• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.61-71
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• 2005

We evaluated the availability of Origin-Destination Matrix from traffic counts Using conjugate gradient method to large scale networks by applying it to the networks in 246 zones. As a result of the analysis of the consistency of the model on Nationwide Networks, the upper and lower levels in model had the systematic relationship internally. From the analysis of the estimable power or the model according to the number of traffic counting links, the error in traffic volume had the estimable power in the range of permissible error. In addition, the estimable power of estimation of an Origin-Destination Matrix was more satisfactory than that of existing methods. We conclude that conjugate gradient method cab be applied to nationwide networks if we can make sure that the algorithm of the developed model is reliable by doing various kinds of experiment.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.17-24
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• 2014

Blind algorithms designed to maximize the probability that constant modulus errors become zero carry out some summation operations for a set of constant modulus errors at an iteration time inducing heavy complexity. For the purpose of reducing this computational burden induced from the summation, a new approach to the estimation of the zero-error probability (ZEP) of constant modulus errors (CME) and its gradient is proposed in this paper. The ZEP of CME at the next iteration time is shown to be calculated recursively based on the currently calculated ZEP of CME. It also is shown that the gradient for the weight update of the algorithm can be obtained by differentiating the ZEP of CME estimated recursively. From the simulation results that the proposed estimation method of ZEP-CME and its gradient produces exactly the same estimation results with a significantly reduced computational complexity as the block-processing method does.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.147-153
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• 2007

The conjugate gradient (CG) method is one of the most efficient algorithms for solving a linear system of equations. In addition to being used as a linear equation solver, it can be applied to a least-squares problem. When the CG method is applied to large-scale three-dimensional inversion of magnetotelluric data, two approaches have been pursued; one is the linear CG inversion in which each step of the Gauss-Newton iteration is incompletely solved using a truncated CG technique, and the other is referred to as the nonlinear CG inversion in which CG is directly applied to the minimization of objective functional for a nonlinear inverse problem. In each procedure we only need to compute the effect of the sensitivity matrix or its transpose multiplying an arbitrary vector, significantly reducing the computational requirements needed to do large-scale inversion.

• Journal of the Korean Magnetics Society
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• v.27 no.2
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• pp.63-69
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• 2017

Naval ships are particularly required to maintain acoustic and magnetic silence due to their operational characteristics. Among them, underwater magnetic field signals derived by ships are likely to be detected by threats such as surveillance systems and mine systems at close distance. In order to increase the survivability of the vessels, various techniques for reducing the magnetic field signal are being studied and it is necessary to consider not only the magnitude of the magnetic field signal but also the gradient of it. In this paper, we use the commercial electromagnetic finite element analysis tool to predict the induced magnetic field signal of ship's scaled model, and arrange the degaussing coil. And the optimum degaussing current of the coil was derived by applying the particle swarm optimization algorithm considering the gradient constraint. The validity of the optimal degaussing technique is verified analytically by comparing the magnetic field signals after the degaussing with or without gradient constraint.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.231-237
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• 2024

Brain injury causes persistent disability in survivors, and epidural hematoma(EDH) and subdural hematoma (SDH) resulting from cerebral hemorrhage can be considered one of the major clinical diseases. In this study, we attempted to automatically segment and hematomas due to cerebral hemorrhage in three dimensions based on computed tomography(CT) images. An improved GVF(gradient vector flow) algorithm was implemented for automatic segmentation of hematoma. After calculating and repeating the gradient vector from the image, automatic segmentation was performed and a 3D model was created using the segmentation coordinates. As a result of the experiment, accurate segmentation of the boundaries of the hematoma was successful. The results were found to be good even in border areas and thin hematoma areas, and the intensity, direction of spread, and area of the hematoma could be known in various directions through the 3D model. It is believed that the planar information and 3D model of the cerebral hemorrhage area developed in this study can be used as auxiliary diagnostic data for medical staff.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1047-1057
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• 2024

Recently, deep neural network-based reinforcement learning models have been applied in various advanced industrial fields such as autonomous driving, smart factories, and home networks, but it has been shown to be vulnerable to malicious adversarial attack. In this paper, we applied deep reinforcement learning models, DQN and PPO, to the autonomous driving simulation environment HighwayEnv and conducted three adversarial attacks: FGSM(Fast Gradient Sign Method), BIM(Basic Iterative Method), PGD(Projected Gradient Descent) and CW(Carlini and Wagner). In order to respond to adversarial attack, we proposed a method for deep learning models based on reinforcement learning to operate normally by removing noise from adversarial images using a bilateral filter algorithm. Furthermore, we analyzed performance of adversarial attacks using two popular metrics such as average of episode duration and the average of the reward obtained by the agent. In our experiments on a model that removes noise of adversarial images using a bilateral filter, we confirmed that the performance is maintained as good as when no adversarial attack was performed.