• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.657-663
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• 2018

In this study, shape optimization was performed to improve the vibration isolation capability of an anti-vibration rubber assembly, which is used in the field option cabin of agricultural tractors. A uniaxial tension test and biaxial tension test were performed to characterize the hyper-elastic material properties of rubber, and the data were used to calibrate the material model used in the finite element analyses. A field test was performed to quantify the input excitation from the tractor and the output response at the cabin frame. To account for the nonlinear behavior of rubber, static analyses were performed and the load-displacement curve of rubber was derived. The stiffness of the rubber was calculated from this curve and input to the harmonic analyses of the cabin. The results were verified using the test data. Taguchi's parameter design method was used to find the optimal shape of the anti-vibration rubber assembly, which indicated a shape with reduced stiffness. The vibration of the cabin frame was reduced by the optimization by as much as 35% compared to the initial design.

• Journal of KIISE
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• v.44 no.7
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• pp.668-673
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• 2017

Deep neural networks have shown remarkable performance in various fields of pattern recognition such as voice recognition, image recognition and object detection. However, underlying mechanisms of the network have not been fully revealed. In this paper, we focused on empirical analysis of the network parameters. The Faster R-CNN(region-based convolutional neural network) was used as a baseline network of our work and three important parameters were analyzed: the dropout ratio which prevents the overfitting of the neural network, the size of the anchor boxes and the activation function. We also compared the performance of dropout and batch normalization. The network performed favorably when the dropout ratio was 0.3 and the size of the anchor box had not shown notable relation to the performance of the network. The result showed that batch normalization can't entirely substitute the dropout method. The used leaky ReLU(rectified linear unit) with a negative domain slope of 0.02 showed comparably good performance.

• Journal of Korean Society on Water Environment
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• v.36 no.1
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• pp.48-54
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• 2020

This paper presents a screening protocol for the selection of solvents available for the solvent extraction desalination process. The desalination solvents hypothetically and theoretically require the capability of (1) Forming hydrogen bonds with water, (2) Absorbing some water molecules into its non-polar solvent layer, (3) Changing solubility for water-solvent separation, and (4) Rejecting salt ions during absorption. Similar to carboxylic acids, amine solvents are solvent chemicals applicable for desalination. The key parameter for selecting the potential solvent was the octanol-water partitioning coefficient (Kow) of which preferable value for desalination was in the range of 1-3. Six of the 30 amine solvents can absorb water and have a variable, i.e., temperature swing solubility with water molecule for water-solvent separation. Also, the hydrogen bonding interaction between solvent and water must be stronger than the ion-dipole interaction between water and salt, which means that the salt ions must be broken from the water and only water molecules absorbed for the desalination. In the final step, three solvents were selected as desalination solvents to remove salt ions and recover water. The water recovery of these three solvents were 15.4 %, 2.8 %, 10.5 %, and salt rejection were 76 %, 98 %, 95 %, respectively. This study suggests a new screening protocol comprising the theoretical and experimental approaches for the selection of solvents for the desalination method which is a new and challenges the desalination process in the future.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.38 no.4
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• pp.307-316
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• 2002

In order to evaluate the deformation and stiffness of frame structure for fishery, composed of unit platforms which made of two concentric high density polyethylene buoys fixed by clamps and belts and rubber hinge components, under wave, the structural analysis for the square type of the structure was carried out by using finite element methods. The accurate physical properties of rubber hinge components determined by material tests were an important parameter to evaluate more reliable structural stability for the structure. The idealization to beam element with equivalent stiffness and rubber element with linearity for rubber hinges was necessary for the modeling of rubber component which has hyper-elastic characteristics. In addition, it was shown that the structural response of the structure under wave was larger in the hogging condition than that of in the still water or in the sagging condition.

• Convergence Security Journal
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• v.19 no.1
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• pp.3-10
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• 2019

The 5G network is a next-generation converged network that combines various ICT technologies to realize the need for high speed, hyper connection and ultra low delay, and various efforts have been made to address the security vulnerabilities of the previous generation mobile networks. However, the standards released so far still have potential security vulnerabilities, such as USIM deception and replication attack, message re-transmission attack, and race-condition attack. In order to solve these security problems, this paper proposes a new 5G-AKA protocol with PUF technology, which is a physical unclonable function. The proposed PUF-based 5G-AKA improves the security vulnerabilities identified so far using the device-specific response for a specific challenge and hash function. This approach enables a strong white-list policy through the addition of inexpensive PUF circuits when utilizing 5G networks in areas where security is critical. In addition, since additional cryptographic algorithms are not applied to existing protocols, there is relatively little burden on increasing computational costs or increasing authentication parameter storage.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.27-33
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• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.617-628
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• 2021

Drought is a major natural disaster that causes serious social and economic losses. Local drought forecasts can provide important information for drought preparedness. In this study, we propose a new machine learning model that predicts drought by using historical drought indices and meteorological data from 10 sites from 1981 to 2020 in the southeastern part of the Korean Peninsula, Busan-Ulsan-Gyeongnam. Using Bayesian optimization techniques, a hyper-parameter-tuned Random Forest, XGBoost, and Light GBM model were constructed to predict the evaporative demand drought index on a 6-month time scale after 1-month. The model performance was compared by constructing a single site model and a regional model, respectively. In addition, the possibility of improving the model performance was examined by constructing a fine-tuned model using data from a individual site based on the regional model.