• Journal of the Korean Society for Railway
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• v.15 no.6
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• pp.631-637
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• 2012

New Opening of Suseo-Pyeongteak High Speed Railway (HSR) will be a new leap in the Korean railway history. However if this section of HSR line around Seoul Metropolitan Area opens, the confluence of new HSR and existing HSR line in Pyeongteak-Osong section will cause a bottle neck problem. In other words, the opening of Suseo-Pyeongteak HSR line will make the capacity of track reach the limit and the section of railroad between Pyeongteak and Osong will be saturated. This will also make such troubles as restricting the number of train which stops at Cheonan-Asan station. In this study, based on the train assignment theory of TVM430 signal system, the methods of calculating headway and number of train are reviewed and the plan for application of optimal operation pattern during peak hour between Pyeongteak-Osong section is also suggested. To remove the bottle neck problem in this HSR section, 3 alternatives are suggested and the expected effects and problems of each alternative are also analyzed. The results show that the troubles caused by excess of track capacity can be removed without any additional cost if the minimum headway in operating system for HSR is adopted in this section. In the future, if these alternatives are considered to the long-term plan for operating train and signal systems, this will improve the efficiency of train operation, which can remove the bottle neck in the HSR line.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2393-2399
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• 2013

In this paper, we proposed a method to screening of Alzheimer's disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.809-818
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• 2019

Since January 1, 2020, the International Maritime Organization (IMO) has put in place strong regulations to reduce air pollution caused by ships by lowing the upper limit of ship fuel oil sulfur content from 3.5% to 0.5% for ships passing through all sea areas around the world. Although it is important to reduce air pollutants by using fuel oil with low sulfur content, reducing the amount of energy waste through the economic operation of a ship can also help reduce air pollutants. Ships can follow designated routes accurately even under the influence of noise using autopilot systems. However, regardless of their quality, the performance of these systems is af ected by noise; heading angles with added measurement noise from the gyroscope are input into the autopilot system and degrade its performance. A technique to solve these problems reduces noise effects through the application of a Kalman filter, which is widely used in condition estimation. This method, however, cannot completely eliminate the effects of noise. Therefore, to further improve noise removal performances, in this study we propose a better denoising method than the Kalman filter technique by applying a multi-layer perceptron (MLP) in forward direction motion and a Kalman Filter in rotation motion. Simulations show that the proposed method improves forward direction motion by preventing the malfunction of a rudder more so than merely using a Kalman Filter.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1249-1263
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• 2013

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.94-101
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• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.391-398
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• 2001

To design and develop secure operating systems, the BLP (Bell-La Padula) model that represents the MLP (Multi-Level Policy) has been widely adopted. However, user\`s security level in the most developed systems based on the BLP model is inherited to a process that is actual subject on behalf of the user, regardless whatever the process behavior is. So, there could be information disclosure threat or modification threat by malicious or unreliable processes even though the user is authorized in the system. These problems can be solved by defining the subject as (user, process) ordered pair and by defining the process reliability. Moreover, when the leveled programs which exist as objects in a disk are executed by a process and have different level from the process level, the security level decision problem occurs. This paper presents an extended BLP (E-BLP) model in which process reliability is considered and solves the security level decision problem. And this model is implemented into the Linux kernel 2.4.7.

• Journal of Life Science
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• v.13 no.4
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• pp.529-534
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• 2003

This study was done to investigated the phytosterol compositions of safflower (Carthamus tinctorius L.) seed. The phytoestrogen activity was also determined using CAT-ELISA Kit in ethanol extract of safflower seed. The phytosterol of safflower seeds was identified using gas chromatography-mass spectrometry after saponification of the oils. The phytosterol content and composition of safflower seed oils were 4% and identified stigmast-5-en-3-ol (3$\beta$, 24S)-form, ${\gamma}$-sitosterol (clionasterol) with Wiley MS spectrum library. The synergistic effect of human estrogen receptor (hER) has been investigated using a minimal chimeric promoters composed of the TATA region of the adenovirus-2 major late promoter (A22MLP) and two consensus perfectly polindromic Xenopus vitellogenin A2 gene estrogen responsive elements (XVEREl19). Transient transfection experiments in tile human breast adenocarcinoma cell line MCF-7, which is known to express the estrogen receptor endogenously, revealed that phytoestrogen from Carthamus tinctorius L. acts as estrogen. We have observed the transcriptional activities stimulated methanol and ethanol extract of safflower seed in MCF-7, were 0.43 and 0.37 respectively, compared to that by $\beta$-estradiol as 1.0. Our data showed that safflower seeds have estrogenic activity methanol and ethanol extracts and ethanol lower than that of $\beta$-estradiol. This result provides the first evidence that the beneficial effect of safflower seeds may be mediated, at least in part, by the stimulating effect of phytoestrogen ell bone-protecting.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.