• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.29-44
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• 2015

Digital Signage, a way of advertising or delivering information to viewers through digital displays, has expanded from being just an advertising channel in public places. Recently, it has become widely prevalent in restaurants and retail stores. Despite its wide expansion, digital signage is limited to specific usages and services and the devices it uses are also quite expensive. This study introduces a stick-type digital signage product that operates on Android OS, which addresses all the weaknesses of digital signage with much more reasonable pricing and stable operation. For stability, performance tests were executed on the hardware and applications. The results for hardware performance were extremely promising, as each scenario's maximum performance results, measured by Load Runner programs, reached target indexes. Also, as a result of the usability test, all participants, including non-digital signage system users (novices), were able to easily learn all the tasks. As a result of user satisfaction survey, positive responses were exhibited for ease of learning and usability (LEU), helpfulness and problem solving capabilities (HPSC), affective aspect and multimedia properties (AAMP), commands and minimal memory load (CMML), and control and efficiency (CE).

• Journal of IKEEE
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• v.19 no.2
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• pp.237-243
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• 2015

Recently, eight out of ten people have smartphone in Korea. Also, many applications of smartphone have increased. So, smartphone addiction has become a social issue. Especially, many people in smartphone addiction can't control themselves. Sometimes they don't realize that they are smartphone addiction. Many studies, mostly surveys, have been conducted to diagnose smartphone addiction, e.g. S-measure. In this paper, we suggest how to detect smartphone addiction based on ECG and Eye Gaze. We measure the signals of ECG from the Shimmer and the signals of Eye Gaze from the smart eye when the subjects see the emotional video. In addition, we extract features from the S-transform of ECG. Using Eye Gaze signals(pupil diameter, Gaze distance, Eye blinking), we extract 12 features. The classifier is trained using Random Forest. The classifiers detect the smartphone addiction using the ECG and Eye Gaze signals. We compared the detection results with S-measure results that surveyed before test. It showed 87.89% accuracy in ECG and 60.25% accuracy in Eye Gaze.

• Informatization Policy
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• v.21 no.2
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• pp.67-88
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• 2014

The research hypothesis of this paper is that education for smart device utilization has a positive effect on ability and utilization of smart devices for the disabled. The data for disabled of NIA's 2012 survey about digital divide is used, and the research method is "Heckman's 2-stage method" which considers the problem of selection bias. As a research result, this paper says that the disabled who had experience of education for smart device utilization have higher level of ability and utilization of smart devices. Also, a high level of education and family income, professional career and young generation have been very positive effect on the high level of ability and utilization of the disabled. The level of ability and utilization of the blind tend to be lower than those of the physically disabled. These results remind that the education for smart device utilization for the disabled is important and effective. And various contents and methods of education which are appropriate for the disability types should be developed.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.