• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.456-458
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• 2022

국제해사기구의 환경규제로 조선소에서는 선박의 효율향상을 위한 다양한 연구를 추진하고 있으며, 선박의 무게절감을 위한 노력이 진행 중이다. 최근, CNT소재를 포함하는 복합소재는 일반 철판 소재 대비 40% 이상 무게절감이 가능한 장점이 있어, 선박의 클램프나 도어스킨으로 대체사용이 가능한 장점이 있다. 이에, 본 연구에서는 CNT소재를 포함하는 복합소재의 수명예측을 위해, 가속열화시험 방법과 머신러닝 기법을 이용한 수명예측을 통해 결과를 비교하였다. 가속열화시험은 아레니우스 모델식을 이용하였고, 머신러닝 기법은 회기분석 알고리즘을 이용하여 수명을 예측하였다.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2011년도 동계학술대회
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• pp.29-35
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• 2011

세계는 매우 빠른 속도로 변하고 있으며 이 변화 속도는 21세기를 살아 갈 어린이들에게 보다 더 효율적으로 빠르게 학습하고 창의적으로 사고 할 수 있는 능력을 요구한다. 이러한 상황 속에서 컴퓨터 교육 분야의 중요성은 점점 더 높아지고 있으나 학교 현장에서는 컴퓨터 교육과 관련한 시수가 줄고 있는 추세이다. 이와 같은 조건에서 학습자 중심으로 가속하는 변화를 다스릴 수 있도록 새로운 정보를 빠르게 흡수하고 이해할 수 있는 능력과 그 정보를 보유할 수 있는 능력을 중요시하는 교수 방법인 가속학습(Acceleated Learning)을 정보통신기술교육에 적용하여 시간적인 한계를 극복하고 제한된 시간 안에 최대한의 효과를 끌어내고자 한다. 이러한 목적을 달성하기 위해 가속학습이론에 기반을 둔 6단계 수업활동과 다중지능이론을 적용한 PLAY(Pre-processing, Learning how to recognize, Activating the problem solving, Yield product) 모형을 구안하였으며, 이를 경기도 남양주시에 소재하고 있는 초등학교 2학년 2개 학급 70명을 대상으로 10차시 분량의 정보통신기술교육 실험 수업을 실시하였다.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• Nuclear Engineering and Technology
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• 제52권4호
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• pp.784-790
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• 2020

The accelerated corrosion by Flow Accelerated Corrosion (FAC) has caused unexpected rupture of piping, hindering the safety of nuclear power plants (NPPs) and sometimes causing personal injury. For the safety, it may be necessary to select some pipes in terms of condition monitoring and to measure the change in thickness of pipes in real time. Direct current potential drop (DCPD) method has advantages in on-line monitoring of pipe wall thinning. However, it has a disadvantage in that it is difficult to quantify thinning due to various thinning shapes and thus there is a limitation in application. The machine learning approach has advantages in that it can be easily applied because the machine can learn the signals of various thinning shapes and can identify the thinning using these. In this paper, finite element analysis (FEA) was performed by applying direct current to a carbon steel pipe and measuring the potential drop. The fundamental machine learning was carried out and the piping thinning model was developed. In this process, the features of DCPD to thinning were proposed.

• 터널과지하공간
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• 제6권4호
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• pp.316-325
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• 1996

A new accelerated neural network adopting modified sigmoid function was developed and applied to estimate engineering properties of rock from insufficient geological data. Developed network was tested on the well-known XOR and character recognition problems to verify the validity of the algorithms. Both learning speed and recognition rate were improved. Test learn on the Lee and Sterling's problems showed that learning time was reduced from tens of hours to a few minutes, while the output pattern was almost the same as other studies. Application to the various case studies showed exact coincidence with original data or measured results.

• 건설관리
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• 제3권1호
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• pp.53-56
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• 2002

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2004년도 e-Biz World Conference
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• pp.294-301
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• 2004

·Text-mining approach to e-documents organization based on topic hierarchy - Machine-Learning ＆ information Theory-based ㆍ 'Category(topic) discovery' problem → document bundle-based user-constraint document clustering ㆍ 'Automatic categorization' problem → Accelerated EM with CU-based active learning → 'Hierarchy Construction' problem → Unsupervised learning of category subsumption relation

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• 대한전기학회논문지
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• 제39권8호
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• pp.856-867
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• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권11호
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• pp.1431-1438
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• 2004

강화학습(reinforcement learning)은 온라인으로 환경(environment)과 상호작용 하는 과정을 통하여 목표를 이루기 위한 전략을 학습한다. 강화학습의 기본적인 알고리즘인 Q-learning의 학습 속도를 가속하기 위해서, 거대한 상태공간 문제(curse of dimensionality)를 해결할 수 있고 강화학습의 특성에 적합한 함수 근사 방법이 필요하다. 본 논문에서는 이러한 문제점들을 개선하기 위해서, 온라인 퍼지 클러스터링(online fuzzy clustering)을 기반으로 한 Fuzzy Q-Map을 제안한다. Fuzzy Q-Map은 온라인 학습이 가능하고 환경의 불확실성을 표현할 수 있는 강화학습에 적합한 함수근사방법이다. Fuzzy Q-Map을 마운틴 카 문제에 적용하여 보았고, 학습 초기에 학습 속도가 가속됨을 보였다.