• Smart Structures and Systems
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• v.13 no.1
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• pp.81-98
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• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

• JSTS:Journal of Semiconductor Technology and Science
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• v.7 no.2
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• pp.76-81
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• 2007

Threshold voltage ($V_{th}$) modeling of doublegate (DG) MOSFETs was performed, for the first time, by considering barrier lowering in the short channel devices. As the gate length of DG MOSFETs scales down, the overlapped charge-sharing length ($x_h$) in the channel which is related to the barrier lowering becomes very important. A fitting parameter ${\delta}_w$ was introduced semi-empirically with the fin body width and body doping concentration for higher accuracy. The $V_{th}$ model predicted well the $V_{th}$ behavior with fin body thickness, body doping concentration, and gate length. Our compact model makes an accurate $V_{th}$ prediction of DG devices with the gate length up to 20-nm.

• Steel and Composite Structures
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• v.29 no.6
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• pp.749-758
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• 2018

This article derived a hybrid coupling technique using the higher-order displacement polynomial and three soft computing techniques (teaching learning-based optimization, particle swarm optimization, and artificial bee colony) to predict the optimal stacking sequence of the layered structure and the corresponding frequency values. The higher-order displacement kinematics is adopted for the mathematical model derivation considering the necessary stress and stain continuity and the elimination of shear correction factor. A nine noded isoparametric Lagrangian element (eighty-one degrees of freedom at each node) is engaged for the discretisation and the desired model equation derived via the classical Hamilton's principle. Subsequently, three soft computing techniques are employed to predict the maximum natural frequency values corresponding to their optimum layer sequences via a suitable home-made computer code. The finite element convergence rate including the optimal solution stability is established through the iterative solutions. Further, the predicted optimal stacking sequence including the accuracy of the frequency values are verified with adequate comparison studies. Lastly, the derived hybrid models are explored further to by solving different numerical examples for the combined structural parameters (length to width ratio, length to thickness ratio and orthotropicity on frequency and layer-sequence) and the implicit behavior discuss in details.

• Journal of the Korean GEO-environmental Society
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• v.4 no.4
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• pp.17-23
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• 2003

Accurate prediction of consolidation behaviors of the soft ground improved by plastic board drains is not easy because the consolidation characteristics of the improved ground has not been fully elucidated yet. The shape of drains is one of the most important factors which affect the consolidation characteristics of the improved ground. In this paper, a series of model consolidation tests of soft clay ground improved by plastic board drain were carried out, in order to investigate the effect of both plastic board width and stress level on consolidation characteristics of the improved ground. As the results, behaviors of both settlement and excess pore pressure dissipation were elucidated. Also, the non-uniform distribution of water content in the model ground was obtained. Then, in order to investigate the effect of vertical drainage on the consolidation behavior in the model tests, the comparison between experimental consolidation behaviors and Barron's theoretical ones were carried out. As the results, it was elucidated that the consolidation behavior in the model tests was affected not only by radial drainage but also by vertical drainage.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.911-918
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• 2024

This study developed and evaluated the performance of a machine learning-based model for predicting cucumber fruit set using cucumber growth data. In this study, plant height, node number, internode length, stem thickness, leaf length, leaf width, leaf count, and female flower count were used as independent variables, and the fruit set was set as the dependent variable to develop a prediction model. Various machine learning algorithms, including Linear Regression, Random Forest, XGBoost, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), were applied, and model performance was evaluated based on Mean Squared Error (MSE) and the coefficient of determination (R²). As a result, the Random Forest algorithm demonstrated the best performance, with an MSE of 3.91 and an R² of 0.828, effectively capturing the non-linear relationships in the cucumber growth data. In particular, the Random Forest model showed robustness against outliers and proved to be highly effective in predicting fruit set.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.485-493
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• 2019

The parametric array phenomenon refers to the generation of a high directivity low frequency wave from a small size radiation plate using the nonlinearity of the medium. In order to improve the usability of parametric array, the beam steering method of low frequency wave is researched, and the beam steering phenomenon is predicted easily using the PD (product directivity) model. However, the PD model can only be applied to Gaussian sources under quasi-linear conditions. Also, the prediction accuracy of low frequency wave beam width is poor. In this paper, a method for predicting the beam steering characteristics of a parametric array that can overcome the limitation of the PD model is investigated. For this purpose, the numerical analysis algorithm of the KZK (Khokhlov-Zabolotskaya-Kuzentsov) equation widely used for parametric array phenomenon prediction is improved. Thus, the beam steering characteristics are calculated by applying the electrical beam steering condition and comparing experimental results. As a result, the numerical analysis using the modified KZK equation algorithm in this study confirms that the beam steering phenomenon can be predicted even in a parametric array source that does not correspond to the quasi-linear condition.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.7
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• pp.821-827
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• 2014

In this study, an experimental study was performed to predict the two-phase frictional pressure drop of FC-72 in parallel micro-channels. The parallel micro-channels consist of 15 channels with depth 0.2 mm, width 0.45 mm and length 60 mm. And tests were performed in the ranges of mass fluxes from 152.2 to $584.2kg/m^2s$ and heat fluxes from 7.5 to $28.3kW/m^2$. The experimental data was compared and analyzed with existing correlations to predict the pressure drop. The existing methods to predict the pressure drop used the homogeneous model and the separated model. In this study, the new correlation was proposed by modified existing correlation using the separated model, and the new correlation predicted consequently with the experimental data within MAE of 9.6%.

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.315-330
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• 2017

Halo coronal mass ejections (CMEs) originating from solar activities give rise to geomagnetic storms when they reach the Earth. Variations in the geomagnetic field during a geomagnetic storm can damage satellites, communication systems, electrical power grids, and power systems, and induce currents. Therefore, automated techniques for detecting and analyzing halo CMEs have been eliciting increasing attention for the monitoring and prediction of the space weather environment. In this study, we developed an algorithm to sense and detect halo CMEs using large angle and spectrometric coronagraph (LASCO) C3 coronagraph images from the solar and heliospheric observatory (SOHO) satellite. In addition, we developed an image processing technique to derive the morphological and dynamical characteristics of halo CMEs, namely, the source location, width, actual CME speed, and arrival time at a 21.5 solar radius. The proposed halo CME automatic analysis model was validated using a model of the past three halo CME events. As a result, a solar event that occurred at 03:38 UT on Mar. 23, 2014 was predicted to arrive at Earth at 23:00 UT on Mar. 25, whereas the actual arrival time was at 04:30 UT on Mar. 26, which is a difference of 5 hr and 30 min. In addition, a solar event that occurred at 12:55 UT on Apr. 18, 2014 was estimated to arrive at Earth at 16:00 UT on Apr. 20, which is 4 hr ahead of the actual arrival time of 20:00 UT on the same day. However, the estimation error was reduced significantly compared to the ENLIL model. As a further study, the model will be applied to many more events for validation and testing, and after such tests are completed, on-line service will be provided at the Korean Space Weather Center to detect halo CMEs and derive the model parameters.

• Journal of environmental and Sanitary engineering
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• v.3 no.2 s.5
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• pp.43-60
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• 1988

This study was carried out to determine traffic noise level and analyze noise reduction effects of various sound protection facilities in the area of Seoul, Inch'on, Songchoo and Seoul- Busan Expressway from March to Octover, 1987. The results were as follows; 1. As compared with the environmental standards and the traffic noise level in heavy noise areas, traffic noise levels observed were shown in higher than environmental standards. The noise levels in Seoul were determined at 12.8-18.2 dB(A) in daytime and 19.0-26.9 dB (A) in nighttime. And incase of inch'on, it were 6.7-9.6 dB(A) in daytime, 7.9-18.9 dB(A) in nighttime, respectively. 2. The environmental noise level observed in the backside of protection facilities, such as apartment, soundproof barrier and houses, which were constructed in paralled to the road was lower about 3-5 dB(A) than perpendicular to theroad. Noise recuction effect of upper stairs in apartment was higher than lower stairs. 3. The predicted noise level obtained from the equation $({\triangle}L\;=\; -10\;log\;(^{I'1}/Ii)\;was\;\pm\;1dB$ (A) and the correlation coefficient (r) was 0.923. 4. The noise reduction effect in backside of apartment was measured at on sites and predicted by total noise loss equation. The predicted noise level was 60.9 dB(A) and the measured level was 60.6 dB(A), respectively. 5. The narrow width landscape less than 10m width was almost no effect for the protection of traffic noise. According to the synthesis of the above results, the noise level of the road was exceeding mostly the environmental standard in the heavy traffic areas. The counterplan should be set as well. The insulation of noise protection facilities were effective by the location with near distance from the road edge. The reduction effect of double window in apartment was represented so much. The prediction model could be applied to estimate the noise levels in the roadside as well as the effectiveness for the noise protection facilities.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.