• Geomechanics and Engineering
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• v.29 no.2
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• pp.171-182
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• 2022

Reusing the used pile has not yet been implemented due to the unpredictability of the bearing capacity evolution. This paper presents an analytic approach to estimate the sides shear setup after the dissipation of pore pressure. Long-term evolution of adjacent soil is simulated by viscoelastic-plastic constitutive model. Then, an innovative concept of quasi-overconsolidation is proposed to estimate the strength changes of surrounding soil. Total stress method (α method) is employed to evaluate the long term bearing capacity. Measured data of test piles in Louisiana and semi-logarithmic time function are cited to validate the effectiveness of the presented method. Comparisons illustrate that the presented approach gives a reasonably prediction of the side shear setup. Both the presented method and experiment show the shaft resistance increase by 30%-50%, and this highlight the potential benefit of piles reutilization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.339-341
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• 2024

본 논문에서는 전기 자동차 배터리 팩 설계에서 성능 예측을 위해 전산유체해석 및 Long Short-Term Memory (LSTM)를 활용한다. 두 계산 모두의 예측이 상당한 유사성을 나타내며, 전산유체해석은 시스템 유체 역학을 고려한 상세한 물리 모델을 제공하고, LSTM은 시계열 데이터를 기반으로 한 딥러닝 모델로 효과적으로 패턴을 파악, 향후 온도 상승을 예측한다. 결과는 두 접근 모두가 효과적인 예측을 제공하며 향후 전기 자동차 배터리 팩 설계 및 최적화에서 종합적인 접근의 필요성을 강조한다. 특히, LSTM 기반 예측에 소요되는 시간은 계산 유체 역학의 약 25%로, 약 일주일 정도로 빠르게 확인 가능하다. 이는 현대 산업 환경에서 시간적 효율성이 중요한 측면을 강조하며, 계산 유체 역학의 상세한 물리 모델링과 LSTM의 빠른 예측 속도를 결합한 설계 방법론을 제안한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.50-53
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• 2017

Not only growth of importance to understanding economic trends, but also the prediction to overcome the uncertainty is coming up for long-term maritime recession. This paper discussed about the prediction of BDI with artificial neural networks (ANN). ANN is one of emerging applications that can be the finest solution to the knotty problems that may not easy to achieve by humankind. Proposed a prediction by implementing neural networks that have recurrent architecture which are a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). And for the reason of comparison, trained Multi Layer Perceptron (MLP) from 2009.04.01 to 2017.07.31. Also made a comparison with conventional statistics, prediction tools; ARIMA. As a result, recurrent net, especially RNN outperformed and also could discover the applicability of LSTM to specific time-series (BDI).

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.313-319
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• 2023

In this paper, a time series machine learning model, Long Short Term Memory (LSTM), is applied into the bubble flow noise data and the underwater projectile launch noise data to predict missing values of time-series underwater noise data. The former is mixed with bubble noise, flow noise, and fluid-induced interaction noise measured in a pipe and can be classified into three types. The latter is the noise generated when an underwater projectile is ejected from a launch tube and has a characteristic of instantaenous noise. For such types of noise, a data-driven model can be more useful than an analytical model. We constructed an LSTM model with given data and evaluated the model's performance based on the number of hidden units, the number of input sequences, and the decimation factor of signal. It is shown that the optimal LSTM model works well for new data of the same type.

• Proceedings of the Korean Society of Toxicology Conference
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• 2006.05a
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• pp.45-46
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• 2006

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05b
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• pp.721-726
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• 2001

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.11-20
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• 2023

The cost of medical treatment for motor vehicle accidents is increasing every year. In this study, we created a model to predict long-term hospitalization(more than 18 days) among minor patients, which is the main item of increasing traffic accident medical expenses, using five algorithms such as decision tree, and analyzed the factors affecting long-term hospitalization. As a result, the accuracy of the prediction models ranged from 91.377 to 91.451, and there was no significant difference between each model, but the random forest and XGBoost models had the highest accuracy of 91.451. There were significant differences between models in the importance of explanatory variables, such as hospital location, name of disease, and type of hospital, between the long-stay and non-long-stay groups. Model validation was tested by comparing the average accuracy of each model cross-validated(10 times) on the training data with the accuracy of the validation data. To test of the explanatory variables, the chi-square test was used for categorical variables.

• Journal of the Korean Professional Engineers Association
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• v.18 no.2
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• pp.1-5
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• 1985

In the flood prediction research, it is pointed out that the difficulty of flood prediction is the frequently experienced overestimation of flood peak. That is caused by the rainfall prediction difficulty and the nonlinearity of hydrological phenomena. Even though the former reason will remain still unsolved, but the latter one can be possibly resolved the method of the AMRA (Auto Regressive Moving Average) model for each runoff component as developed by Dr. Hino and Dr. Hasebe. The principle of the method consists of separating though the numerical filters the total runoff time series into long-term, intermediate and short-term components, or ground water flow, interflow, and surface flow components. As a total system, a hydrological system is a non-linear one. However, once it is separated into two or three subsystems, each subsystem may be treated as a linear system. Also the rainfall components into each subsystem a estimated inversely from the runoff component which is separated from the observed flood. That is why flood prediction can be done without rainfall data. In the prediction of surface flow, the Kalman filter will be applicable but this paper shows only impulse function method.

• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• The Journal of the Acoustical Society of Korea
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• v.14 no.4
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• pp.38-45
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• 1995

In this paper a way to improve the performance of the long term prediction is proposed, which adopts the Multi-band Excitation (MBE) method in addition to the Code-Excited Linear Prediction (CELP) method at low bit rates below 4.8 kbps. In the proposed method, the multiband long term prediction is performed on the periodic components which still remain after the long term prediction of the conventional CELP method. At this point, the whole frequency region is divided into subbands whose size is equal to the spacing between the harmonics of the fundamental frequency, and the periodic multiband excitation signals. are represented as the sum of sine waves approximately as large as the spectrum of the excitation signals, so that the actual characteristics of the excitation signals can be better taken into account. To evaluate the performance of the proposed method, computer simulation is performed at 4.8 kbps. The 4.8 kbps DoD CELP and the 4.4 kbps IMBE were chosen as the reference vocoders for the speech quality measure. The result of the perceptual speech quality measure showed that the performance of the proposed method is better than that of the 4.8 kbps DoD CELP vocoder, and similar to that of the 4.4 kbps IMBE vocoder.