• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권10호
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• pp.2376-2394
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• 2013

In order to ensure both of the whole system capacity and users QoS requirements in heterogeneous wireless networks, admission control mechanism should be well designed. In this paper, Multi-agent Q-learning based Admission Control Mechanism (MQACM) is proposed to handle new and handoff call access problems appropriately. MQACM obtains the optimal decision policy by using an improved form of single-agent Q-learning method, Multi-agent Q-learning (MQ) method. MQ method is creatively introduced to solve the admission control problem in heterogeneous wireless networks in this paper. In addition, different priorities are allocated to multiple services aiming to make MQACM perform even well in congested network scenarios. It can be observed from both analysis and simulation results that our proposed method not only outperforms existing schemes with enhanced call blocking probability and handoff dropping probability performance, but also has better network universality and stability than other schemes.

• 대한간호학회지
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• 제42권6호
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• pp.771-782
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• 2012

Purpose: This study was conducted to develop and implement a case-based small group learning program on the care of children with infectious disease, and to examine its effects on knowledge, attitude and preventive practice behaviors of daycare center teachers compared to a control group. Methods: Based on the need assessment, the case-based learning program for the management of infectious children was developed. For this quasi-experimental study, 69 teachers were recruited from 14 child daycare centers in a city located in J province. Thirty four teachers were assigned to experimental group and participated in the case-based small group learning once a week for 5 weeks. Data were analyzed using the SPSS 18.0 program to perform ${\chi}^2$-test and t-tests. Analysis of covariance was used to treat the covariate of the number of assigned children between experimental and control groups. Results: The experimental group showed significantly higher posttest scores in knowledge, attitude and preventive practice behaviors than those of control group (p<.001). Conclusion: These findings indicate that case-based small group learning is an effective educational strategy for daycare center teachers to learn infection management through the emphasis of self-reflection and discussion.

• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• 실천공학교육논문지
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• 제16권2호
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• pp.215-226
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• 2024

본 연구는 일학습병행 사업관계자 직무연수 참여자 2,543명의 교육훈련 만족도 조사 결과를 활용하여 일학습병행 공동훈련센터 전담자를 주 대상으로 한 교육훈련의 성과분석을 진행하고, 교육과정 효과성 제고를 위한 시사점을 제시하였다. 연구 결과 일학습병행 공동훈련센터 전담자 교육훈련은 매우 높은 수준의 학습자 만족도를 나타내 성공적인 교육훈련을 제공한 것으로 평가되었다. 교육과정 운영 현황 및 교육 참여자 특성은 학습자 만족도에 영향을 미치는 것으로 나타났으며, 강사·주제·시설환경·운영 측면의 만족은 모두 교육과정 만족에 유의한 영향력을 미치는 것으로 나타났다.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2005년도 하계학술대회
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• pp.3-12
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• 2005

평생교육 종합정보시스템은 국민들 누구나 자신이 원하는 정보에 접근할 수 있도록 평생교육에 대한 Guide 역할을 수행하는 평생교육 포탈사이트 구축을 목표로 하고 있으며, 나아가 평생교육의 'Information', 'Learning', 'Communication', 'Business' 창구로서의 역할을 하고 있다. 그러나 현재 이러한 비전을 수행할만한 시스템 설계 원리나 구체적인 기능들에 대해서는 논란이 되고 있는 것 같다. 이에 한국교육개발원 평생교육센터에서는 평생교육 체제에 관한 국민들의 요구를 반영할 새로운 시스템을 설계 중에 있다. 이 글은 이런 목표를 담당할 시스템의 설계에 관한 이론적인 접근이다.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

• 한국의학물리학회지:의학물리
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• 제30권2호
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2005년도 추계 종합학술대회 논문집
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• pp.428-432
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• 2005

본 논문에서는 국내에 산재한 생물다양성정보를 학습에 활용하기 위하여 KISTI에서 구축한 생물다양성 DB 현황과 e-Learning의 기술요소 등을 조사하였으며, 기존에 구축된 생물다양성정보 DB를 활용하여 일반인과 학생들을 위한 생물다양성 학습 콘텐트를 기획하고 설계하였다. 본 설계를 바탕으로 생물다양성 콘텐트를 개발한다면, 국토가 좁고, 네트워크 인프라가 잘 갖추어져 있는 우리나라의 실정에 맞는 사이버공간상의 학습의 장으로서 일반인과 학생들에게 양질의 생물다양성 학습 콘텐트를 제공할 수 있으리라 기대한다.

• 한국인공지능학회지
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• 제9권1호
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• pp.1-7
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• 2021

In this paper, We focused the issue of creating a socially problematic nurse schedule. The nurse schedule should be prepared in consideration of three shifts, appropriate placement of experienced workers, the fairness of work assignment, and legal work standards. Because of the complex structure of the nurse schedule, which must reflect various requirements, in most hospitals, the nurse in charge writes it by hand with a lot of time and effort. This study attempted to automatically create an optimized nurse schedule based on legal labor standards and fairness. We developed an I/O Q-Learning algorithm-based model based on Python and Web Application for automatic nurse schedule. The model was trained to converge to 100 by creating an Fairness Indicator Score(FIS) that considers Labor Standards Act, Work equity, Work preference. Manual nurse schedules and this model are compared with FIS. This model showed a higher work equity index of 13.31 points, work preference index of 1.52 points, and FIS of 16.38 points. This study was able to automatically generate nurse schedule based on reinforcement Learning. In addition, as a result of creating the nurse schedule of E hospital using this model, it was possible to reduce the time required from 88 hours to 3 hours. If additional supplementation of FIS and reinforcement Learning techniques such as DQN, CNN, Monte Carlo Simulation and AlphaZero additionally utilize a more an optimized model can be developed.

• 공학교육연구
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• 제14권4호
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• pp.11-19
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• 2011

본 연구의 목적은 공과대학생을 위한 교수학습 전략에 대한 시사점을 도출하기 위해 공과대학 신입생의 학습양식과 학습전략 수준을 검토하고, 학습양식에 따른 학습전략 활용 정도를 분석하였다. 서울에 소재한 K대학교와 H대학교의 1학년생 273명을 대상으로 분석한 결과, 감각형, 시각형, 숙고형, 순차형이 더 높은 비율을 차지하였다. 학습전략은 3.28점(SD=0.38)으로서 평균 수준이었으며, 상대적으로 초인지 영역의 수준이 가장 높고, 내적관리 영역 수준이 가장 낮은 것으로 나타났다. 한편, 학습양식에 따른 학습전략 수준의 차이를 분석한 결과, 정보처리 차원에서 학습전략의 유의한 차이가 나타났는데 적극적 학습자의 학습전략 수준이 높은 것으로 나타났다. 세부 전략에서는 인지 영역과 내적/외적관리 영역에서 학습양식별 유의한 차이를 보였다. 본 연구 결과에 기초하여 공과대학의 교수전략 및 학습전략에 대한 시사점을 도출하였다.