• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.346-347
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• 2021

기계학습을 위한 패턴인식을 위해서는 학습데이터의 양이 많을수록 그 성능이 향상된다. 하지만 일상에서 검출해내야하는 패턴의 종류 및 정보가 항상 많은 양의 학습데이터를 확보할 수는 없다. 따라서 일반적인 기계학습을 위해 적은데이터셋을 의미있게 부풀릴 필요가 있다. 본 연구에서는 기계학습을 수행할 수 있도록 데이터를 증강시키는 기법에 관해 연구한다. 적은데이터셋을 이용하여 기계학습을 수행하는 대표적인 방법이 전이학습(transfer learning) 기법이다. 전이학습은 범용데이터셋으로 기본적인 학습을 수행한 후 목표데이터셋을 최종 단계에 대입함으로써 결과를 얻어내는 방법이다. 본 연구에서는 ImageNet과 같은 범용데이터셋으로 학습시킨 학습모델을 증강된 데이터를 이용하여 특징추출셋으로 사용하여 원하는 패턴에 대한 검출을 수행한다.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• 한국전자통신학회논문지
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• 제14권1호
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• pp.169-178
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• 2019

현재 인공지능의 한 영역인 머신러닝을 적용하여 다양한 예측을 수행하고 있으나 실제 현장에서 어떤 종류의 알고리즘을 사용하는 것이 가장 좋은 방법인지는 늘 문제가 된다. 본 논문은 여러 머신러닝 지도 학습 알고리즘을 이용하여 월별 전력 거래량, 전력 거래금액, 월별 생산 확산 지수, 최종 에너지 소비, 자동차용 경유를 예측하여 각 경우에 어떤 알고리즘이 가장 적합한 알고리즘인지를 알아본다. 이를 위해 통계청에 나와 있는 월별 전력 거래량과 월별 전력 거래금액, 월별 생산 확산 지수, 최종에너지 소비, 자동차용 경유로 머신 러닝이 예측하는 값의 확률을 보여주고 각각의 예측 값을 평균화 하여 이들 중에서 어떤 기법이 가장 우수한 기법인지를 확인한다.

• ETRI Journal
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• 제32권5호
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• pp.766-773
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• 2010

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call "the small example problem" and "the asymmetric distribution problem." This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.

• 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.

• 산업경영시스템학회지
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• 제46권1호
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• 산업경영시스템학회지
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• 제44권3호
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제35권4호
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• pp.445-473
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• 2021

이 연구는 2015 개정 교육과정에서 신설 과목으로 설계된 <인공지능 수학> 교과서를 분석하여 차기 교육과정 설계의 시사점을 도출하는 데 목적이 있다. <인공지능 수학> 시안을 담은 수학과 교육과정 문서에서는 '학습 요소' 대신에 '관련 학습 요소'를 제시하고 있다. '관련 학습 요소'는 인공지능의 맥락에서 활용될 수 있는 수학적 개념이나 원리로 정의하고 있는데 '관련 학습 요소'를 다루는 범위와 방법에 대해서는 구체적인 제한은 없다. 이에 '관련 학습 요소'가 <인공지능 수학> 교과서에서 반영된 양상을 형식, 범위와 방법, 공학적 도구 활용 방식을 중심으로 분석하였다. 교과서별로 '관련 학습 요소'를 교과서에 기술하는 형식상의 차이와 수학 개념을 취급하는 양과 범위에 차이가 있었다. 또한, '관련 학습 요소'를 하나의 수학 개념과 동일하게 정의하여 사용한 경우와 정의보다는 인공지능의 맥락에서 설명 위주로 서술하였다. '관련 학습 요소'를 인공지능의 맥락에서 활용할 수 있도록 교과서별로 유사한 공학적 도구를 다루었지만, 계산과 결과를 해석하는 활동 중심이었다. 고등학교 수학 과목으로서 <인공지능 수학>의 지향을 교과서에 충분히 반영하기 위해서 '관련 학습 요소'에 관한 체계적인 논의가 필요하다. 또한, 학생들이 인공지능 맥락의 활용 사례를 경험하기 위해서는 공학적 도구를 활용하여 문제를 설정하고 해결할 수 있는 내실화된 활동이 교과서에 구현되어야 할 것이다.