• 한국산학기술학회논문지
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• 제19권3호
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• pp.75-82
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• 2018

최근 신흥개발국들은 한국형 경제개발 정책에 관심을 갖고 한국의 지역혁신 모델을 접목하고자 노력하고 있다. 한국도 신흥국의 수요에 맞추어 지역혁신 모델을 수출하고 해당국과 경제협력을 강화하는데 관심이 있다. 본 연구는 신흥국에 대한 한국형 지역혁신모델 전수사업을 분석하고 정책을 제시하고자 한다. 이를 위해 태국, 키르키즈공화국, 베트남, 멕시코 등 신흥 4개국의 혁신기관 실무자들이 공동학습 - 네트워크- 상호작용 활동에 참여하는 행태를 표적집단면접법에 의해 분석하였다. 한국의 혁신기관에서 제공하는 전수사업에서 현지국가 혁신주체의 초기 활동을 분석한 결과, 전수사업의 연수 시간과 이후 현지인들에 의한 자체 공동학습 참여율의 관계는 가장 밀접한 상관관계 (0.975)가 있음을 발견하였다. 그러나 이들의 자체 공동학습과 현지의 네트워크 참여율의 상관계수는 다소 낮아 (0.667), 현지에서 공동학습을 네트워크로 연결하는 정책이 필요하다. 네트워크 참여율과 혁신주체의 상호작용 참여율은 높은 상관관계 (0.950)로 나타나, 네트워크 구축이 지역혁신 모델의 관건이라는 것을 보여준다. 본 연구는 교육과 컨설팅 형태의 연수보다는 현지의 혁신 네트워크 활동을 촉진하는 사업을 추천한다. 본 연구는 충남테크노파크의 사례와 같이 혁신주체들이 스스로 학습 네트워크를 구축하여 지역에 맞는 혁신모델을 창출하고 지역혁신플랫폼을 직접 운영하는 단계까지 추진하는 전수사업 정책을 제안한다.

• Asia pacific journal of information systems
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• 제17권4호
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 춘계학술대회
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• pp.363-364
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• 2022

The COVID-19 pandemic had substantial negative impacts and caused several disruptions to the global supply chain of the shipping industry. The key challenges identified in terms of maritime manpower are the Certificates of Competency (CoC) or the expiration and/or failure to complete refresher and/or revalidation courses, which directly hinder employment retention and lost opportunities at sea. To tackle this issue directly and swiftly, the creation of the APEC SEN Distance Learning Platform was suggested and approved by APEC as part of an official project. This paper introduces the APEC-wide accessible distance learning platform with the following key topics: the organisation and operation of the platform, the themes and content to be prioritised, the process of education, training, certification, and the ways to promote accreditation, mutual recognition on CoC, education and training videos by taking collaborative actions, and the development of content.

• 간호행정학회지
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• 제22권5호
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• pp.518-530
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• 2016

Purpose: The purpose of this study was to investigate effect of the SBAR-Collaborative Communication Program on nurses'communication skills and on collaboration between nurses and doctors. Methods: From March 11 to November 11, 2013, data were collected from 180 hospital nurses working in a university hospital in Gyeonggi province. Outcomes were measured at three time intervals; before, three and six months after the program was completed. Results: After participating in this program, there was a significant increase in nurses'communication skills but not in collaboration between nurses and doctors. None of the participants' general categories influenced nurses'communication skills at pre-test, but age, education level, total years of working and work department significantly influenced scores at 3 and 6 months. Work department was the only category for which there was a significant difference in collaboration between nurses and doctors at pre-test, and education level and work department were related to significant improvement at 6 months. Conclusion: Findings indicate that this program can improve communication skills for nurses and also, collaboration between nurses and doctors, especially for nurses under 25 years of age. Thus nursing and hospital managers should provide SBAR-Collaborative Communication Programs to new nurses in their job training.

• 지능정보연구
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• 제18권2호
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• pp.131-141
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• 2012

논문에서는 협업필터링(collaborative filtering : CF) 기반한 추천시스템의 정확도를 높일 수 있는 방법을 제안하고 그 효과를 분석한다. 일반적인 CF기반 추천시스템에서는 시스템 세팅(참조집단 크기, 유의도 수준 등)을 한 가지 정해서 모든 경우에 대해서 동일하게 적용한다. 본 논문에서는 개별 사용자의 특성에 따라 이러한 세팅을 최적화 해서 개별적으로 적용하는 방법을 개발하였다. 이런 개인화된 세팅의 효과를 측정하기 위해서 Netflix의 자료를 사용해서 일반적인 추천시스템과 추천 정확도를 비교하였다. 분석 결과, 동일한 세팅을 적용하는 일반적인 추천시스템에 비해서 개인화된 세팅을 적용한 경우 정확도가 월등히 향상됨을 확인하였다. 이 결과의 시사점과 함께 미래 연구의 방향에 대해서도 논의한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권9호
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• pp.2589-2604
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• 2024

Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities.

• 한국산학기술학회논문지
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• 제12권12호
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• pp.5938-5945
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• 2011

공간환경을 매체로 한 교육은 학교 교사와 학생들이 생활환경에 담긴 의미와 가치를 이해하고 보다 창의적이고 책임감 있는 사회적 의식을 배양하는 데 기여한다. 공간환경의 중요성에도 불구하고 국내 초등학교의 공간환경에 대한 교육은 단순한 공간환경의 이해에만 그치고 있고, 공간환경에 대한 폭넓은 이해나 공간환경을 미디어로 한 교과목간의 협력형 수업이나 다양한 사회적 이슈와 결합된 입체적 교육은 아직 시도되고 있지 못한 실정이다. 또한 학교 밖 교육은 미술관이나 박물관 등의 체험활동을 통해 이루어지고 있으나 지역사회와 연계되거나 교육결과가 사회와 공유되는 교육의 소통은 미흡한 실정이다. 본 논문에서는 핀란드와 영국에서 학교와 학교 밖 기관이 연계하여 진행하는 공간환경 매체형 교육의 사례를 분석하여 공간환경을 통한 교육이 주는 의미와 가치를 파악하고자 한다. 결론적으로 국내외의 공간환경 관련 교육프로그램이 시사하는 바를 정리하여 현재 진행되는 국내의 학교교육 중심의 공간환경 교육의 문제점을 보완하고 활성화할 수 있도록 공간환경을 매체로 한 교육프로그램의 개선방안을 제안한다.

• Industrial Engineering and Management Systems
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• 제13권3호
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• pp.252-257
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• 2014

Global economic recession, global market reduction, and the deteriorating economic recession are forcing the industry to reconsider the business plan and strategy related to supply chain regardless of the size of the companies. During the economic recession caused by financial anxiety, development of international collaborative producer is the key theme for a manufacturing industry to get over the situation. Especially, development of high-skilled people who are capable of handling global manufacturing management is the key factor in China which shifted from a world factory to a world market. In this background, we started the international project of global manufacturing education and training for Chinese company managers from 2009. In this paper, based on the research conducted by the universities and companies in China, a design of global manufacturing education and training is proposed. This presented design is based on the theory of Japanese production strategy and investigation of actual conditions for Chinese companies and the research methodology of business management practice with efficiency and productive for international level management organization. The effectiveness of the education design is evaluated by applying the international education and training project of Aoyama Gakuin Human Innovation Consulting Inc. and Vigor Management Technology Association.

• 영어어문교육
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• 제17권2호
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• pp.219-245
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• 2011

This case study aims to investigate how a primary English teacher's professional development was pursued through novice teacher mentoring after the six-month intensive in-service teacher training program(IIETTP). The teacher was involved in mentoring two novice teachers working at the same school. They observed each other's classes and exchanged their views on the classes, focusing on areas to be improved. The observation was done within a framework that consisted of pre-, during- and post-observation sessions. Data was gathered through retrospective entries kept after the post-observation meetings. The entries were categorized according to their saliency, frequency and recurring patterns identified. The findings reveal that learning from the training course could be applied professionally and could serve to bridge the gap between training and teaching. It is also shown that the mentee teachers' professional development was enhanced and the mentor teacher herself benefited from the collaborative learning process involved with working with the novice teachers. Some suggestions are made for the effective implementation of school-based teacher development programs after the IIETTP.