A template-based baseball video scene classification using efficient playfield segmentation

In this paper, we present an effective and efficient framework for baseball video scene classification. The results of scene classification can be able to provide the ground for baseball video abstraction and high-level event extraction. In general, most conventional approaches are shot-based, which shot change detection and key-frame extraction are necessary prerequisite procedures. On the contrary, we propose a frame-based approach. In our scene classification framework, an efficient playfield segmentation technique is proposed, and then the reduced field maps are utilized as scene templates. Because the shot change detection and the key-frame extraction are not required in proposed method, the new framework is very simple and efficient. The experimental results have demonstrated that the effectiveness of our proposed framework for baseball videos scene classification, and it can be easily extended the template-based approach to other kinds of sports videos.     

Simulation optimization using particle swarm optimization algorithm with application to assembly line design

Assembly line design is an important part of production system. Some processes need to undergo changes in order to increase in efficiency. Computer simulation has been applied on process design for many decades. Traditionally, simulation had to run all possible alternatives of assembly line and was not considered as an optimization technique. Thus, this study employs particle swarm optimization (PSO) algorithm which is with mutation based on similarity for simulation optimization in order to optimize the managerial parameters in production system. Through experimentation designs and statistics tests, the simulation results show that the proposed method is better than other algorithms, like genetic algorithm and conventional PSO algorithm for solving assembly line design problem.     

An econometric analysis of SARS and Avian Flu on international tourist arrivals to Asia

This paper compares the impacts of SARS and human deaths arising from Avian Flu on international tourist arrivals to Asia. The effects of SARS and human deaths from Avian Flu are compared directly according to the number of human deaths. The nature of the short run and long run relationship is examined empirically by estimating a static line fixed effect model and a difference transformation dynamic model, respectively. Empirical results from the static fixed effect and difference transformation dynamic models are consistent, and indicate that both the short run and long run SARS effect have a more significant impact on international tourist arrivals than does Avian Flu. In addition, the effects of deaths arising from both SARS and Avian Flu suggest that SARS is more important to international tourist arrivals than is Avian Flu. Thus, while Avian Flu is here to stay, its effect is currently not as significant as that of SARS.     

Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly.     

Modeling analysis of a triaxial microaccelerometer with piezoelectric thin-film sensing using energy method

This study applies energy method to derive the system modeling of a triaxial microaccelerometer that consists of a quadri-beam suspension, a seismic mass, and displacement transducers using piezoelectric thin films. Two suspension beams support both ends of the seismic mass, which is fabricated by anisotropic etching of silicon. An out-of-plane acceleration will result in a symmetric bend, and in-plane accelerations will produce asymmetric bend and torsion of the suspension beams. Two piezoelectric thin-film transducers are arranged at both ends of each suspension beam. Eight transducers in total are interconnected such that triaxial accelerations can be measured selectively. The structure stiffness of the suspension beams considers both the silicon beams and piezoelectric films by the use of the laminated beam theory. Therefore, the analytical model is applicable to the accelerometers with thick piezoelectric films. The model is based on the anisotropic material properties of Silicon and PZT and Euler’s beam equation with the assumptions that smaller strains and stresses are negligible. The analytical results of the resonant frequencies and sensor sensitivities to triaxial accelerations are presented and confirmed by finite element analysis.     

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.     

An expert system to classify microarray gene expression data using gene selection by decision tree

Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable.     

Automated thermal fuse inspection using machine vision and artificial neural networks

Machine vision is an excellent tool for inspecting a variety of items such as textiles, fruit, printed circuit boards, electrical components, labels, integrated circuits, machine tools, etc. This paper presents an intelligent system that incorporates machine vision with artificial intelligent networks to automatically inspect thermal fuses. An effective inspection flow is proposed to detect four commonly seen defects, including black-dot, small-head, bur, and flake during the production of thermal fuses. Backpropagation neural networks and learning vector quantization performance is compared in detecting the bur defect because of its illegibility. Different numbers of defective samples were screened out from a production line in a case study company and used to demonstrate the efficacy of the proposed system. Currently, the proposed inspection system is operating at the case study company, replacing four to six human inspectors. The system not only ensures the quality of the thermal fuses produced, but also reduced the cost of manual visual inspection.     

A simple algorithm for checking compatibility among discrete conditional distributions

A distribution is said to be conditionally specified when only its conditional distributions are known or available. The very first issue is always compatibility: does there exist a joint distribution capable of reproducing all of the conditional distributions? We review five methods-mostly for two or three variables-published since 2002, and we conclude that these methods are either mathematically too involved and/or are too difficult (and in many cases impossible) to generalize to a high dimension. The purpose of this paper is to propose a general algorithm that can efficiently verify compatibility in a straightforward fashion. Our method is intuitively simple and general enough to deal with any full-conditional specifications. Furthermore, we illustrate the phenomenon that two theoretically equivalent conditional models can be different in terms of compatibilities, or can result in different joint distributions. The implications of this phenomenon are also discussed.