• 대한안전경영과학회지
• /
• 제12권3호
• /
• pp.271-281
• /
• 2010

In an industrial Era, OJT(On-the-Job Training) has been accepted as the field learning. But in a breaking up era, traditional field training needs to change and make an evolutionary model. Also, we need to make evolutionary model for various changing ways and means and need means to maximize the transformation of learning by operating learning organization. In knowledge based society, as people work and learn new knowledge in order to pass the experience knowledge and capabilities, they are not the traditional relationship between trainer and trainee but maximize work and learning, development and performance through several different ways. So, the study about new learning model is needed because the learning is creating the value and makes low cost and high efficiency about the elements of cost and time. We study the evolutionary model, OJF(On-the-Job Facilitating) - new learning methodology - through operating learning organization in S Electronics and its application practices.

• 한국콘텐츠학회논문지
• /
• 제14권4호
• /
• pp.513-521
• /
• 2014

본 연구의 목적은 통합교육의 이론적 배경을 토대로, 유비쿼터스 학습 환경에 적용이 가능한 에이전트 기반 모델링 활용 융합 교육 콘텐츠를 개발하고 적용하는 것이다. 이 콘텐츠의 구조는 탈학문적 통합 개념과 상황학습 이론을 토대로 설계하였으며, 3개의 모듈로 구성되어 있다. 3개의 모듈은 융합 문제 제시 모듈, 지식 리소스 모듈, 그리고 에이전트 기반 모델링과 IT 도구에 대한 학습 모듈이다. 구현한 콘텐츠의 만족도를 묻는 설문을 실시한 결과 5점 만점에 4.05(효과성), 4.13(편의성), 3.86(디자인)의 평균 값을 받았으며 각 평가 영역에 대하여 사용자들이 대체적으로 만족하고 있는 것을 확인할 수 있었다. 이 콘텐츠를 사용하여 학습자는 디바이스, 시간, 공간의 제한이 없이 IT 도구를 활용하여 융합 문제를 해결하는 과정에 대한 학습과 경험을 할 수 있으며, 이러한 구조의 콘텐츠 설계는 향후 융합형 교육 콘텐츠를 개발하려는 연구자에게 좋은 가이드라인이 될 것으로 판단한다.

• 가족자원경영과 정책
• /
• 제9권4호
• /
• pp.133-144
• /
• 2005

The purpose of this study is to propose a remodification of the family resource management curriculum in order to vitalize the entire healthy family specialist program. In January 2005, 'the Act of Healthy Families' was enacted. From then on, healthy family specialists not only have assumed a key role in health family Projects, which is based on the Act of Healthy Families itself, but they have also become key members of the healthy family support centers. Therefore, it can be said that cultivating competent healthy family specialists is vital to the success of the management of the healthy family support centers as well as the entire healthy family project. In order to enhance the quality of the healthy family specialists, we need to modify the current curriculum, which is based on primary courses that offers healthy family specialist licences in the end, into a curriculum that focuses on work-oriented learning and practical education. Especially, the curriculum of public family management should be administered in a way that strengthens the practical management of healthy family support centers. The basic curriculum as well as the guidelines of the practical training that is being conducted through healthy family support centers should also be organized in a way that enhances the professionality and the unification of the healthy family specialist.

• 디지털산업정보학회논문지
• /
• 제19권3호
• /
• pp.245-251
• /
• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권3호
• /
• pp.911-931
• /
• 2021

Recently, most cloud services use Docker container environment to provide their services. However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment. In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries. We compare the performances of the parameter server architecture and the All-reduce architecture, which are typical distributed deep learning architectures. Further, we analyze the performances of two separate multi-GPU resource allocation policies - allocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container. We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four. Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting. In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%. Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.

• International journal of advanced smart convergence
• /
• 제6권1호
• /
• pp.39-43
• /
• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• The Journal of Asian Finance, Economics and Business
• /
• 제8권1호
• /
• pp.257-269
• /
• 2021

Intellectual capital is becoming a crucial factor for a firm's long-term profit and performance in the knowledge-based economy as more firms identify their core competence as invisible assets rather than visible assets (Itami, 1987). The company was encouraged to measure financial and non-financial factors, including the customer perspective groups, the internal business process, learning and growth perspective, then to link all these measurements in a coherent system. This paper seeks to investigate the influence of intellectual capital elements on company performance, as well as the relationship among intellectual capital elements from a cause-effect perspective. Resource-Based View (RBV) considers intellectual capital as resource and capability to sustain competitive advantage on company performance. The partial least squares approach is used to examine listed banks in Indonesia Stock Exchange for year 2017-2019. Results show that human capital directly has positive influences on innovation capital, customer capital, and process capital. Innovation capital has positive, but less significant influence on process capital, which in turn influences customer capital. Human capital and process capital also influence customer capital. Finally, customer capital contributes to performance. This study helps management to identify relevant intellectual capital elements as competitive advantage and their indicators to enhance business performance.

• 한국CDE학회논문집
• /
• 제13권5호
• /
• pp.362-371
• /
• 2008

Generally, a PLM education program in university consists of lectures of theory, software lab and software development raining as an advanced subject. Most industries want more than these, such as practical problem solving capabilities, teamwork skills and engineering communications including human relationship, rhetoric, technical writing, presentation and etc. Problem-Based Learning is a problem-stimulated and student-centered teaming method, and an innovative education strategy for collaborative and self-directed learning by applying real world problems. Education paradigm changes from "teaching" to "learning" accomplished by team working, and students are encouraged to develop, present, explain and defense their ideas, suggestions or solutions of a problem, and the "cooperative teaming" proceeds spontaneously during team operations. Co-operative education program is an into-grated academic model and a structured educational program combining classroom learning with productive work experience in a field related to a student's academic or career goals. Based on the partnership between academic institutions and industries, students are engaged in real and productive "work" in the industry, in contrast with merely observing. In this paper, PBL with Co-op program is suggested as an effective approach for PLM education, and we made and operated a PBL-based education course with industry co-op program. The Co-op education in industry accompanied with the PBL course in university can improve practical problem solving capabilities of students, including modeling and management of P3R(Product, Process, resource and Plant) using commercial PLM software tools. By the result, we found this to be an effective strategy for helping students, professors and industries succeed in engineering education, especially PLM area.

• 한국수자원학회논문집
• /
• 제54권3호
• /
• pp.157-166
• /
• 2021

효율적인 댐 운영을 위해서는 높은 신뢰도를 기반으로 하는 유입량 예측이 요구된다. 본 연구에서는 최근 다양한 분야에서 사용되고 있는 데이터 기반의 예측 방법 중 하나인 딥러닝을 댐 유입량 예측에 활용하였다. 그 중 시계열 자료 예측에 높은 성능을 보이는 Sequence-to-Sequence 구조기반의 Long Short-Term Memory 딥러닝 모형(LSTM-s2s)을 이용하여 소양강 댐의 유입량을 예측하였다. 모형의 예측 성능을 평가하기 위해 상관계수, Nash-Sutcliffe 효율계수, 평균편차비율, 그리고 첨두값 오차를 이용하였다. 그 결과, LSTM-s2s 모형은 댐 유입량 예측에 대한 높은 정확도를 보였으며, 단일 유량 수문곡선 기반의 예측 성능에서도 높은 신뢰도를 보였다. 이를 통해 홍수기와 이수기에 수자원 관리를 위한 효율적인 댐 운영에 딥러닝 모형의 적용 가능성을 확인할 수 있었다.

• 벤처창업연구
• /
• 제13권4호
• /
• pp.99-116
• /
• 2018

본 연구의 목적은 기업의 성장단계별로 창업가가 갖춰야 할 핵심 역량요인이 반영된 통합 창업가 역량 모델을 제시하여 기술창업기업의 성공률을 제고하는 것이다. 연구의 목적을 달성하기 위해 본 연구는 초기 및 후기 기술창업기업의 핵심 창업가 역량과 기업성과 간의 관계, 그 관계를 강화하는 학습 역량의 조절효과를 실증분석 하였다. 연구의 결과는 다음과 같다. 첫째, 초기 기술창업기업 창업팀의 기술 기능적 역량, 창업가의 자원 활용 역량, 그리고 사업계획 수립 역량은 기업의 비재무적 및 기술적 성과를 모두 향상시키는 것으로 나타났다. 또한 기회 인식 역량은 비재무적 성과를 높이는 것으로 분석되었다. 이때 높은 수준의 학습 역량을 가진 창업가는 기술 기능적 역량과 자원 활용 역량 수준이 높아질수록 비재무적 성과를 더욱 향상시키며, 사업계획 수립 역량 수준이 높아질수록 기술적 성과를 더욱 향상시키는 것으로 확인되었다. 둘째, 후기 기술창업기업 창업가의 리더십과 자원 확보 역량은 기업의 비재무적 및 기술적 성과를 모두 향상시키는 것으로 나타났다. 또한 창업가의 전략적 역량은 비재무적 성과를 향상시키는 것으로 나타났다. 여기에서 학습 역량 수준이 높은 창업가는 전략적 역량 수준이 높아질수록 기업의 비재무적 성과를 더욱 향상시키는 것으로 분석되었다. 마지막으로 초기 및 후기 기술창업기업의 비재무적 성과와 기술적 성과는 모두 재무적 성과를 유의하게 향상시키는 것으로 나타났다.