• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1784-1786
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• 2004

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency. STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33${\times}$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13${\times}$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.877-879
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• 2005

배터리를 이용하는 모바일 장비에서 전력 소비를 줄이기 위한 많은 연구들이 있다. 그 중에 동적 전력 관리(Dynamic Power Management)는 시스템의 각 컴포넌트의 상태를 쉽게 관찰할 수 있다는 측면에서 운영체제에서 접근하기 적합한 전력 관리 방법이다. 본 논문에서는 대표적인 모바일 장비인 노트북에서 하드 디스크의 전력소비를 줄이기 위하여 기계 학습 기반의 동적 전력 관리 방법을 제안한다. 하드 디스크 접근 패턴을 분석하여 Artificial Neural Network(ANN) 기법으로 모형을 만들고 이 모형을 바탕으로 하드 디스크의 다음 유휴기간을 예측하였다. 예측된 유휴기간 동안 하드 디스크로의 공급 전력을 감소시키지 않았을 경우에 소비하는 비용이 전력을 줄였다 다시 늘이는 비용보다 크다면 하드 디스크로 공급되는 전력을 줄임으로써 유휴기간 동안 낭비되는 배터리 전력을 줄일 수 있었다. 본 연구에서 생성된 모형을 하드 디스크 디바이스 드라이버에 적용하면 기존의 시간 경계 값을 이용한 방법에 비해 약 23.05W의 전력 소비 감소를 기대할 수 있다.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.470-473
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• 1990

In this thesis, the recognition method of the movement of an 3D object is presented. The information about the movement of a 3D object is used to recognize the object. There are 2 kinds of movements which are translation and rotation. A difference picture is obtained from a sequence of images of a moving object or a scene which is taken by a monocular stationary observer. The 3D movement of an object is recognized by the Artificial Neural Network(ANN) using the difference picture.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.163-168
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• 2005

It is one of the most important things to determinate optimized cutting conditions which satisfy productivity and cost simultaneously in production and CAPP systems. These days many researchers have figured out the optimizing way for solutions of multi-object function to find the approach methods using algorithm such as genetic algorithm or tabu search, etc., instead of mathematical methods. The main creation of objective function is proposed by empirical method but which is difficult to set it up and to analysis. In this paper, an optimization method of cutting condition is shown using the ANN and GA for the multi-objective function in high speed machining.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.5
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• pp.29-35
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• 2002

This study shows the process design and forming analysis of permalloy shielding can that support the automobile multi-display parts to indicate the accurate information of car. This study is particularly important, since the accuracy of permalloy shielding can is known to affect the magnetic properties such as coercivity and permeability quite sensitively. The objective functions are defects such as hemming wind, hemming length, hemming wrap and tightness in prehemming process. The pre-hemming angle is considered as design parameter. The commercial finite element program PAM-STAMP™ was used to simulate the pre-hemming and hemming process. The ANN (Artificial Neural Network) has been implemented for minimizing of objective function and for investigating effect of punch angle relevant to the pre-hemming process. The results of analysis to validate the proposed design method are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.416-424
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• 2016

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.248-253
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• 2006

The purpose of this study is to develop a portable gait-event detection system which is necessary for the cycle-to-cycle FES(functional electrical stimulation) control of locomotion. To make the system portable, we made following modifications in the gait signal measurement system. That is, 1) to make the system wireless using Bluetooth communication, 2) to make the system small-sized and battery-powered by using low power consumption ${\mu}$ P(ATmega8535L). The gait-events were analyzed in off-line at the main computer using ANN(Artificial Neural Network). The Proposed system showed no mis-detection of the gait-events of normal subject and hemiplegia subjects. The performance of the system was better than the previous wired-system.