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스마트 금융서비스를 위한 사용자 중심의 핀테크(fintech) 어플리케이션 사례연구를 통한 핀테크 어플리케이션 UI 지향점 제안
이나현(Lee, Na-Hyeon),조준동(Cho, Jun-Dong),김태양(Kim, Tae-Yang) 한국디자인학회 2015 한국디자인학회 학술발표대회 논문집 Vol.2015 No.10
Consumers are now in more conveniant online shopping settings thanks to the generalization of computers and smart phones, so there comes new notion which is called pin-tech. finally, consumers can easily make a payment in mobile without going through complicate process. This paper analyzes Kakao-pay and Siren-order through focus group interview based on the theory of behavior affordance. By analyzing with FBI, there are mainly 2 things to concentrate on. First one is the natural flow of information. Users want their behaviors to naturally induced by affordance. So the streams of info should be designed in natural ways. Second one is issue concerning security. Infact, 75% which is apsolute majority of respondents worries the security when using the pin-tech application. So that kind of restrictions should be resolved. That is why this paper try to deal with those two insight through applications and suggest the further directions of research.
지방자치단체의 갈등대응방식에 관한 연구 - 화력발전소 유치갈등사례를 중심으로-
이나현(Na-Hyeon Lee),조은영(Eun-Yung Cho),김광구(Gwang-gu Kim) 한국지방행정학회 2017 한국지방행정학보 (KLAR) Vol.14 No.2
Many local governments have been facing stringent conditions such as rapid population decline, financial hardship, and economic instability, etc. Many of them have tried to deal with their urgent crisis by locating NIMBY facilities into their localities for population and economic growth. But many local governments have tended to face unexpected conflicts that then resulted to undermine local community and social trust. This paper analyzes the processes of many local governments that tried to locate thermal power plants in order to register their decisions into a national energy plan. During the processes, they have experienced serious conflicts between the mayors who wanted power plants for growth engine and residents who saw them as damage to local environments. This goal of this paper is to differentiate the reactions to the conflicts among local governments and to find a better approach both to find growth engine and to deal with conflicts in local governments. And this paper also pays attention to the leadership of local governments, especially mayors to deal with and resolve their public disputes. Many local governments and mayors did not recognized the effects of their unilateral decisions without deliberation and consultation with local residents about locating power plants. So, they faced anger and resistance from their residents. Finally, they failed the locations of power plants. However, a few of local governments and mayors tried to communicate the issues–including both positive and negative- related to locating power plants into their localities with their residents. They succeeded in locating power plants with support from residents. The implications from this paper is that the decisions for many local governments and mayors to stimulate their localities may tend to bring about unexpected conflicts and only result to undermine their already scarce resources. The decisions for the future of fragile localities need to be made with their residents by information-sharing, communication, and deliberations.
소량 데이터 딥러닝 기반 강판 표면 결함 검출 시스템 개발
게이뷸라예프 압둘라지즈,이나현,이기환,김태형,Gaybulayev, Abdulaziz,Lee, Na-Hyeon,Lee, Ki-Hwan,Kim, Tae-Hyong 대한임베디드공학회 2022 대한임베디드공학회논문지 Vol.17 No.3
Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.