• Genomics & Informatics
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• v.8 no.1
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• pp.28-33
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• 2010

Increased exposure of human to RF fields has raised concerns for its potential adverse effects on our health. To address the biological effects of RF radiation, we used genome wide gene expression as the indicator. We exposed normal WI-38 human fibroblast cells to 1763 MHz mobile phone RF radiation at a specific absorption rate (SAR) of 60 W/kg with an operating cooling system for 24 h. There were no alterations in cell numbers or morphology after RF exposure. Through microarray analysis, we identified no differentially expressed genes (DEGs) at the 0.05 significance level after controlling for multiple testing errors with the Benjaminiochberg false discovery rate (BH FDR) method. Meanwhile, 82 genes were differentially expressed between RF-exposed cells and controls when the significance level was set at 0.01 without correction for multiple comparisons. We found that 24 genes (0.08% of the total genes examined) were changed by more than 1.5-fold on RF exposure. However, significant enrichment of any gene set or pathway was not observed from the functional annotation analysis. From these results, we did not find any evidence that non-thermal RF radiation at a 60-W/kg SAR significantly affects cell proliferation or gene expression in WI-38 cells.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1973-1980
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• 2017

In the case of a small UUV operating an active synthetic aperture sonar, various velocity disturbances may occur on the path due to the influence of external underwater environment, and this causes phase errors in coherent synthetic aperture processing, which has a large influence on the detected image. In this paper, when a periodic sinusoidal velocity disturbance is generated in the traveling direction, the phase generated by the round trip slope range at each position is estimated the cross correlation coefficient for multiple received signals and compensated the position variation in the overlapped DPC by the average value within the maximum allowable width. Through simulations, it has been confirmed that the images degraded by the velocity disturbance amplitude and fluctuating frequency of the UUV are removed from the false targets and the performance of azimuth resolution is improved by the proposed phase compensation method.

• Journal of Marine Bioscience and Biotechnology
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• v.1 no.4
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• pp.260-267
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• 2006

The Liquid Culture Assay using a recombinant Agrobacterium tumefaciens strain has been developed as a means for quorum sensing autoinducer screening. However, the low aqueous solubility of marine natural product extracts used as potential autoinducers has been a hindrance in the screening process. Although the addition of organic solvents and/or surfactants could increase aqueous solubility, errors in data interpretation including false positive results could be a serious problem. Therefore, determining the best possible solvent and surfactant at the optimum concentration is crucial. Evaluating methanol, ethanol, 1-propanol, DMSO and DMF at concentration ranges of 0~10% revealed < 2% methanol to be most favorable when tested for ${\beta}$-gal activity and growth inhibition of the recombinant A. tumefaciens strain. On the other hand, among surfactants tested, Triton X-100 was similarly effective in increasing the delivery of autoinducers for activity at less than 0.05% concentration.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.83-92
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• 2006

This paper describes an image disparity estimation method using region-based stereo matching. Region-based disparity estimation yields a disparity map as the unit of segmented region. However it estimates disparity imprecisely because it not only has matching errors but also applies an identical way to disparity estimation, which does not consider each type of matched regions. To solve this problem, we proposes a disparity estimation method which considers the type of matched regions. That is, the proposed method classifies whole matched regions into a similar-matched region, a dissimilar-matched region, a false-matched region and a miss-matched region. We then performs proper disparity estimation for each type of matched regions. This method minimizes the error in estimating disparity which is caused by inaccurate matching and also improves the accuracy of disparity of the well-matched regions. The experimental results show the improved accuracy of the proposed method.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.1978-1982
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• 2006

The EPIRB overcame the limitations of the conventional marine communication systems, the false distress calls by EPIRB systems internationally account for about 94 percent of the total calls because of the different usages of EPIRB systems with manufacturers, users' errors, and systems' faults. To resolve these problems, international bodies and manufacturers are developing many measures to reduce those error emissions. In conventional systems, the distress call was sent immediately after the EPIRB is removed from the automatic release system. Taking into account the properties of the system, however manufacturers improved the operation so that the distress call is sent only when the EPIRB is released and then immersed into water. In spite of these efforts, the error emissions have not significantly reduced. In this study, the domestic and international technical regulations and standards for the COSPAS-SARSAT and satellite EPIRB systems were reviewed, and a bridge-type water detection sensor was developed to minimize the error emission from EPIRB.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.396-403
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• 2019

Metro stations are public places used by many people. If they use the station frequently, there is no struggling to find a way. But the first tourists or foreigners to visit are facing finding difficulties because of errors of the complicated sign and information. This study is the guidance of the metro station sign to provide effective information system and the research on the design improvement of metro station. In the first survey for this study I proceeded research about the concept of sign systems in metro station and literature search. In the second survey, I researched needs of the problems of the metro station users. In the third survey, I investigated Busan Metro Station's problems and design to improve the guide sign. In the survey results, users had a lot of difficulties in finding information because systematically increasing amount of information and guidance of the metro station. People can't find information because of sign indiscreet commercial advertising. Also they can get false information from sign without unity. To deal with these problems the metro station needs to improve design for a second-class citizen and refrain commercial advertising and design for information transfer capability.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.685-701
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• 2022

FDR is widely used in high-dimensional data inference since it provides more liberal criterion contrary to FWER which is known to be very conservative by controlling Type-1 errors. This paper proposes a sparse VHAR model estimation method controlling FDR by adapting the knockoff introduced by Barber and Candès (2015). We also compare knockoff with conventional method using adaptive Lasso (AL) through extensive simulation study. We observe that AL shows sparsistency and decent forecasting performance, however, AL is not satisfactory in controlling FDR. To be more specific, AL tends to estimate zero coefficients as non-zero coefficients. On the other hand, knockoff controls FDR sufficiently well under desired level, but it finds too sparse model when the sample size is small. However, the knockoff is dramatically improved as sample size increases and the model is getting sparser.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.