• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.18-25
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• 2001

KBANN (knowledge-based artificial neural network) combining the analytical learning and the inductive learning has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects. The algorithms which could solve this TopGen's defects, enabling the refinement of theory, by extending KBANN, are designed.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.633-635
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• 2023

센서 데이터를 예측 또는 분석하여 시스템을 제어하거나 모니터링할 수 있다. 센서 데이터를 이용한 예측의 신뢰성을 확보하기 위해서는 데이터의 적절한 빈도수가 중요하다. 이를 위해 본 논문에서는 Diffusion Model을 사용한 센서 데이터 주파수 보간을 통해 행동을 예측하는 방법을 제시하고자 한다. 주파수 보간은 반려동물 행동별 25hz 센서 데이터로 학습된 Diffusion Model을 사용한다. 학습된 Diffusion Model에 1hz 센서 데이터와 가우시안 노이즈를 결합한 데이터를 입력으로 사용해 센서데이터를 보간한다. 제안한 방법은 CNN-LSTM 모델 학습 후 예측 성능 비교를 통해 검증한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.425-432
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• 2009

In this study, the ability of neural network in modeling and predicting of the unsteady aerodynamic force coefficients of 2D airfoil with the data obtained from Euler CFD code has been confirmed. Neural network models are constructed based on supervised training process using Levenberg-Marquardt algorithm, combining this into genetic algorithm, hybrid genetic algorithm and the efficiency of the two cases are analyzed and compared. It is shown that hybrid-genetic algorithm is more efficient for neural network of complex system and the predicted properties of the unsteady aerodynamic force coefficients of 2D airfoil by the neural network models are confirmed to be similar to that of the numerical results and verified as suitable representing reduced models.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.15-25
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• 2024

For effective analysis of animal ecosystems, technology that can automatically identify the current status of animal habitats is crucial. Specifically, animal sound classification, which identifies species based on their sounds, is gaining great attention where video-based discrimination is impractical. Traditional studies have relied on a single deep learning model to classify animal sounds. However, sounds collected in outdoor settings often include substantial background noise, complicating the task for a single model. In addition, data imbalance among species may lead to biased model training. To address these challenges, in this paper, we propose an animal sound classification scheme that combines predictions from multiple models using Focal Loss, which adjusts penalties based on class data volume. Experiments on public datasets have demonstrated that our scheme can improve recall by up to 22.6% compared to an average of single models.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.407-407
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• 2010

본 논문에서는 확장된 히든마코브모델을 이용하여 플라즈마 식각공정에서 식각종료검출을 위한 방법을 연구하였다. 플라즈마 식각장비는 유도성 결합플라즈마 시스템을 사용하였으며, 종료점 검출을 위해 식각공정이 진행됨에 따른 플라즈마의 상태를 확인할 수 있는 광학 방사 분광기(Optical Emission Spectroscopy: OES)를 사용하였다. 식각이 진행되는 동안 여기되는 입자들은 특정한 재료에 해당하는 파장에서 빛을 방출한다. 플라즈마상태에서 여기되는 원자와 분자들에 의해서 방출되는 빛은 OES를 통해 식각되는 물질을 확인하기 위해서 특별한 파장의 빛을 선택하여 분석한다. 본 논문에서는 확장된 히든마코브모델을 이용해 산화물이 식각될 때 방출하는 고유한 파장의 빛을 분석하여 식각이 종료되는 시점을 찾는 연구를 하였다. 제안된 확장형 히든마코브 모델은 세미-마코브모델과 분절특징 히든마코브모델을 결합한 것으로, 확률적 통계기법을 통해 종료시점을 찾아내는 방법이다. OES를 통해 얻은 데이터는 식각 종료가 일어나기 전의 파장의 상태와 식각이 종료된 후의 파장의 상태로 구분되어지는데, 식각종료시점에서 파장의 상태가 변화하며 이를 감지하여 식각종료점을 검출한다. 분절특징 히든마코브모델을 이용하여 식각종료시점 전후의 파장의 상태를 모델링 하였으며, 일반적인 마코브 모델의 특정상태가 유지될 시간의 확률을 변형된 세미-마코브 모델을 이용하여 OES를 통해 얻은 데이터 내에서 식각 종료가 일어나기 전의 상태가 유지될 수 있는 확률을 모델링 하였다. 실험을 통해 얻어진 6개의 데이터중 4개를 학습을 위해 사용하여 모델링을 하였고 나머지 2개의 데이터를 검증을 위해 사용한 결과, 확장형 히든마코브모델의 식각종료시점검출에 있어 뛰어난 정확성과 우수성을 증명하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• Proceedings of the Technology Innovation Conference
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• 2003.02a
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• pp.173-190
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• 2003

Demand forecasting is essential in establishing national and corporate strategy as well as the management of their resource. We forecast demand for multi-generation product using discrete choice model combining diffusion model The discrete choice model generally captures consumers'valuation of the product's qualify in the framework of a cross-sectional analysis. We incorporate diffusion effects into a discrete choice model in order to capture the dynamics of demand for multi-generation products. As an empirical application, we forecast demand for worldwide DRAM (dynamic random access memory) and each of its generations from 1999 to 2005. In so doing, we use the method of 'Technological Forecasting'for DRAM Density and Price of the generations based on the Moore's law and learning by doing, respectively. Since we perform our analysis at the market level, we adopt the inversion routine in using the discrete choice model and find that our model performs well in explaining the current market situation, and also in forecasting new product diffusion in multi-generation product markets.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.329-330
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• 2021

벌크화물운송은 해상운송시장에서 가장 큰 규모이고 철강 및 에너지 산업을 뒷받침 하는 중요한 시장이다. 또한 운임의 변동성이 가장 큰 시장으로 상당한 수익을 기대할 수 있는 반면에 파산에 이르는 큰 손실이 발생할 수 있기때문에 시장 참여자들은 합리적이고 과학적인 예측을 기반하여 의사결정을 해야 한다. 그러나 해운시장에서는 과학적 의사결정보다는 경험기반의 의사결정에 의존하기 때문에 시황변동성에 취약하다. 본 논문은 벌크운임예측에 신호 분해 방법인 EMD와 인공신경망을 결합한 하이브리드 모델을 적용하여 과학적 예측방법을 제시하고자 한다. 본 논문은 학문적으로 해운시장 운임예측연구에서 거의 시도되지 않았던 시계열분해법과 기계학습기법을 결합한 하이브리드 모델을 제시하였다는데 의미가 있으며 실무적으로는 해운시장에서 빈번이 일어나는 의사결정의 질이 제고되는데 기여할 것으로 기대된다.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2078-2079
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• 2009

This research suggests the framework for human body learning system using various forms of bidirectional interfaces. The existing systems mostly use the limited and unidirectional methods which are merely focused on the visual information. Our system provides more realistic visual information using 3D organ models from the real human body. Also we combine the haptic and augmented reality techniques into our system for wider range of interaction means. Through this research, we aim to overcome the limitation of existing science education systems and explore the effective scheme to fuse the real and virtual educational environment into one.