손미애 교수

Providing context-aware personalized service in smart home environment is very important to improve the quality of the disabled life. To reaching the goal, it needs to challenge due to diversity of needs: type of disability, degree of disability, preferences, tasks, and context. We propose a framework to recommend context-aware personalized services for the disabled in smart home environment called SOLVE-D. Main module of SOLVE-D is ontology repository that is consisted of three ontologies: gene

Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the

Dynamic adaptation is to create proper information from the context of the end user exposed to a ubiquitous computing environment. Thereby, we propose a Context Ontology-based personalized recommendation in the ubiquitous computing Environment (CORALE) framework to process dynamic adaptation considering social context as a kind of tacit knowledge. The CORALE uses two types of reasoning for the dynamic adaptation, (i) we create a case including an end user's context and decision-making result and

In this paper, we propose a context-based hybrid semantic matching framework for e-mentoring system. The framework adopts ontology technologies to define the attributes of mentors and mentees, the matching results, and satisfaction values of the mentoring, and to recommend adequate mentors via semantic reasoning. In the developed ontology named mentoring-matching ontology (M2O), the pairs of mentor-mentee and the satisfaction values of mentoring are managed as cases. To increase the satisfaction

RFID(Radio Frequency IDentification)가 국방, 물류/유통, 도서 관리 등의 분야에서 바코드를 대체하는 대안으로 각광을 받고 있으나, 적용 범위가 제품 생산이나 판매유통 관리 등의 분야로 제한되어 있는 것이 사실이다. 본 논문에서는 RFID 기술을 공정관리에 적용해 본 후, 그 효과를 분석하였다. 또한 유통단계뿐 아니라 제품의 이력관리 및 품질관리에 RFID의 적용 가능성을 타진하기 위해, 제조 및 포장공정에 활용하는 방안을 제안하였다. RFID 적용 효과를 검증하기 위해 실제 제조업체를 대상으로 현재 바코드를 부착하는 공정을 RFID로 변경하였을 때 공정 개선 효과를 산출하였으며, 연구 결과 RFID 도입에 소모되는 초기 투자 비용을 상쇄할 수 있는 비용절감 효과가 있음을 확인하였다. 이로써 높은 초기 비용으로 인해 바코드에서 RFID로의 교체를 망설이는 제조업체들의 의사결정에 기여할 수 있을 것으로 기대된다.

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflec