• 한국IT서비스학회지
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• 제12권3호
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• pp.23-37
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• 2013

Social commerce is a subset of e-commerce that involves using social media, and user contributions to assist in the online buying and selling of products and services. Given the rapid growth of social commerce sites such as Groupon, Ticketmonster, and Coupang, it has become critical to understand customer purchasing decision-making processes in the social commerce environment. This study developed a theoretical model to examine the role of social commerce site's stickiness in customers' repurchasing decision processes. This study identifies price attribute, variety of selection, shopping enjoyment, and anger as the key factors of social commerce site's stickiness. Data collected from 164 users who had more purchasing experiences with social commerce for more than 7 months were empirically tested against the research model. The analysis results indicate that social commerce site's stickiness plays an important role in enhancing customer's purchasing behavior. Moreover, price attribute and shopping enjoyment significantly influence social commerce site's stickiness, whereas anger does not significantly affect consumer purchasing decision-making processes. However, contrary to our expectation, variety of selection negatively influences social commerce site's stickiness. The theoretical and practical implications of the findings are described.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.

• 한국의류학회지
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• 제21권7호
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• pp.1205-1214
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• 1997

The purpose of this study is to positively investigate if the theory of self-monitoring among various individual trait theories would be a theoretical concept which can explain about the differences of clothing behavior under given social situations among Female university students in Korea. For this purpose, the following research problem were set up; 1. Self-monitoring levels and changing differences of self-image as per situation would be reviewed. 2. Self-monitoring levels and changing differences of clothing selection factors as per situation would be reviewed. The results of this study can be summarized as follows; First, as a result of analyzing the differences of situational self-image pursuits within per situation depending on individuals self-monitoring levels, the differences were found significant by. Namely, the adjectives for situational self-image which corresponded to those who had high self-monitoring than low self.monitoring were "womanly", "refined", "sensual", "lively" and "elegant". Second, as a result of analyzing the differences of priority of clothing selection factors within per situation depending on individuals self-monitoring levels, the differences were found significant by. Those who had high self-monitoring level put a higher priority on fashionability, aesthetics and status.symbol of clothing within per situation, while those who had low self-monitoring thought important for economy or utility within per situation.rtant for economy or utility within per situation.

• Interdisciplinary Bio Central
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• 제3권4호
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• pp.13.1-13.8
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• 2011

Previous research has established the existence of individual differences with regards to individuals' optimum time of well-functioning; specifically in terms of being either morning or evening oriented. An association has also emerged between being more evening, as opposed to morning, oriented and having a greater number of sexual partners. The aim of the present study was to investigate whether "eveningness" in males is an evolved sexually dimorphic trait consistent across different cultures. A sample of 179 male Sri Lankan men residing in two different cultural and economic settings, Italy and Sri Lanka, were administered the Morningness-Eveningness Questionnaire (MEQ) followed by assessing their sexual behavior history. The results robustly portrayed a highly significant main effect of MEQ types highlighting the twofold sexual success enjoyed by the evening individuals in both regional locations. Morning oriented individuals, showed a stronger preference for going out and partying than evening-types, suggesting that the higher mating success of evening types is not due to their different lifestyles allowing more opportunities to encounter females. However, evening types exhibited a preference for flirtatious behaviors in the later part of the day. Shoulder-to-hip and handgrip strength, as measures of testosterone levels, were not significantly associated with eveningness. The results are discussed in terms of sexual selection and its interplay with human cultural variation.

• 한국지역사회생활과학회지
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• 제16권4호
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• pp.59-69
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• 2005

This study examined the shop selection standards and preferred shops depending on the clothing attitudes identified by the psychological characteristics of consumers. To this end, this study selected the Korean Chinese college women in Yanbian. The study was conducted against 300 college students from May to June, 2002. Questionnaire was used for studying the subject of the thesis. Each question was rate4 in 5 point scale, where 1 means 'not at all' and 5 means 'definitely'. The data of this study was statistically analyzed using the SAS PC program. The t-test and $X^{2}$ were conducted to identify the evaluation criteria for clothing store and the preferred shops depending on clothing attitude groups and the factor analysis was carried out to analyze the clothing behavior factors. The results of study are summarized as described below. The clothing attitude of college women was classified into four factors: fashionable, brand-oriented, aesthetic and modest. The subjects were divided into two groups with higher average score and that with lower average score by factor, respectively. As a result of study on the evaluation standards of shop selection and preferred shops depending on the clothing attitude, for the evaluation standards of shop selection, three factors, fashionable, brand-oriented and modest factors, showed the significant difference between two groups. There was a significant difference between two groups in fashionable and brand-oriented factor and the preferred shops.

• Transactions on Electrical and Electronic Materials
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• 제13권3호
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• pp.139-143
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• 2012

Optical emission spectroscopy (OES) is often used for real-time analysis of the plasma processes. OES has been suggested as a primary plasma process monitoring tool. It has the advantage of non-invasive in-situ monitoring capability but selecting the proper wavelengths for the analysis of OES data generally relies on empirically established methods. In this paper, we propose a practical method for the selection of OES wavelength peaks for the analysis of plasma etch process and this is done by investigating reactants and by-product gas species that reside in the plasma etch chamber. Wavelength selection criteria are based on the standard deviation and correlation coefficients. Moreover, chemical actinometry is employed for the normalization of the selected wavelengths. We also present the importance of chemical actinometry of OES data for quantitative analysis of plasma. Then, the suggested OES peak selection method is employed.. This method is used to find out the reason behind abnormal etching of PR erosion during a series of $SiO_2$ etch processes using the same recipe. From the experimental verification, we convinced that OES is not only capable for real-time detection of abnormal plasma process but it is also useful for the analysis of suspicious plasma behavior.

• 한국의류학회지
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• 제33권5호
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• pp.764-774
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• 2009

The purpose of this study is to investigate the differences in consumer clothing shopping orientation and shopping behaviors by the multi-selection of internet and offline stores. The data were collected from 201 men and women in their twenties and the respondents were grouped into three as internet-store users, multi-store users, and offline-store users. The data were analyzed using factor analysis, ANOVA, post-hoc analysis, frequency analysis, and chi-square analysis. The results are as following. First, the clothing shopping orientation was partly different among the groups. Regarding the offline shopping orientation, the groups showed difference in the impulsive orientation, and regarding the online shopping orientation, the groups showed differences in the goal oriented and enjoying orientation. In all the three cases, the internet users showed strongest orientation, and the next were multi-store users and offline-store users. The cause of these results were explained as the familiarity and experience with the channel. Second, the clothing shopping behaviors were also partly different among the groups. The groups showed no differences in the preferred store type and benefits sought, but showed significant difference in the attitude toward the internet shopping. The internet-store users showed most positive attitude, and the next were multi-store users and offline-store users.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1168-1182
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• 2017

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5425-5448
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• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.