• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.9-26
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• 2005

In the pursuit of ever higher levels of performance, recent computer systems have made use of deep pipeline, dynamic scheduling and multi-issue superscalar processor technologies. In this situations, branch prediction schemes are an essential part of modem microarchitectures because the penalty for a branch misprediction increases as pipelines deepen and the number of instructions issued per cycle increases. In this paper, we propose a novel branch prediction scheme, direction-gshare(d-gshare), to improve the prediction accuracy. At first, we model a neural network with the components that possibly affect the branch prediction accuracy, and analyze the variation of their weights based on the neural network information. Then, we newly add the component that has a high weight value to an original gshare scheme. We simulate our branch prediction scheme using Simple Scalar, a powerful event-driven simulator, and analyze the simulation results. Our results show that, compared to bimodal, two-level adaptive and gshare predictor, direction-gshare predictor(d-gshare. 3) outperforms, without additional hardware costs, by up to 4.1% and 1.5% in average for the default mont of embedded direction, and 11.8% in maximum and 3.7% in average for the optimal one.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.259-272
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• 2022

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• IE interfaces
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• v.18 no.3
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• pp.268-276
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• 2005

In recent years, the money Laundering crimes are increasing by means of foreign exchange transactions. Our study proposes four scoring models to provide early warning of the laundering in foreign exchange transactions for both inward and outward remittances: logistic regression model, decision tree, neural network, and ensemble model which combines the three models. In terms of accuracy of test data, decision tree model is selected for the inward remittance and an ensemble model for the outward remittance. From our study results, the accumulated number of transaction turns out to be the most important predictor variable. The proposed scoring models deal with the transaction level and is expected to help the bank teller to detect the laundering related transactions in the early stage.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.215-220
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• 2002

본 연구에서는 다중채널 뇌파에서 특징 파라미터로 선형 예측기 계수(Linear predictor coefficients)를 추출하고, 패턴인식기로는 신경회로망을 이용한 쾌적성 분류 알고리즘을 개발하여 다중 템플릿 방법으로 쾌적성 분류 실험을 하고자 하였다. 뇌파 데이터는 대학생 10명으로부터 쾌적한 환경과 불쾌적한 환경에서의 데이터를 수집하였으며, 전극 위치는 Fpl, Fp2, F3, F4, T3, T4, P3, P4, O1, O2를 사용하였다. 수집된 뇌파는 전처리를 거친 후 특징 파라미터를 추출하고 패턴 분류기로 사용된 신경회로망의 입력으로 사용하였다. 쾌적성 분류 방법은 다중템플릿 방법으로 여러 명의 피검자를 각각 학습시켜 이로부터 생성되는 신경회로망의 가중치들을 템플릿에 저장한다. 그리고 테스트를 할 때에는 먼저 처음의 안정 상태의 뇌파를 이용하여 템플릿 검색을 하고 가장 가까운 템플릿을 선택한다. 그리고 선택된 템플릿을 이용하여 다른 감정에 대한 쾌적성 분류 실험을 하게 된다. 쾌적성 분류 실험 결과 평균 인식률이 약 75%의 성능을 나타내었다.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2690-2692
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• 2002

This paper describes a technique for human sensibility evaluation using 10-channel EEG(electroencephalogram). The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as, relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance using the proposed method is about 72.6%. This will be promising performance in the human sensibility evaluation.