Taichung, Taiwan
Taichung, Taiwan

Ling Tung University is a private university founded in 1964. Ling Tung University is located in Nan-tun District, Taichung City, Taiwan. Wikipedia.

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Hsu L.-C.,Ling Tung University
Expert Systems with Applications | Year: 2010

In this article, an improved nonlinear grey Bernoulli model by using genetic algorithms to solve the optimal parameter estimation problem of small amount of data used in the forecast is proposed. The time series data of Taiwan's integrated circuit industry (1990-2007) was used as the test data set. In addition, the mean absolute percentage error and the root mean square percentage error were used to compare the performance of the forecast models. The results showed that the improved nonlinear grey Bernoulli model is more accurate and performs better than the traditional GM(1,1) model and grey Verhulst model. Moreover, the optimum mechanisms indeed improve the grey model of prediction accuracy by using genetic algorithms approach. © 2009 Elsevier Ltd. All rights reserved.

Wan S.,Ling Tung University
Environmental Earth Sciences | Year: 2013

Generation of landslide susceptibility maps is important for engineering geologists and geomorphologists. The goal of this study is to generate a reliable susceptibility map based on digital elevation modeling and remote sensing data through clustering technique. This study focused on the landslide problems on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Two stages of analysis were used to extract the dominant attributes and thresholds: (1) calculate the entropy with regard to the measure of influenced variables to the occurrence of landslide and (2) use the clustering analysis K-means with particle swarm optimization (KPSO) to resolve the difficulties in creating landslide susceptibility maps. The knowledge scope with regard to core factors and thresholds are solved. The self-organization map (SOM) is used as a parallel study for comparison. The overall accuracy of the susceptibility map is 86 and 77 % for KPSO and SOM, respectively. Then, the susceptibility maps are drawn and verifications made. The generation of a susceptibility map is useful for decision makers and managers to handle the landslide risk area. © 2012 Springer-Verlag.

This study proposes a method of cluster validity index that simultaneously provide the measurements of goodness of clustering on clustered data and of classification accuracy for complicated information systems based upon the PBMF-index method and rough set (RS) theory. The maximum value of this index, called the Huang-index, not only provides the best partitioning, but also obtains the optimal accuracy of classification for the approximation sets. The traditional PBMF-index method is only used to ensure the formation of a small number of compact clusters with large separation between at least two clusters. In contrast to the traditional PBMF-index method, the Huang-index method extends the applications of unsupervised optimal cluster to the fields of classification. In the proposed algorithm, all the attributes of the data are first clustered into groups using the Fuzzy C-means (FCM) method. The clustered data are then used to identify approximate regions and classification accuracy and to calculate centroids of clusters for decision attribute based on the RS theory. Finally, all those calculated data are put into the proposed index method to find the cluster validity index. The validity of the proposed approach is demonstrated using the data derived from a hypothetical function of two independent variables and electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The clustering results obtained using the proposed method are compared with the results obtained using the traditional PBMF-index partition method. The effects of the number of clusters on the partitions of clusters and the RS regions are systematically examined and compared. The results show that the proposed Huang-index method not only yields a superior clustering capability than the traditional clustering algorithm, but also yields a reliable classification and obtains a set of suitable decision rules extracted from the RS theory. © 2010 Elsevier Ltd. All rights reserved.

Hsu L.-C.,Ling Tung University
Expert Systems with Applications | Year: 2011

Numerous forecasting models have been developed. Each has its own conditions of application. However, it has always been an important research objective to improve prediction accuracy with a small amount of data. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting models still have some potential problems that need to be improved. Therefore, this study proposed an improved transformed grey model based on a genetic algorithm (ITGM(1,1)), and used the output of the opto-electronics industry in Taiwan from 1990 to 2008 as an example for verification. Three grey forecasting models, GM(1,1), rolling GM(1,1), and the transformed GM(1,1), were chosen for the purpose of comparison with ITGM(1,1) by mean absolute percent error and root mean square percent error. The results show that ITGM(1,1) is more accurate than the other three models in both in-sample and out-of-sample forecasting performance, and can greatly improve the accuracy of short-term forecasts. © 2011 Elsevier Ltd. All rights reserved.

Wang K.-C.,Ling Tung University
Expert Systems with Applications | Year: 2011

Nowadays customers choose products strictly in terms of their specific demands. How to quickly and accurately catch customers' feelings and transform them into design elements and vice versa becomes an important issue. This study explores the bi-directional relationship between customers' demands or needs and product forms by using a novel integral approach. High-price machine tools are used as our demonstration target. This integral approach adopts the "grey system theory (GST)", and the state-of-the-art machine learning based modeling formalism "support vector regression (SVR)" in the "Kansei engineering (KE)" process. The GST is used to effectively determine the influence weighting of form parameters on product images and the SVR is used to precisely establish the mapping relationship between product form elements and product images. Furthermore, for practical concerns, a user-friendly design hybrid design expert system was developed based on the proposed novel integral schemes. © 2011 Elsevier Ltd. All rights reserved.

Models based on data mining and machine learning techniques have been developed to detect the disease early or assist in clinical breast cancer diagnoses. Feature selection is commonly applied to improve the performance of models. There are numerous studies on feature selection in the literature, and most of the studies focus on feature selection in supervised learning. When class labels are absent, feature selection methods in unsupervised learning are required. However, there are few studies on these methods in the literature. Our paper aims to present a hybrid intelligence model that uses the cluster analysis techniques with feature selection for analyzing clinical breast cancer diagnoses. Our model provides an option of selecting a subset of salient features for performing clustering and comprehensively considers the use of most existing models that use all the features to perform clustering. In particular, we study the methods by selecting salient features to identify clusters using a comparison of coincident quantitative measurements. When applied to benchmark breast cancer datasets, experimental results indicate that our method outperforms several benchmark filter- and wrapper-based methods in selecting features used to discover natural clusters, maximizing the between-cluster scatter and minimizing the within-cluster scatter toward a satisfactory clustering quality. © 2013 Elsevier B.V.

Huang K.Y.,Ling Tung University
Knowledge-Based Systems | Year: 2011

This paper introduces a new hybrid cluster validity method based on particle swarm optimization, for successfully solving one of the most popular clustering/classifying complex datasets problems. The proposed method for the solution of the clustering/classifying problem, designated as PSORS index method, combines a particle swarm optimization (PSO) algorithm, Rough Set (RS) theory and a modified form of the Huang index function. In contrast to the Huang index method which simply assigns a constant number of clusters to each attribute, this method could cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is investigated by comparing the classification results obtained for a real-world dataset with those obtained by pseudo-supervised classification BPNN, decision-tree and Huang index methods. There is good evidence to show that the proposed PSORS index method not only has a superior clustering accomplishment than the considered methods, but also achieves better classification accuracy. © 2010 Elsevier B.V. All rights reserved.

Lin K.-M.,Ling Tung University
Computers and Education | Year: 2011

This study explores the determinants of the e-learning continuance intention of users with different levels of e-learning experience and examines the moderating effects of e-learning experience on the relationships among the determinants. The research hypotheses are empirically validated using the responses received from a survey of 256 users. The results reveal that negative critical incidents and attitude are the main determinants of the users' intention to continue using the e-learning, irrespective of their level of e-learning experience. In addition, the findings show that the user's experience of the e-learning service plays a moderating role. The impact of negative critical incidents on perceived ease of use is greater for less experienced users. By contrast, the impact of negative critical incidents on perceived usefulness is greater for more experienced users. Perceived ease of use has a more critical effect on the attitude and continuance intention of less experienced users, whereas perceived usefulness is found to be a stronger determinant of the attitude and behavioral intention of more experienced users. Moreover, the relationship between satisfaction and continuance intention is stronger for less experienced users than for more experienced users. The implications of the present findings for research and managerial practice are analyzed and discussed. © 2010 Elsevier Ltd. All rights reserved.

An adaptive beamformer often suffers from severe performance degradation when a mismatch exists in the steering vector of interest. In this study, a new approach to the robust adaptive beamforming technique is proposed based on minimum variance distortionless response (MVDR) for space-time systems. The proposed method combines particle swarm optimization (PSO) and Taylor series expansion to enable the use of direction-of-arrival (DOA) estimation. Taylor series expansion can be robust to pointer errors for the presumed steering vector. However, the Taylor approach is easily trapped at a local minimum, causing errors in DOA estimation. Therefore, a more accurate DOA estimate is necessary for MVDR beamforming. To resolve this problem, a DOA estimation approach featuring a high resolution and low computational load is presented in this paper. An initial DOA estimate is first determined using a PSO estimator. Next, the predominant DOA estimate is sent to the Taylor series expansion-based estimator to form an estimate. Several simulation results are provided to demonstrate the effectiveness of the proposed approach. © 2014 Elsevier Inc. All rights reserved.

Huang C.-P.,Ling Tung University
International Journal of Systems Science | Year: 2013

This article primarily investigates the stability and controller design of fuzzy descriptor systems. The standard Takagi-Sugeno (T-S) fuzzy model is generalised into a descriptor T-S fuzzy model with the distinct derivative matrices in each rule, which can be used to represent a larger class of nonlinear systems. Based on a derived equivalent stability condition for the nominal descriptor system, the stability of unforced fuzzy descriptor systems with blending different derivative matrices can be treated. Furthermore, parallel distributed compensation (PDC) and a fuzzy proportional and derivative state feedback (PDSF) controller are proposed for stabilising the resulting closed-loop fuzzy descriptor systems. Significantly, all the presented criteria are formulated in terms of linear matrix inequalities (LMIs), so the stability analysis or a stabilising fuzzy controller can be readily achieved via current LMI solvers. Given numerical examples, we demonstrate the effectiveness and merit of the proposed approach. © 2013 Copyright Taylor and Francis Group, LLC.

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