• The Journal of Korean Association of Computer Education
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• v.19 no.1
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• pp.77-86
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• 2016

This paper is concerned with identifying the determinants of committing digital piracy in university settings. Using a theoretical model based on Theory of Reasoned Action, the causal relationship among the research variables, including intention to commit digital piracy, attitude, subjective norm and fear of legal consequences, is developed. Results from 295 survey responses indicate that fear of legal consequences affect the subjective norm and the attitude, which in turn influence the intention to commit digital piracy. In addition, relativism based on EPQ(Ethics Perception Questionnaire) is introduced as a moderating variable. The relative influences of path coefficients are investigated and implications from findings are also discussed.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.381-391
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• 2017

Biometric technology is a technology for authenticating a user using the physical or behavioral features of the inherent characteristics of the individual. With the necessity and efficiency of the technology in the fields of finance, security, access control, medical welfare, inspection, and entertainment, the service range has been expanding. Biometrics using biometric information such as fingerprints and faces have been exposed to counterfeit and disguised threats and become a social problem. Recent studies using a bio-signal from the inside of the body other than the bio-information of the external body are being developed. This paper analyzes the recent research and technology of biometric systems using bio-signals, ECG, heart sounds, EEG, and EMG to present the skills needed for the development direction. In the future, utilizing the deep learning to build and analyze database to manage bio-signal based big data for the complex condition of individuals, biometrics technologies suitable for real time environment are expected to be researched.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.518-520
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• 2021

In this paper, we propose a hand gesture recognition method by modifying the textNAS used for text classification so that it can be applied to multivariate time series data. It can be applied to various fields such as behavior recognition, emotion recognition, and hand gesture recognition through multivariate time series data classification. In addition, it automatically finds a deep learning model suitable for classification through training, thereby reducing the burden on users and obtaining high-performance class classification accuracy. By applying the proposed method to the DHG-14/28 and Shrec'17 datasets, which are hand gesture recognition datasets, it was possible to obtain higher class classification accuracy than the existing models. The classification accuracy was 98.72% and 98.16% for DHG-14/28, and 97.82% and 98.39% for Shrec'17 14 class/28 class.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.96-104
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• 2017

As new smart devices are evolved into the intelligent agent who can reflect user intention and use context, user experience design for easy and convenient usability becomes a core competitive edge. Under the assumption that human centered natural interaction is necessary for the optimal smart TV experience, this study explores the types of voice interaction which are peculiar to TV watching context. In order to build a model for the users to naturally interact with Smart TV, conversation patterns were collected by requesting key features of Smart TV to intelligent agent. Collected sentences were applied to CfA model and classified by responses to activate features. The classified conversation patterns were divided into feature activation and information search. This study has identified that CfC1 occurred when voice interaction between Smart TV and users was vague and CfC2 occurred when the requests were complex or conditional. In conclusion, Simple Request Type is the most efficient model and voice interaction is more appropriate to use to clarify users' vague requests.

• Information Systems Review
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• v.23 no.4
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• pp.165-182
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• 2021

In nowadays, Covid-19 has transformed patterns of consumers' behavior into a non-face-to-face mode. As the patterns of consumption have been digitalized, it has become a daily routine for consumers who perform so-called shadow work, which involves unpaid jobs that they have to do by themselves. In mobile grocery service context, consumers' shadow work could lead to shopping avoidance as well as switching toward other shopping channels. Thus, this study is to examine how consumers' perception of shadow work affect mobile shopping avoidance and switching intention toward other shopping channels. This study collected 283 survey data from online respondents who have experience on subscription services for ordering groceries in online. We also tested our research model by using partial least squares. Based on our results, this study has found that the perception of shadow work had a positive effect on mobile shopping avoidance as well as switching intention. We expect that our findings could contribute to relevant research on shadow work and suggest practical implications for digital platforms dealing with subscription business models

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.119-133
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• 2022

The COVID-19 pandemic is rapidly changing the behavior of members of society. Typically, the strong contagious power of the virus minimizes interaction between people in the real world, and they keep interaction activities to a minimum through online activities. Recently, as people demand online activities that enhance a sense of reality, the metaverse, which strengthens the 3D technology-centered sense of presence capability, is being chosen by people. The purpose of this study is to suggest a strategic direction for the establishment of the metaverse business model of startups by presenting factors for users' use and gratification of the metaverse. In detail, this study proposes the motivation for using the metaverse by reflecting the uses and gratification theory, and suggests a method to strengthen the motivation for the metaverse by reflecting the presences provided by the metaverse plotform and individual characteristics (normtive interpersonal influence). We surveyed people over 20 years of age who experienced metaverse and obtained 314 samples. In addition, we conducted the main effect analysis using the structural equation model and the moderating effect analysis using Process 3.1. As a result of hypothesis testing, we confirmed that metaverse presence (telepresence, social presence) has a positive effect on intention to continuous use by increasing metaverse's use and satisfaction factors (information, enjoyment, social interactivity). In addition, we found that individuals' normative interpersonal influence moderated the positive relationship between uses and gratification factors(enjoyment and social interactivity) intention to continuous use. Our study suggests strategies for establishing a user-centered business model for companies related to the metaverse.

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.115-125
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• 2007

This study analyzed the importance of user activity, communication between users and identity establishment by growth and change of player's character through users' communication structure and then it classified such an importance into activity by group, dealings, information sharing, duel and strategy performance. It also looked into the plans to revitalize smooth social relationship and suggested for users to behave with responsibility in virtual society. In the virtual space where thousands of people connect and interact with each other every day, users find out their identities through human relationship with others proceeding solo story. What is more important is that users consider virtual space as a society and feel collective responsibility on words and behavior against others.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.235-242
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• 2019

Recently, the use of artificial intelligence technology including deep learning has become active in various fields. In particular, several algorithms showing superior performance in object recognition and detection based on deep learning technology have been presented. In this paper, we propose the proper direction for the implementation of mobile healthcare application that user's convenience is effectively reflected. By effectively analyzing the current state of use satisfaction research for the existing fitness applications and the current status of mobile healthcare applications, we attempt to secure survival and superiority in the fitness application market, and, at the same time, to maintain and expand the existing user base.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.357-364
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• 2000

기존의 웹 디자인은 웹이라는 매체의 특성 상 디자인적인 요소가 매우 중요함에도 불구하고 디자인은 위한 구체적인 방법론이 미약하다. 특히, 많은 소비자들을 유인하고 구매를 촉발시켜야 하는 인터넷 쇼핑몰의 경우에는 더욱 더 그럼하에도 불구하고 이를 위한 전략적인 방법론이 부족하다. 즉, 기존 연구들은 제품의 다양성, 서비스, 촉진, 항해량, 편리성, 사용자 인터페이스 등이 중요하다고 하였지만 실제 인터넷 쇼핑몰을 디자인하는 입장에서는 활용하기가 상당히 애매하다. 그 이유는 이들 요인들은 서로 영향관계를 가지고 있어서 사용자 인터페이스가 복잡하면 항해량이 늘어나 편리성이 감소하고, 제품이 늘어나더라도 검색엔진을 사용하면 상대적으로 항해량이 감소하게 되어 편리성이 증가한다. 따라서, 이들 요인을 활용하여 인터넷 쇼핑몰을 구축하려면 요인간의 영향관계를 면밀히 파악하고 이 영향요인이 소비자의 구매행동에 어떠한 영향을 주는지가 충분히 검토되어야 한다.이에 본 연구에서는 퍼지인식도를 이용하여 인터넷 쇼핑몰 상에서 소비자의 구매행동에 영향을 주는 요인을 추출하고 이들 요인간의 인과관계를 도출하여 보다 구체적이고 전략적으로 인터넷 쇼핑몰을 디자인할 수 있는 방법으로 web-Cogmulator를 제시한다. Web-Cogmulator는 소비자의 쇼핑몰에 대한 암묵지식 형태의 구매행동을 형태지식화하여 지식베이스 형태로 가지고 있기 때문에 인터넷 쇼핑몰의 다양한 요인의 변화에 따른 소비자의 구매행동을 추론 시뮬레이션하는 것이 가능하다. 이에 본 연구에서는 기본적인 인터넷 쇼핑몰 시나리오를 바탕으로 추론 시뮬레이션을 실시하여 Web-Cogmulator의 유용성을 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computati

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.396-402
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• 2010

The tracking of current location of residents is an essential requirement for context-aware service of smart houses. This paper presents a wireless sensor network system which could detect location transition such as entrance and exit to a room and also identify the user who passed the room, without duty of wearing any sort of tag. We designed new sensor node to solve the problem of short operation lifetime of previous work[1] which has two pyroelectric infrared (PIR) sensors and an ultrasonic sensor, as well as a 2.4 GHz radio frequency wireless transceiver. The proposed user identification method is to discriminate a person based on his/her height by using an ultrasonic sensor. The detection idea of entering/exiting behavior is based on order of triggering of two PIR sensors. The topology of the developed wireless sensor network system is simple star structure in which each sensor node is connected to one sink node directly. We evaluated the proposed sensing system with a set of experiments for three subjects in a model house. The experimental result shows that the averaged recognition rate of user identification is 81.3% for three persons. and perfect entering/exiting behavior detection performance.