• 대한토목학회논문집
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• 제14권3호
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• pp.523-534
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• 1994

본 연구의 목적은 간헐수문과정인 일강수계열의 모의발생 모델을 개발하는 것이다. 이를 위하여 연구(I)에서는 교대재생과정을 이용하여 강수발생과정을 해석하였으며, 본 연구(II)에서는 강수발생과정으로 Markov 연쇄를 이용하고 습윤일의 강수량 분포를 조합하여 일 강수계열을 모의발생하는 추계학적 모델을 개발하였다. Markov 연쇄로는 상태 2(건조, 습윤)의 1차 연쇄를 사용하였으며, 습윤일의 강수량 분포는 연속확률분포인 Gamma, Pearson Type-III(PT3), Extremal Type-III(T3E), Weibull 분포를 적용하였다. 일 강수계열 자료의 계절적 변동성을 고려하여 월별로 분리하여 해석하였으며, 강수발생과정과 습윤일의 강수량과정을 조합하여 구성한 두 개의 모의발생 모델 M-W, M-G 모델을 낙동강과 섬진강 유역의 7개 관측소에 적용하여 관측치와 모의발생치를 비교하므로써 모의발생 모델의 적용성을 확인하였다.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.458-459
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• 2021

최근, 실내 위치 기반 서비스에서 정확한 서비스를 위해 Wi-Fi 핑거프린트 기반의 딥러닝 기술을 이용한 연구가 이루어지고 있다. 딥러닝 모델 중에서 과거의 정보를 기억할 수 있는 RNN 모델은 실내측위에서 연속된 움직임을 기억할 수 있어 측위 오차를 줄일 수 있다. 이때 학습 데이터로서 연속적인 순차 데이터를 필요로 한다. 그러나 일반적으로 Wi-Fi 핑거프린트 데이터의 경우 특정 위치에 대한 신호들만으로 관리되기 때문에 RNN 모델의 학습데이터로 사용이 부적절하다. 본 논문은 RNN 모델의 순차적인 입력 데이터의 생성을 위해 클러스터링을 통한 영역 데이터로 확장된 Wi-Fi 핑거프린트 데이터 기반 이동 경로의 예측을 통한 경로 생성 방법에 대해 제안한다.

• Structural Engineering and Mechanics
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• 제91권1호
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• pp.39-47
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• 2024

Bridge deterioration shows the change of bridge condition during its operation, and predicting bridge deterioration is important for implementing predictive protection and planning future maintenance. However, in practical application, the raw inspection data of bridges are not continuous, which has a greater impact on the accuracy of the prediction results. Therefore, two kinds of bridge deterioration models are established in this paper: one is based on the traditional regression theory, combined with the distribution fitting theory to preprocess the data, which solves the problem of irregular distribution and incomplete quantity of raw data. Secondly, based on the theory of Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the network is trained using the raw inspection data, which can realize the prediction of the future deterioration of bridges through the historical data. And the inspection data of 60 prestressed concrete box girder bridges in Xiamen, China are used as an example for validation and comparative analysis, and the results show that both deterioration models can predict the deterioration of prestressed concrete box girder bridges. The regression model shows that the bridge deteriorates gradually, while the LSTM-RNN model shows that the bridge keeps great condition during the first 5 years and degrades rapidly from 5 years to 15 years. Based on the current inspection database, the LSTM-RNN model performs better than the regression model because it has smaller prediction error. With the continuous improvement of the database, the results of this study can be extended to other bridge types or other degradation factors can be introduced to improve the accuracy and usefulness of the deterioration model.

• 전자공학회논문지B
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• 제28B권7호
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• pp.548-556
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• 1991

In this paper, Korean spoken continuous digits are ercognized using DHMM(Dynamic Hidden Markov Model) and lexical analysis to provide the base of developing voice dialing system. After segmentation by phoneme unit, it is recognized. This system can be divided into the segmentation section, the design of standard speech section, the recognition section, and the lexical analysis section. In the segmentation section, it is segmented using the ZCR, O order LPC cepstrum, and Ai, parameter of voice speech dectaction, which is changed according to time. In the standard speech design section, 19 phonemes or syllables are trained by DHMM and designed as a standard speech. In the recognition section, phomeme stream are recognized by the Viterbi algorithm.In the lexical decoder section, finally recognized continuous digits are outputed. This experiment shiwed the recognition rate of 85.1% using data spoken 7 times of 21 classes of 7 continuous digits which are combinated all of the occurence, spoken by 10 man.

• 한국해안·해양공학회논문집
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• 제31권3호
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• pp.146-157
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• 2019

최근 해양예보/재해 등의 주요 분야에서 해양수치모델의 수행 및 연구결과에 대한 활용도가 증가함에 따라 정확도 높은 수심자료의 중요성이 크게 부각되고 있다. 해양수치모델에 주로 활용되는 국내 수심자료는 Choi et al.(2002), Seo(2008)의 자료가 있지만, 제작년도가 오래되고 해도를 기반으로 작성했다는 제한사항이 있다. 해도는 항해를 목적으로 제작되어 수심 측량자료 중 최천소 자료를 사용하므로 실제 해저지형을 재현하는데 한계가 있다. 국립해양조사원은 매년 지속적인 수심측량을 통하여 해도를 생산하고 있지만, 수치모델을 목적으로 한 수심자료는 생산하지 않았다. 본 연구에서는 원시 수심측량자료를 이용하여 수치모델을 위한 해양수치모델 전용 수심 데이터셋(BADA Ver.1)을 구축하고, 공개된 해양 수심자료와 비교하였다.

• Structural Engineering and Mechanics
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• 제81권6호
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• pp.751-767
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• 2022

In the hogging bending moment area, continuous composite beams are subjected to the ultimate limit state of lateral-torsional buckling (LTB), which depends on web stiffness as well as concrete slab and shear connection stiffnesses. The design of the LTB and the determination of the elastic critical moment are produced approximately, using the European Standard EN 1994-1-1:2004, for continuous composite steel beams, but is applicable only for those with a plane web steel profile. Also, and from the previous researches, the elastic critical moment of the continuous composite beams with corrugated sinusoidal web steel profiles was determined. In this paper, a finite element analysis (FEA) model was developed using the ANSYS 16 software, to determine the elastic critical moments of continuous composite steel beams with various corrugated web profiles, such as trapezoidal, zigzag, and rectangular profiles, which were evaluated against numerical data of the sinusoidal one from the literature. Ultimately, the failure load of a composite steel beam with various web profiles was predicted by studying 46 models, based on FEA modeling, and a procedure for predicting the elastic critical moment of composite beams with various web steel profiles was proposed. When compared to sinusoidal web profiles, the trapezoidal, zigzag, and rectangular web profiles required an average increase in load capacity and stiffness of 7%, 17.5%, and 28%, respectively, according to the finite element analysis. Also, the rectangular web steel profile has a greater stiffness and load capacity. In contrast, the sinusoidal web has lower values for these characteristics.

• 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).

• 한국음향학회지
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• 제19권1호
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• pp.11-15
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• 2000

RM 음성 Corpus를 이용한 화자 적응 연속 음성 인식을 수행하였다. RM Corpus의 훈련용 데이터를 이용해서 기준화자에 대한 HMM 학습을 실시하고 평가용 데이터를 이용하여 화자 적응 인식에 대한 평가를 실시하였다. 화자 적응을 위해서는 훈련용 데이터의 일부가 사용되었다. DTW를 이용하여 인식 대상화자의 데이터를 기준화자의 데이터와 시간적으로 일치시키고 오차 역전파 신경회로망을 사용하여 인식 대상화자의 스펙트럼이 기준화자의 스펙트럼 특성을 지니도록 변환시켰다. 최적의 화자 적응이 이루어지도록 하기 위해 신경회로망의 여러 요소들을 변화시키면서 실험을 실시하고 그 결과를 제시하였다. 학습을 거쳐 적절한 가중치를 지닌 신경회로망을 이용하여 기준화자에 적응시킨 결과 단어 인식율이 최대 2.1배, 단어 정인식율이 최대 4.7배 증가하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.165-168
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• 2006

Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.