Affective aspects of design attributes such as color, Pattern, and texture are important to the overall impression and the success of interior products. Among all the interior materials, wallpapers and flooring materials take up largest construction area and they are main components in creating affective impression for customers. This study aims to investigate the relationship between luxuriousness and related affective variables and design elements of wallpapers and flooring materials. The approach consists of 3 steps: (1) selecting related affective features and product design attributes through a literature survey, opinion of expert panel, and focus group interview, (2) conducting evaluation experiments, and (3) developing Kansei models using multivariate statistical analysis and analyzing critical attributes. Evaluation experiment was conducted using a questionnaire made up of 7-point scale and 100-point scale and 30 housewives and 20 interior designers participated in the evaluation experiment. The result of evaluation was analyzed through principal component regression and quantification I analysis. As a result of analyzing the survey data, the relationship between luxuriousness and related affective features and product design attributes was identified, moreover a optimal combination of the design component was identified. Consequently, it is expected that the results of the study would be a basis of the concept of emotion-based design by giving insights about how customers perceive the luxuriousness and suggesting the optimal combination, and providing specific quantitative design guidelines.

Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

This article suggests the methods to investigate adverse movement across global stock markets arising from insolvency of subprime mortgage in U.S. Our application deals with asymptotic tail dependence of daily stock index returns (KOSPI, DJIA, Shanghai Composite) of three countries; Korea, U.S., and China, over specific period via extreme value theory and copula functions. Daily stock index returns among three countries show higher extremal dependence during the period exposed to systematic shock. We confirm that extreme value theory and copula functions have potential to well describe the extreme dependence between three countries' daily stock index returns.

In this paper, we consider the M/G/1 queueing system with randomized control of T-policy. Whenever the busy period ends, the server is turned off and takes multiple vacations whose interval is fixed time T with probability p or stays on and waits for arriving customers with probability 1-p. We introduce the cost function and determine the optimal combination of (p, T) to minimize the average cost per unit time.

The purpose of this study is to propose types of joint venturesthat can increase the competitivenessof a company in the marketplace. We examine the characteristics of individual venture enterprises based on technology. We considered 16 TEA in order to categorize companies into four groups. Next, we used a multinomial logistic regression model to identify the significant characteristics of a venture company that successfully predicts group membership. Based on this information, we propose various forms of joint venture which complement each other and produce higher overall competence. Our study can provide important feedback information to academics, Policy-makers.

In the current RFID (Radio Frequency IDentification) middleware systems based on the EPCglobal standard, the tag data received by multiple readers are processed sequentially in FIFO (First In First Out) order. Considering the priority of RFID reader makes the RFID system more flexible and improve the data transaction throughput in the service environment where important tag data with high priority for a specific reader. In this study, we propose a new RFID middleware system architecture supporting priority service with the Buffer Management Component. Our proposals are compliant with the EPCglobal ALE (Application Level Events) standard interface for middleware systems and their clients. To verify the efficiency of this proposed system, simulation is used for evaluation.

A new location model, where the demand varies by periods, and the facility at each period can be open or closed depending on the demand, is discussed in this paper. General facility location problem is extended with the assumption that demands per period vary. A mixed integer programming is suggested and the solution is found for various instances which are randomly generated. Instances included various cases with respect to the length of periods, moving distance of customer locations, and cost structure. The characteristics of optimal solutions are analyzed for various cases, and it is shown that demand changing location model can be applied in a practical fields of supply chains.

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

This paper proposes an adaptive moving average (A-MA) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI A-MA chart is to adjust sampling intervals as well as to accumulate previous samples selectively in order to increase the sensitivity. The VSI A-MA chart employs a threshold limit to determine whether or not to increase sampling rate as well as to accumulate previous samples. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI A-MA chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The control length L is introduced to prevent small mean shifts from being undetected for a long period. A Markov chain model is employed to investigate the VSI A-MA sampling process. Formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI A-MA chart is proposed. Comparative studies show that the proposed VSI A-MA chart is uniformly superior to the adaptive Cumulative sum (CUSUM) chart and to the Exponentially Weighted Moving Average (EWMA) chart, and is comparable to the variable sampling size (VSS) VSI EWMA chart with respect to the ATS performance.

This paper focuses on the capacitated vehicle routing problem that determines the routes of vehicles in such a way that each customer must be visited exactly once by one vehicle starting and terminating at the depot while the vehicle capacity and the travel time constraints must be satisfied. The objective is to minimize the total traveling cost. Due to the complexity of the problem, we suggest a tabu search algorithm that combines the features of the existing search heuristics. In particular, our algorithm incorporates the neighborhood reduction method using the proximity information of the Voronoi diagram corresponding to each problem instance. To show the performance of the Voronoi tabu search algorithm suggested in this paper, computational experiments are done on the benchmark problems and the test results are reported.

A gait diagnosis supporting system is necessary to evaluate the characteristics of abnormal gait of a patient in a systematic and efficient manner. The present study developed a gait diagnosis supporting system which compares abnormal gait of a patient with a reference gait data and presents abnormal gait characteristics in an organized form. Three types of diagnosis modules were developed for the spatio-temporal, kinematic and kinetic gait parameters, and a gait data for Korean normal adults was used for the reference data of the system. The system was applied to evaluate the gait pattern of three arthritis patients and the abnormal gait characteristics of them could be easily identified with a systematic and graphical presentation.

In this paper, we deal with an optimal software-release problem of determining the time to stop testing and release the software system to the user. The optimal release time problem is considered from maintenance like the periodic distribution of service packs and the unpredictable distribution of patches after the release. Moreover, the environment of software error-detection during operation differs from the environment during testing. This paper proposes the software reliability growth model which incorporates periodic service packs, unpredictable patches and operational environment. Based on the proposed model, we derive optimal release time to minimize total cost composed of fixing an error, testing and maintenance. Using numerical examples, optimal release time is determined and illustrated.