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Songhyun LIM,Seongbae AN,Inbo HAN,Byung-Soo KIM 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.10
Intervertebral disc (IVD) degeneration (IVDD) is a common cause of chronic low back pain. As conventional treatments provide only symptomatic relief, there is a strong demand for more effective treatments of IVDD. One clinically relevant mechanism of IVDD pathology is disc cell senescence. Thus, removing senescent disc cells with senolytic drug could be an effective treatment for IVDD. Meanwhile, systemic administration of senolytic drug can cause systemic toxicity and repeated injections of drug into the IVD may increase morbidity. Therefore, a drug delivery system that releases senolytic drug locally for an extended period of time is prepared. This study shows that senolytic therapy with local and sustained drug delivery can inhibit IVDD and restore IVD integrity. ABT263, a senolytic drug, is loaded in poly(lactic-co-glycolic acid) nanoparticles (PLGA-ABT) and injected intradiscally into an injury-induced IVDD rat model. The strategy reduces expressions of pro-inflammatory cytokines and matrix proteases, inhibits progression of IVDD, and even restores the IVD tissues. The senolytic therapy with local and sustained drug delivery can be a promising treatments for IVDD.
임수연(SooYeon Lim),손기준(KiJun Son),박성배(SeongBae Park),이상조(SangJo Lee) 한국지능시스템학회 2005 한국지능시스템학회논문지 Vol.15 No.1
강화학습(Reinforcement-Learning)의 목적은 환경으로부터 주어지는 보상(reward)을 최대화하는 것이며, 강화학습 에이전트는 외부에 존재하는 환경과 시행착오를 통하여 상호작용하면서 학습한다. 대표적인 강화학습 알고리즘인 Q-Learning은 시간 변화에 따른 적합도의 차이를 학습에 이용하는 TD-Learning의 한 종류로서 상태공간의 모든 상태-행동 쌍에 대한 평가 값을 반복 경험하여 최적의 전략을 얻는 방법이다. 본 논문에서는 강화학습을 적용하기 위한 예를 n-Queen 문제로 정하고, 문제풀이 알고리즘으로 Q-Learning을 사용하였다. n-Queen 문제를 해결하는 기존의 방법들과 제안한 방법을 비교 실험한 결과, 강화학습을 이용한 방법이 목표에 도달하기 위한 상태전이의 수를 줄여줌으로써 최적 해에 수렴하는 속도가 더욱 빠름을 알 수 있었다. The purpose of reinforcement learning is to maximize rewards from environment, and reinforcement learning agents learn by interacting with external environment through trial and error. Q-Learning, a representative reinforcement learning algorithm, is a type of TD-learning that exploits difference in suitability according to the change of time in learning. The method obtains the optimal policy through repeated experience of evaluation of all state-action pairs in the state space. This study chose n-Queen problem as an example, to which we apply reinforcement learning, and used Q-Learning as a problem solving algorithm. This study compared the proposed method using reinforcement learning with existing methods for solving n-Queen problem and found that the proposed method improves the convergence rate to the optimal solution by reducing the number of state transitions to reach the goal.
한국형 창조경제 생태계 조성 방안에 대한 내용분석 연구
홍순구(Soongoo Hong),임성배(Seongbae Lim),김주남(Joonam Kim),이현미(Hyunmi Lee) 한국인터넷전자상거래학회 2015 인터넷전자상거래연구 Vol.15 No.3
The Korea government has been driving Government 3.0, a policy for building creative economy to overcome global economic crisis and strengthen national competitiveness. With content analysis, this study derived a new paradigm model which is designed to support the formation of a new eco-system for creative economy in Korea. Based on the derived paradigm, this study explored methods to form the ecological system for creative economy in two aspects. First, regarding methods to create the base for the eco-system for creative economy, formation of creative culture, innovation in the government, and nurturing creative human resources are identified as important factors. And, in terms of methods to create the eco-system for creative economy, creation of an eco-system where role of convergence of ICT (information, communications, and technology) are emphasized, creative cluster eco-system, an ecosystem where venture business foundation is active, and an eco-system raising globally competitive small companies are identified as critical factors. The academic contribution of this study is to expand qualitative research by analyzing literature data by using content analysis. The practical contribution of this study is that the results of this study can be utilized by police makers for better implementation of creative economy.
Soongoo Hong,Narang Kim,Seongbae Lim 한국인터넷전자상거래학회 2015 인터넷전자상거래연구 Vol.15 No.5
Many small and medium-sized manufacturing enterprises(SMMEs) have introduced and operated Production Information Systems(PISs). While previous studies evaluated performance immediately before and after system implementation, few investigated the operational performance of PIS, which focuses on the performance over a given period after system implementation. This study analyzes the differences between performance immediately after system implementation(establish and stabilization period) and the performance after a given period of time(operational phase). It also investigates factors that positively influence operational performance. The results showed that downtime decreased more in the operational phase than in the establishment and stabilization phases. In addition, the CEO’s support, user training, system improvement and maintenance, and the expertise and interest of the person in charge of a system were all identified as critical success factors influencing the operational performance of PISs.
송무희(Muhee Song),임수연(Sooyeon Lim),박승배(Seongbae Park),강동진(Dongjin Kang),이상조(Sangjo Lee) 한국정보과학회 2005 한국정보과학회 학술발표논문집 Vol.32 No.1
본 논문에서는 온톨로지의 개념구조를 이용한 웹페이지의 의미적 분류방법을 제안한다. 웹 문서들이 가지는 용어 정보들과 어휘들 간의 개념 구조를 파악하여 온톨로지를 확장시키면서 이를 문서분류에 적용하여 의미적 분류가 이루어지게 한다. 문서 분류는 문서들을 가장 잘 표현할 수 있는 자질들을 정하고 이러한 자질들을 통해 미리 정의된 2개 이상의 카테고리에 문서의 내용을 파악하여 가장 관련이 있는 카테고리로 할당하는 것이다. 본 논문에서는 웹 문서에서 추출한 용어 정보들의 유사도와 온톨로지 카테고리의 유사도를 계산하여 웹 문서를 분류하며, 문서 분류를 위한 실험데이터나 학습과정 없이 바로 실시간으로 문서분류가 이루어지며, 결과적으로 온톨로지와 문서들이 가지는 고유한 의미와 관계의 식별을 통하여 보다 더 정확하게 문서분류를 가능하게 해준다.