• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권6호
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• pp.177-186
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• 2020

최근 딥러닝(Deep Learning) 기술의 발전에 따라 컴퓨터 비전(Computer Vision) 분야의 이미지 인식 성능이 향상되고 있으며, 또한 Serverless Computing이 이벤트 기반의 클라우드 애플리케이션 개발 및 서비스를 위한 차세대 클라우드 컴퓨팅 기술로 각광받고 있어 딥러닝과 Serverless Computing 기술을 접목하여 실생활에 이미지 인식 서비스를 사용하고자 하는 시도가 증가하고 있다. 따라서 본 논문에서는 Serverless Computing 기술을 활용하여 효율적인 딥러닝 기반 이미지 인식 서비스 시스템 개발 방법을 기술한다. 제안하는 시스템은 Serverless Computing 기반 AWS Lambda Server를 이용하여 적은 비용으로 대형 신경망 모델을 사용자에게 서비스할 수 있는 방법을 제안한다. 또한 AWS Lambda Server의 단점인 Cold Start Time 문제와 용량제한 문제를 해결하여 효과적으로 대형 신경망 모델을 사용하는 Serverless Computing 시스템을 구축할 수 있음을 보인다. 실험을 통해 AWS Lambda Serverless Computing 기술을 활용하여 본 논문에서 제안한 시스템이 비용 절감뿐만 아니라 처리 시간 및 용량제한 문제를 해결하여 대형 신경망 모델을 서비스하기에 효율적인 성능을 보임을 확인하였다.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.110-117
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• 2021

The purpose of the study is to develop effective teaching methods to strengthen the major learning capabilities of electronic engineering learners through inquiry learning using computing thinking ability. To this end, first, in the electronic engineering curriculum, we performed teaching-learning through an inquiry and learning model related to mathematics, probability, and statistics under the theme of various majors in electronic engineering, focusing on understanding computing thinking skills. Second, an efficient electronic engineering subject inquiry class operation using computing thinking ability was conducted, and electronic engineering-linked education contents based on the components of computer thinking were presented. Third, by conducting a case study on inquiry-style teaching using computing thinking skills in the electronic engineering curriculum, we identified the validity of the teaching method to strengthen major competency. In order to prepare for the 4th Industrial Revolution, by implementing mathematics, probability, statistics-related linkage, and convergence education to foster convergent talent, we tried to present effective electronic engineering major competency enhancement measures and cope with innovative technological changes.

• Educational Technology International
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• 제18권2호
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• pp.131-157
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• 2017

The purposes of the study were to investigate the participation of artists in paper circuit computing learning and to conduct an in-depth study on the formation and development of practical knowledge. To do this, we selected as research participants six artists who participated in the learning program of an art museum, and used various methods such as pre-open questionnaires, participation observation, and individual interviews to collect data. The collected data were analyzed based on community of practice theory. Results showed that the artists participated in the learning based on a desire to use new technology or find a new work production method for interacting with their audiences. In addition, the artists actively formed practical knowledge in the curriculum and tried to apply paper circuit computing to their works. To continuously develop the research, participants formed a study group or set up a practical goal through planned exhibitions. The results of this study can provide implications for practical approaches to, and utilization of, paper circuit computing.

• 한국IT서비스학회지
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• 제6권2호
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• pp.99-112
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• 2007

In this study we designed the killer services for the scene of labor learning in ubiquitous computing. To achieve this study, we have explored the unmet needs of teachers in the scene of labor learning and examined whether the unmet needs could be served by the resources and capabilities of ubiquitous computing. Then, we have crafted a detail killer services that includes value propositions and resource maps by using statistical methodology. Finally, the killer services for the scene of labor learning proposed to serve educational users with the service architecture. The result of this study will be applied to develop new business model in ubiquitous computing as the basic research.

• 한국실천공학교육학회논문지
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• 제3권1호
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• pp.69-75
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• 2011

본 논문에서는 유비쿼터스 컴퓨팅 환경에 기반을 둔 e-러닝 모델을 제안하였다. 이를 위해 국내외 대학의 진보된 e-러닝 시스템을 조사 및 분석하였으며, 이를 근간으로 유비쿼터스 환경에 기반을 둔 최적의 e-러닝 모델을 제안하였다. 제안한 모델은 최적의 e-러닝 하드웨어 및 소프트웨어, 그리고 다양한 e-러닝 서비스를 포함하고 있다. 여기에는 출결체크 서비스, 수업운영 서비스, 공용지식 서비스, 성적처리 서비스, 편의시설 서비스, 개인운영 서비스, 신용조회 서비스, 캠퍼스안내 서비스, 강의실운영 서비스 등이 있다. 또한, 실험.실습에 관련된 서비스도 포함하고 있다.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.605-615
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• 2005

This paper represent a method of U-Learning based on advanced e-Learning. Ubiquitous computing configuration and advanced Information technology. As we know well, the 21th century is called knowledge based informational society. Many scholar stress that the improved 21th century's educational paradigm be able to success based on advanced educational paradigm. Therefore, we discuss the material for e-Learning fields including with necessity, vision, law, quality authorization etc. Also, we discuss the relational technologies including with meta data, standardization, identification etc. Finally, we propose a method for constructing the U-Learning based on advanced e-Learning and Ubiquitous computing configuration.

• 정보교육학회논문지
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• 제22권3호
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• pp.297-305
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• 2018

피지컬 컴퓨팅은 컴퓨팅을 인간과 환경, 사물의 영역으로 확장한 개념으로 하드웨어와 소프트웨어 통합한 물리적 산출물 기반의 프로그래밍 학습매체로 주목받고 있다. 본 연구에서는 기술적 자유도가 높은 피지컬 컴퓨팅의 특징을 활용한 인터랙티브 프로토타이핑 기반의 프로그래밍 학습 모형을 개발하고 실험연구를 통해 학습 효과를 분석하였다. 실험처치 효과 검증을 위해 초등학교 5학년 59명 학습자를 대상으로 실험집단과 통제집단으로 구성하고 실험집단에는 인터랙티브 프로토타이핑 프로그래밍 학습모형을 적용하고 통제집단에는 선형순차 프로그래밍 학습모형을 적용하였다. 실험처치 전 후 정보과학 창의적 성향 검사를 실시하였고 두 집단의 사전검사 점수를 공변량으로 처리한 공분산분석(ANCOVA) 결과 유의수준 .05에서 학습 효과가 있음을 증명하였다. 이를 통해 초등학교 5학년 학습자를 대상으로 피지컬 컴퓨팅 기반의 인터랙티브 프로토타이핑 프로그래밍 학습모형의 프로그래밍 학습에 적용 가능성을 시사한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• 대한임베디드공학회논문지
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• 제17권2호
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.