• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.267-268
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• 2012

본 논문에서는 Google사의 Project glass를 이용한 피아노 연습 어플리케이션을 제안한다. 하지만 아직 이 기기는 발매되지 않았기 때문에 안드로이드 모바일에서 제작중이다. 사용자악보를 database로 간단하게 관리하고, OpenCV라이브러리를 통해 실제 피아노의 위치와 건반을 인식하고, 손가락의 위치 파악과 소리 인식을 통하여 서로 인식한 정보의 일치여부를 확인한다. 그리고 증강현실 기술을 이용하여 게임적인 요소를 추가시켜서 보다 쉽고, 재미있게 실제 피아노 연습을 할 수 있다. 본 논문에서는 기존에 피아노 연주 연습하는 방식에서 IT기술을 접목시켜서, 교육과 기술발전에 기여할 수 있음을 보인다. 그리고 앞으로 영상처리 기술이 널리 사용될 것으로 예상되어 미리 기술을 학습하는 효과도 있다.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.