• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.93-95
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• 1991

As the ensemble averaged dZ/dt signal during exercise is smoothed, it is difficult to find the distinctive marks. The cross correlation function was made use of estmating these marks. LVET was calculated based on the calculated parameters of the characteristic points. For the accuracy validation, LVET calculated by hand, by the ensemble average and the cross correl at ion were compared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2919-2937
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• 2020

Post-Quantum Cryptography (PQC) is rapidly developing as a stable and reliable quantum-resistant form of cryptography, throughout the industry. Similarly to existing cryptography, however, it does not prevent a third-party from using the secret key when third party obtains the secret key by deception, unauthorized sharing, or unauthorized proxying. The most effective alternative to preventing such illegal use is the utilization of biometrics during the generation of the secret key. In this paper, we propose a biometric-based secret key generation scheme for multivariate quadratic signature schemes, such as Rainbow. This prevents the secret key from being used by an unauthorized third party through biometric recognition. It also generates a shorter secret key by applying Principal Component Analysis (PCA)-based Confidence Interval Analysis (CIA) as a feature extraction method. This scheme's optimized implementation performed well at high speeds.

• The Journal of Engineering Geology
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• v.11 no.3
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• pp.269-278
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• 2001

We describe the calculation of thermal expansion coefficients of $\alpha$-quartz, muscovite and albite using a MD simulation method. The selection of interatomic potentials is important for the MD calculation, and we used the 2-body interatomic potential function. The coefficients are calculated using a differential operation of the temperature dependence of the lattice constant obtained from the NPT-ensemble molecular dynamics simulation. Reasonable agreement is found between the analytical results and measured data.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.818-819
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• 2016

지오코딩(Geocoding)은 자연어로 표현된 주소를 컴퓨터가 인지 가능한 (x,y) 좌표로 변환하는 과정이며, 지리정보 분석 등 다양한 영역의 필수적인 전처리 과정에서 사용된다. 현재 국내 주소를 지오코딩하는 API를 제공하는 서비스 프로바이더는 다수 존재하나, 성능 향상의 여지가 남아있는 현황이다. 본 연구에서는 지오코딩 위치정확도의 향상을 위해 Euclidean/Edit distance 기반 앙상블(Ensemble) 지오코딩 알고리즘(EEE-Geocoding)을 제안하였다. 화학물질 보유 업체 5569개소의 주소를 토대로 제안 기법에 대한 성능평가 실험을 진행하였으며, 평가결과는 0.99 precision, 0.87 recall, 0.92 F1 score 이었다.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.93-102
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• 2022

Dependency parsing is a decision problem of the syntactic relation between words in a sentence. Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space. However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space. To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit. Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM. Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model. The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.524-527
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• 2022

본 연구에서는 머신러닝(Machine Learning, ML)과 딥러닝(Deep Learning, DL) 모델을 앙상블(Ensemble)하여 어떠한 주가 예측 방법이 우수한지에 대한 연구를 하고자 한다. 연구에 사용된 모델은 하이퍼파라미터(Hyperparameter) 조정을 통하여 최적의 결과를 출력한다. 앙상블 방법은 머신러닝과 딥러닝 모델의 앙상블, 머신러닝 모델의 앙상블, 딥러닝 모델의 앙상블이다. 세 가지 방법으로 얻은 결과를 평균 제곱근 오차(Root Mean Squared Error, RMSE)로 비교 분석하여 최적의 방법을 찾고자 한다. 제안한 방법은 주가 예측 연구의 시간과 비용을 절약하고, 최적 성능 모델 판별에 도움이 될 수 있다고 사료된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.95-97
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• 2022

본 논문에서는 산업 현장에서 발생하는 불량품 탐지 분야에서 효율적으로 생산품의 불량을 탐지할 수 있는 PaDiM 구조의 Backbone 모델을 단일 Wide-ResNet 대신 두 개의 Wide-ResNet을 사용함으로써, 단일 모델에서 추출된 저차원의 Feature를 앙상블을 통해 성능 향상을 일으킬 수 있는 것을 증명하였다. 단일 Wide-ResNet 환경에서는 MVTec 데이터셋에서 생성된 다변량 가우시안 분포가 데이터셋의 적은 샘플수로 인하여 각 클래스 간 불균형이 발생하는 문제를 동종 앙상블을 통해 해결할 수 있었다. 따라서 본 논문에서는 제안하는 동종 모델의 앙상블을 사용함으로써 기존의 One-class classification 환경에서 불량품 탐지환경에서 적은 수의 데이터 샘플 환경에서 성능 향상을 나타낼 수 있음을 입증하였다.

• Journal of the Korean Magnetic Resonance Society
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• v.27 no.4
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• pp.28-34
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• 2023

We study the linear-nonlinear quantum transport theory of Wannier-Landau transition system in the confinement of electrons by a square well confinement potential. We use the projected Liouville equation method with the ensemble density projection technique. We select the dynamic value under a linearly oscillatory external field. We derive the dynamic value formula and the memory factor functions in three electron phonon coupling systems and electron impurity coupling systems of two transition types, the intra-band transitions and inter-band transitions. We obtain results that can be applied directly to numerical analyses. For simple example of application, we analyze the absorption power and line-widths of ZnO, through the numerical calculation of the theoretical result in the Landau system.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.61-68
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• 2019

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.