• 대한영상의학회지
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• 제83권6호
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• pp.1219-1228
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• 2022

최근 영상의학 연구 분야에서 영상 인자를 포함한 임상 예측 모형의 수요가 증가하고 있고, 특히 라디오믹스 연구가 활발하게 이루어지면서 기존의 전통적인 회귀 모형뿐만 아니라 머신러닝을 사용하는 연구들이 많아지고 있다. 본 종설에서는 영상의학 분야에서 예측 모형 연구에 사용된 통계학적 방법과 머신 러닝 방법들을 조사하여 정리하고, 각 방법론에 대한 설명과 장단점을 살펴보고자 한다. 마지막으로 예측 모형 연구에서 분석 방법 선택에서의 고려사항을 정리해 보고자 한다.

• 정보과학회 논문지
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• 제43권3호
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• pp.362-369
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• 2016

본 논문은 음절 단위 한국어 품사 태깅 방법의 성능 개선을 위해 기분석사전과 기계학습 방법을 결합하는 방법을 제안한다. 음절 단위 품사 태깅 방법은 형태소분석을 수행하지 않고 품사 태깅만을 수행하는 방법이며, 순차적 레이블링(Sequence Labeling) 문제로 형태소 태깅 문제를 접근한다. 본 논문에서는 순차적 레이블링 기반 음절 단위 품사 태깅 방법의 전처리 단계로 품사 태깅말뭉치와 국어사전으로부터 구축된 복합명사 기분석사전과 약 1천만 어절의 세종 품사 태깅말뭉치로부터 자동 추출된 어절 사전을 적용함으로써 품사 태깅 성능을 개선시킨다. 성능 평가를 위해서 약 74만 어절의 세종 품사 태깅말 뭉치로부터 67만 어절을 학습 데이터로 사용하고 나머지 7만 4천 어절을 평가셋으로 사용하였다. 기계학습 방법만을 사용한 경우에 96.4%의 어절 정확도를 보였으며, 기분석사전을 결합한 경우에는 99.03%의 어절 정확도를 보여서 2.6%의 성능 개선을 달성하였다. 퀴즈 분야의 평가셋으로 실험한 경우에도 기계학습 엔진은 96.14% 성능을 보인 반면, 하이브리드 엔진은 97.24% 성능을 보여서 제안 방법이 다른 분야에도 효과적임을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5765-5781
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• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• 문화기술의 융합
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• 제10권4호
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• pp.595-600
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• 2024

학습 성과를 예측하거나 학습 경로를 최적화하는 연구 분야에서 기계학습과 같은 인공지능 기술의 사용이 점차 증가하면서 교육 분야의 인공지능 활용은 점차 많은 진전을 보이고 있다. 이러한 연구는 점차 심층학습과 강화학습과 같은 좀 더 고도화된 인공지능 방법으로 진화하고 있다. 본 연구는 학습자의 과거 학습 성과-이력 데이터를 기반으로 미래의 학습 성과를 예측하는 방법을 개선하는 것이다. 따라서 예측 성능을 높이기 위해 Markov Chain 방법을 응용한 조건부 확률을 제안한다. 이 방법은 기계학습에 의한 분류 예측에 추가하여 학습자가 학습 이력 데이터를 분류 예측에 추가함으로써 분류기의 예측 성능을 향상 시키기 위해 사용된다. 제안 방법의 효과를 확인하기 위해서 실증 데이터인 '교구 기반의 유아 교육 학습 성과 데이터'를 활용하여 기존의 분류 알고리즘과 제안 방법에 의한 분류 성능 지표를 비교하는 실험을 수행하였다. 실험 결과, 분류 알고리즘만 단독 사용한 사례보다 제안 방법에 의한 사례에서 더 높은 성능 지표를 산출한다는 것을 확인할 수 있었다.

• 한국운동역학회지
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• 제32권1호
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 춘계 학술논문 발표대회
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• pp.152-153
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.239-245
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• 2020

Blockchain, machine learning, and big data are among the key components of the future IT track. These technologies are used in various fields; hence their increasing application. This paper discusses the technologies developed in various research fields, such as data representation, Blockchain application, 3D shape recognition and classification, query method, classification method, and search algorithm, to provide insights into the future paradigm. In this paper, we present a summary of 18 high-quality accepted articles following a rigorous review process in the fields of Blockchain, machine learning, and big data.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.693-712
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• 2012

The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Na$\ddot{i}$ve Bayes, and Artificial Neural Networks for keyphrase extraction. We consider keyphrases as being phrases that consist of one or more words and as representing the important concepts in a text document. The three machine learning based keyphrase extraction methods that we use for experimentation have been compared with a publicly available keyphrase extraction system called KEA. The experimental results show that the Neural Network based keyphrase extraction method outperforms two other keyphrase extraction methods that use the Decision Tree and Na$\ddot{i}$ve Bayes. The results also show that the Neural Network based method performs better than KEA.