
TL;DR: A fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed and enables managers to deal with heterogeneous information.
Abstract: Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study.
TL;DR: Analysis results show that user satisfaction, perceived usefulness, perceived enjoyment, and perceived fee are an important part of the formation of MDS continuance intention.
Abstract: Due to the rapid growth of mobile data service (MDS), research into MDS continuance has recently emerged as an important issue in information systems (IS) and marketing literature. This study develops an integrated model designed to predict a user's continuance behavior toward MDS based on the expectation-confirmation model (ECM) and the theory of planned behavior (TPB). Empirical data collected from 207 users who had prior experience with MDS and was tested against the proposed research model using structure equation modeling. Analysis results show that user satisfaction, perceived usefulness, perceived enjoyment, and perceived fee are an important part of the formation of MDS continuance intention. Furthermore, the two components of the TPB, subject norm and perceived behavioral control, also have a significant impact on MDS continuance intention. Overall, this study provides evidence that an integrated model has a better explanatory power of MDS continuance compared to either model considered alone.