• Design & Manufacturing
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• 제14권3호
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• 한국가시화정보학회지
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• 제21권1호
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• pp.103-109
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• 2023

This research developed a measurement technique that can measure the oxygen temperature inside a high temperature furnace. Instead of measuring only changes in frequency components within a small range used in the existing variable laser absorption spectroscopy, laser spectroscopy technology was used to spread out wavelength of the light source passing through the gas Based on a total of 20,000 image data, research was conducted to predict the temperature of a high-temperature furnace using CNN with black and white images in the form of spectral bands by temperature of 25 to 800 degrees. The optimal model was found through Hyper parameter optimization, R2 score is 0.89, and the accuracy of the test data is 88.73%. Based on this research, it is expected that concentration measurement and air-fuel ratio control technology can be applied.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.115-128
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• 2023

Deep learning has been incorporating various optimization techniques motivated by new pragmatic optimizing algorithm advancements and their usage has a central role in Machine learning. In recent past, new avatars of various optimizers are being put into practice and their suitability and applicability has been reported on various domains. The resurgence of novelty starts from Stochastic Gradient Descent to convex and non-convex and derivative-free approaches. In the contemporary of these horizons of optimizers, choosing a best-fit or appropriate optimizer is an important consideration in deep learning theme as these working-horse engines determines the final performance predicted by the model. Moreover with increasing number of deep layers tantamount higher complexity with hyper-parameter tuning and consequently need to delve for a befitting optimizer. We empirically examine most popular and widely used optimizers on various data sets and networks-like MNIST and GAN plus others. The pragmatic comparison focuses on their similarities, differences and possibilities of their suitability for a given application. Additionally, the recent optimizer variants are highlighted with their subtlety. The article emphasizes on their critical role and pinpoints buttress options while choosing among them.

• 융합정보논문지
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• 제11권7호
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• pp.31-38
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• 2021

본 논문은 감성 분석에 사용되는 RNN 모델의 최적화를 얻기 위한 성능분석을 위하여 하이퍼파라미터 튜닝에 따른 손실과 정확도의 추이를 관찰하여 모델과의 상관관계를 연구하였다. 연구 방법으로는 시퀀셜데이터를 처리하는데 가장 최적화된 LSTM과 Embedding layer로 히든레이어를 구성한 후, LSTM의 Unit과 Batch Size, Embedding Size를 튜닝하여 각각의 모델에 대한 손실과 정확도를 측정하였다. 측정 결과, 손실은 41.9%, 정확도는 11.4%의 차이를 나타내었고, 최적화 모델의 변화추이는 지속적으로 안정적인 그래프를 보여 하이퍼파라미터의 튜닝이 모델에 지대한 영향을 미침을 확인하였다. 또한 3가지 하이퍼파라미터 중 Embedding Size의 결정이 모델에 가장 큰 영향을 미침을 확인하였다. 향후 이 연구를 지속적으로 이어나가 모델이 최적의 하이퍼파라미터를 직접 찾아낼 수 있는 알고리즘에 대한 연구를 지속적으로 이어나갈 것이다.

• 한국융합학회논문지
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• 제12권8호
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• pp.31-37
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• 2021

산업의 발전으로 도시화로 인해 건물의 규모가 커지면서, 건물의 공기 정화 및 쾌적한 실내 환경을 유지의 필요성 또한 증가하고 있다. 냉동 시스템의 모니터링 기술의 발전으로 건물 내에 발생하는 전력 소모량을 관리할 수 있게 되었다. 특히 상업용 건물에서 발생하는 전력 소모량 중 약 40%가 냉동 시스템에서 일어난다. 따라서 본 연구 냉동시스템 고장진단 알고리즘을 개발하기 위해서 냉동시스템의 구조를 이해하고, 냉동 시스템의 운영과정에서 발생하는 데이터를 수집 분석하여 다양한 유형과 심각도를 가지는 고장 상황을 조기에 신속하게 탐지 분류하고자 하였다. 특히 분류가 어려운 고장 유형들의 분류 정확도를 향상시키기 위하여 3단계 진단 및 분류 알고리즘을 개발하여 제안하였다. 다수의 실험과 초모수 (hyper parameter) 최적화 과정을 거쳐 각 단계에 적합한 분류 모형으로 SVM과 LGBM에 기반 한 모형을 제시하였다. 본 연구에서는 고장에 영향을 미치는 특성을 최대한 보존하면서, 선행연구에서 어려움을 겪었던 냉매 관련 고장을 포함한 모든 고장 유형을 우수한 결과로 도출하였다.

• 한국수자원학회논문집
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• 제54권8호
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• pp.617-628
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• 2021

가뭄은 심각한 사회적 경제적 손실을 초래하는 주요 자연재해이다. 지역 가뭄 예측은 가뭄 대비에 중요한 정보를 제공할 수 있다. 본 연구에서는 한반도 동남부 부산-울산-경남 지역에서 1981년부터 2020년까지 10개 관측소의 과거 가뭄지수 및 기상 관측자료를 사용하여 가뭄을 예측하는 새로운 기계학습모델을 제안한다. 베이지안 최적화기법을 이용하여 하이퍼 파라미터가 튜닝된 Random Forest, XGBoost, Light GBM 모델을 구축하여 1개월 뒤의 6개월 시간 척도의 증발 수요 가뭄지수를 예측하였다. 단일 지점별 모델과 지역 모델을 각각 구성하여 모델 성능을 비교하였다. 또한 지역 모델을 기반으로 개별 지점의 자료에 대해 미세조정된 모델을 구성하여 모델 성능을 높일 가능성을 살펴보았다.

• 품질경영학회지
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• 제50권4호
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• 문화기술의 융합
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• 제9권2호
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• pp.475-481
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• 2023

머신러닝과 같은 소프트웨어가 일상생활에 매우 큰 영향력을 발휘하고 있는 상황에서, 소프트웨어의 개발비용을 평가하는 비용 모델의 중요성이 지속적으로 증가하고 있다. 비용 모델로서 LOC(Line of Code)와 M/M(Man-Month) 모델은 소프트웨어의 양적인 요소들을 측정하는 비용모델이다. 이와는 달리, FP(Function Point)는 소프트웨어의 기능적 특징들을 평가하는 비용모델로서 소프트웨어의 질적인 요소를 평가한다는 점에서 효과적이다. 그러나 FP는 머신러닝 소프트웨어의 주요한 요소들을 평가하지 않기 때문에 머신러닝 소프트웨어를 평가하는데 한계를 가진다. 본 논문은 확장형 FP(Extended Function Point, ExFP)를 제안한다. 확장형 FP는 머신러닝의 주요 특징인 하이퍼 파라미터와 그것의 최적화에 대한 복잡도를 반영하여 소프트웨어의 기능적 요소를 평가하도록 확장하였기 때문에 머신러닝과 같은 최신 소프트웨어에의 비용 평가에 적합하다. 머신러닝 소프트웨어의 특징을 반영한 평가를 통해 제안된 확장형 FP의 효용성을 보였다.