• Journal of Gifted/Talented Education
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• v.23 no.5
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• pp.771-791
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• 2013

What are the identification criteria elementary school teachers prefer? What are the characteristics of students that teachers consider when nominating them to gifted programs? Will those criteria of identification/nomination differ as to teacher experiences related to gifted education or teacher involvement in the professional development? This study aims to find the answer to these questions. For this purpose, a total of 511 elementary school teachers with a varying degree of experiences with gifted education were recruited from 23 schools in 11 school districts in Seoul. The results show that teachers generally preferred task commitment, creativity, curiosity, and domain specific talents as criteria for identifying gifted students, while perceiving achievement records, total grades, leadership, and general intelligence as less important. Teachers experienced in gifted education or having been involved in professional development perceived curiosity, task commitment, and creativity as more important than teachers without such experiences. The importance-performance analysis of identification criteria indicates that teachers reported high importance on task commitment, curiosity, and creativity, but those factors were less considered in actual nomination. On the contrary, teachers reported low importance on quick learning and achievement(total grades, subject grades), but those were highly considered in nomination. A similar pattern was found in both experienced and nonexperienced teachers although the importance-performance gap was higher for the latter. Implications for teacher nominations and professional development were discussed.

• 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.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.306-318
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• 2021

The dispatch law has a negative impact on safety management at construction sites as the command and command relationship to safety assistants of the original contractor are applied to the dispatch law. Purpose: The purpose is to study the importance and impact of safety management according to the dispatch law, and to propose a direction for safety management so that safety assistants can actively and proactively prevent accidents. In this study, we used AHP analysis techniques for experts to achieve the final goal and verified the suitability through logistic regression. Method: AHP analysis technique is used for experts and workers and logistic regression analysis is conducted. Result: The result of analyzing scenario data where the dispatch method can be applied showed the importance in the order of education (SkillUp education), management (work-time management) and direct instructions (feedback instruction). In logistic regression analysis, feedback is the factor that affects direct instruction, and in education management, the ratio of education management is 3.42 times lower than that of other groups when only the team leader of the company gives work instructions. Conclusion: The management of feedback and education is more important than anything else within the range in which the dispatch method is not applied, and the expansion of non-face-to-face online education is judged to avoid the violation of dispatch method because the expansion of non-face-to-face online education due to covid 19 recently has brought more various target for safety education.

• Journal of the HCI Society of Korea
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• v.9 no.2
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• pp.5-12
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• 2014

There was widespread criticism of the online news services due to their bias toward sensational and soft news. Thus, news services based on journalist values are socially requested. News source network analysis(NSNA), an algorithm to cluster and weight news sources, quotes, and articles, is suggested as a method to emphasize on journalist values like facts, variety, depth, and criticism in the previous study. This study suggests 'News Sources' as a visualization tool of NSNA. 'News Sources' shows news as bar graphs, weighted by facts and criticism, and arranged by organizations and subjects. This study designed a beta version using KINDS, a news archive of Korean Press Foundation.

• Journal of the Korean association of regional geographers
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• v.13 no.4
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• pp.381-395
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• 2007

After the democratization process since 1988, the national scale voting behavior in congressional elections has changed from a rural-government party and urban-opposite party connection to a political regionalism oriented pattern. In this context, the case study with provincial border regions aims to investigate possible party identification change of the region, and to find a relationship between polling score ratio and socio-political characteristics of the candidates. As a result, Yeongdong shows a strong negation to the presumed Chungcheong local party and shows a continuous party identification with the Kyungsang local party. Muju reveals a more or less weakened identification with the Jeolla local party, on the contrary, Kimcheon shows a unchanged strong identification with the Kyungsang local party. The regional neighborhood effect was verified quite partly between the subdivision districts of the border regions. With a application of linear fitting method, it is certified that voters have attached great importance to the belonging party, native place, as well as political career of the candidates as a voting criterion.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1030-1036
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• 2006

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the method that assigns different weighted values to feature attributes depending on importance.

• Journal of Korean Medical classics
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• v.22 no.2
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• pp.23-34
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• 2009

형상(形相) 망진(望診)에 대한 국제공동연구와 해외진출을 위한 준비과정으로 중의사(中醫師)를 대상으로 수요조사를 실행하였다. 참여자의 학문적인 경향성은 전통 한의학적인 보수 경향성보다는 현대의학을 실용적으로 활용하는 태도를 보였으며, 임상에서 활용도가 높은 병증체계는 장부병증과 팔강병증의 순이었다. 한방 진단법에서 중요하게 활용하는 방법은 문진(問診)이며, 망진(望診), 문진(聞診), 맥진(脈診), 복진(腹診) 등의 방법도 고른 분포도를 보였으며, 그 이유는 진단 효율성, 환자와의 상담, 치료효과 입증, 환자 정보 공유, 진단 결과의 재현성과 진단의 표준화 객관성 등으로 고르게 나타났다. 한약과 침구의 활용에 대한 진단기법의 일관성은 비교적 동일하거나 보통으로 나타났으며, 그 이유로는 한약과 침구의 변증행위가 동일한 체계를 활용하거나, 소속 학파의 이론을 한약과 침구에 활용하는 것으로 보인다. 망진 형상진단의 중요도와 활용도는 고르게 나타났으며, 망진에서 중요하게 활용하는 부위와 내용으로는 두면의 생김새, 신체 전반적 생김새, 신체 특징부위로 나타났다. 형상진단의 기전과 표준화 연구에 가장 적합한 연구방법론으로는 형상진단에 입각한 고전 문헌연구, 전문가의 형상분석에 대한 통계처리, 병증과 형상에 대한 임상데이터 구축 등이었다. 형상진단기에 대해 요구하는 기능은 형상유형감별, 오장육부 상태 진단, 표리한열 진단, 경락기운 진단 등으로 나타났으며, 형상진단기의 임상적인 활용도를 높일 수 있는 임상분야는 심혈관 질환, 뇌혈관 질환, 소화기 질환, 대사증후군 질환, 부인과 질환, 노인성 질환 등으로 고른 분포를 보였다.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.1-7
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• 2011

The methodology of KOSPI forecast has been considered as one of the most difficult problem to develop accurately since short-term KOSPI is correlated with various factors including politics and economics. In this paper, we presents a methodology for forecasting short-term trends of stock price for five days using the feature selection method based on a neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. A technical indicator are selected for preprocessing KOSPI data in the first step. In the second step, thirty-nine numbers of input features are produced by wavelet transforms. Twelve numbers of input features are selected as the minimized numbers of input features from thirty-nine numbers of input features using the non-overlap area distribution measurement method. The proposed method shows that sensitivity, specificity, and accuracy rates are 72.79%, 74.76%, and 73.84%, respectively.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.291-302
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• 2020

Korean popular music industry, which is lead by "Idol group", has forsaken their fandom all over the world. Therefore, idol groups has become not only an artist but also the most influential people in the Korean economy. A global idol group with a strong fandom can earn more than a trillion-dollar by attracting their global fan's interest in Korea. In other words, it is considerably important to carry the idol to a successful conclusion. This study tries to expect whether the idols can be survived or not at a certain point after their debut by ANN, Decision Tree, Random Forest. We decide that certain point as the three-year and eight-year after their debut, because it is their break-even point year and the year after their average renewal of the contract. In addition, this study also explains which feature is the most important to their survival by feature importance and Logistic regression. In conclusion, features like the number of idol competitors, the number of debut members and the number of the genre are significant. These results shed light on the efficient management of K-Pop idol to improve industrial competitiveness.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.27-34
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• 2010

This paper presents a methodology for classifying left and right motor imagery using a neural network with weighted fuzzy membership functions (NEWFM) and wavelet-based feature extraction. Wavelet coefficients are extracted from electroencephalogram(EEG) signal by wavelet transforms in the first step. In the second step, sixty numbers of initial features are extracted from wavelet coefficients by the frequency distribution and the amount of variability in frequency distribution. The distributed non-overlap area measurement method selects the minimized number of features by removing the worst input features one by one, and then minimized six numbers of features are selected with the highest performance result. The proposed methodology shows that accuracy rate is 86.43% with six numbers of features.