최용석 교수

Knowledge management (KM) is a formalized, integrated approach to identifying and managing an organization's knowledge assets. The impact of KM implementation in terms of performance improvement and related benefits are still elusive. This ambiguity comes largely from the absence of empirically-based assessment of KM implementation. The main purpose of this study is to develop a better understanding of the critical factors affecting the successful implementation of knowledge management. To achie

As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still hav

Recently, several studies have focused on image-to-image translation. However, the quality of the translation results is lacking in certain respects. We propose a new image-to-image translation method to minimize such shortcomings using an auto-encoder and an auto-decoder. This method includes pre-training two auto-encoders and decoder pairs for each source and target image domain, cross-connecting two pairs and adding a feature mapping layer. Our method is quite simple and straightforward to ad

In this paper, we propose a multi-agent learning approach to information retrieval on the World Wide Web where each agent collaboratively learns its environment from user's relevance feedback using a neural network mechanism.Our approach makes it possible to discover information sources that will give the desired information, and retrieve that information efficiently and effectively.First, we present a framework for our multi-agent learning approach and introduce a training procedure for capturi

Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as spa

Classifying semantic relations between entity pairs in sentences is an important task in natural language processing (NLP). Most previous models applied to relation classification rely on high-level lexical and syntactic features obtained by NLP tools such as WordNet, the dependency parser, part-of-speech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information related to the entity, which may be

석조문화재의 보존․복원 작업에 적용할 수 있는 에폭시 수지의 성능을 개선하기 위해 다양한 성분을 조합하여 경화 거동을 제어할 수 있는 접착 시스템을 연구하였다. 사용된 에폭시 수지 주제는 hydrogenated Bisphenol-A (HBA), 경화제로 속경화형 경화제 FH와 저속 경화제 SH를 구성하였으며, 반응성 희석제는 difunctional polyglycidyl epoxide (DPE), 무기 첨가물은 탈크를 사용하였다. 에폭시 수지와 경화제의 혼합 조건에 따라 경화 시 점도, 온도 및 differental scanning calorimetry (DSC)를 이용하여 경화 동력학을 측정하였고, 기계적 특성을 확인하기 위해 무기 첨가물의 함량에 따른 굴곡강도, 인장강도, 압축전단접착강도를 측정하였다. 연구 결과, 다양한 석조문화재의 보존처리 작업에 맞춰 에폭시 수지 구성물의 성분 조합을 통해 경화 특성을 제어하고, 무기 첨가물의 도입을 통해 기계적 특성을 조절하여 성능 개선 및

석조문화재에 사용되는 에폭시 수지에 열팽창계수가 낮은 무기물을 첨가하여 , 석재와 비슷한 수준으로의 열팽창계수 조절 및 무기물 첨가에 따른 기계적 변화를 연구하였다 . 사용된 에폭시 주제는 hydrogenated bisphenol A (HBA), 경화제로는 상온에서 경화가 가능한 Isophoronediamine (IPDA), 점도 조절을 위한 반응성 희석제로는 difunctional polyglycidyl epoxide (DPE)를 선정하였으며 열팽창계수를 조절하기 위한 무기물 첨가제는 talc와 fused silica를 사용하였다. 에폭시 수지와 희석제의 함량에 따라 점도를, 에폭시 수지와 무기물 첨가제의 부피비에 따른 열팽창계수를 측정하여 물리·화학적 물성 개선 가능성을 확인하였으며 인장강도, 전단강도 측정을 통하여 기계적 성능을 비교하였다. 연구결과 에폭시 수지에 첨가될 무기물 첨가제의 함량이 커질수록 열팽창 계수가 감소할 뿐만 아니라 , 굴곡강도가 감소하고 전단 강도는 증가하

Video captioning via encoder-decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a standard method for improving model performance. Such feature extraction networks are weight-freezing states and are based on convolution neural networks (CNNs). However, these traditional feature extraction methods have some problems. First, when