• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.39-49
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• 2002

In this study, limits of detection (LOD), accuracy and precision of four sampling/ analytical methods were evaluated and compared for the determination of airborne hexavalent chromium, Cr (VI). The methods include : (1) a combination of the National Institute for Occupational Safety and Health (NIOSH) Method 7600/U. S. Environmental Protection Agency (EPA) Method 218.6 (NIOSH/EPA Method) proposed by Shin and Paik, 2) two impinger methods using 2% NaOH/3% Na$_2$CO$_3$. (3) same as (2) but with 0.02 N NaHCO$_3$absorbing solution, and (4) the Occupational Safety and Health (OSHA) Method ID-215. An ion chromatograph/visible absorbance detector was used for the analysis of Cr (VI) in sample solution. Limit of detection (LOD) , analytical accuracy, and precision were also tested using Cr (VI) spike samples. Recoveries (as index of accuracy) and coefficient of variation (CV) (as a index of precision) were determined. Two-way ANOVA and Turkey's test were performed to test the significance in differences among recoveries and CVs of the methods. In all the methods, the peaks of Cr (VI) were separated sharply on chromatograms and exhibited a strong linearity with Cr (VI) concentrations in solution. The correlation coefficients of calibration curves typically ranged from 0.9997 to 0.9999, and the analytical LODs from 0.025 to 0.1$\mu\textrm{g}$/sample. All the method had good sensitivities and linearities between Cr (VI) levels and peak areas. The accuracies (% mean recoveries) of the methods ranged from 80.1 to 104.2%, while the precisions (pooled coefficient of variation) ranged from 3.16 to 4.43%. The impinger methods showed higher recoveries ( > 95%) than those of the PVC filter methods (the OSHA Method and the NIOSH/EPA Method). It was assumed that Cr (VI) on PVC filter was exposed to air and reduced to trivalent chromium, Cr (III), whereas it was stabilized in alkali solution contained in impinger. Thus, a special treatment of Cr (VI) samples collected on PVC filters may be required.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.61-69
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• 2015

This study compares the forecasting accuracy of five econometric models on domestic total freight volume in South Korea. Applied five models are as follows: Ordinary Least Square model, Partial Adjustment model, Reduced Autoregressive Distributed Lag model, Vector Autoregressive model, Time Varying Parameter model. Estimating models and forecasting are carried out based on annual data of domestic freight volume and an index of industrial production during 1970~2011. 1-year, 3-year, and 5-year ahead forecasting performance of five models was compared using the recursive forecasting method. Additionally, two forecasting periods were set to compare forecasting accuracy according to the size of future volatility. As a result, the Time Varying Parameter model showed the best accuracy for forecasting periods having fluctuations, whereas the Vector Autoregressive model showed better performance for forecasting periods with gradual changes.

• Journal of Environmental Science International
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• v.25 no.9
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• pp.1241-1251
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• 2016

A minimum threshold for the signal to noise ratio ($SNR_{min}$) has to be set in the data processing system of wind profiler radar (WPR). The data collection rate and the accuracy of the WPR wind vector depend on the $SNR_{min}$. The WPR at Uljin is operated with an $SNR_{min}$ of 1 dB which is a relatively large threshold. We found that the accuracy and the continuity of the WPR wind vector with height were directly related to the variability of the SNR and vertical gradient of the squared refractive index. We investigated a quantitative method for determining a new $SNR_{min}$ for the WPR at Uljin and it was evaluated with radiosonde data. The accuracy and continuity of the wind vector from an SNR of less than 1 dB, began to decrease at an altitude of 3.5 km. Most of the SNR values were less than -3.5 dB in altitudes higher than 3.5 km. We retrieved high-accuracy wind vectors at altitudes over 3 km where measurements were deficient with an $SNR_{min}$ of 1 dB.

• 축산식품과학과 산업
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• v.7 no.2
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• pp.39-47
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• 2018

• Korean Journal of Optics and Photonics
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• v.20 no.1
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• pp.29-33
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• 2009

We propose a new measuring technique based on optical low-coherence reflectometry that enables us to determine the refractive index and the geometrical thickness of a transparent sample by one-time scanning only. By passing a collimated beam having a finite size through the edge of the sample, the refractive index and the geometrical thickness can be determined simultaneously from the comparison of interferograms generated by two kinds of reflected beams. In this study, a refractive index could be determined with the accuracy of $10^{-3}$, and its accuracy would be enhanced by using a more precise translator and a thicker sample.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.44 no.2
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• pp.149-153
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• 1999

Prediction of rice developmental stage is necessary for proper crop management and a prerequisite for growth simulation as well. The objectives of the present study were to find out the relationship between the plastochrone index(PI) and the developmental index(DVI) estimated by non-parametric phenology model which simulates the duration from seedling emergence(DVI=0) to heading(DVI=l) by employing daily mean air temperature and daylength as predictor variables, and to confirm the correspondency of developmental indice to panicle developmental stages based on this relationship. Four japonica rice cultivars, Kwanakbyeo, Sangpungbyeo, Dongjinbyeo, and Palgumbyeo which range from very early to very late in maturity, were grown by sowing directly in dry paddy field five times at an interval of two weeks. Data for seedling emergence, leaf appearance, differentiation stage of primary rachis branch and heading were collected. The non-parametric phenology model predicted well the duration from seedling emergence to heading with errors of less than three days in all sowings and cultivars. PI was calculated for every leaf appearance and related to the developmental index estimated for corresponding PI. The stepwise polynomial analysis produced highly significant square-rooted cubic or biquadratic equations depending on cultivars, and highly significant square-rooted biquadratic equation for pooled data across cultivars without any considerable reduction in accuracy compared to that for each cultivar. To confirm the applicability of this equation in predicting the panicle developmental stage, DVI at differentiation stage of primary rachis branch primordium was calculated by substituting PI with 82 corresponding to this stage, and the duration reaching this DVI from seedling emergence was estimated. The estimated duration revealed a good agreement with that observed in all sowings and cultivars. The deviations between the estimated and the observed were not greater than three days, and significant difference in accuracy was not found for predicting this developmental stage between those equations derived for each cultivar and for pooled data across all cultivars tested.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.371-381
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• 2015

In the mountain streams in Jeju Island, strong turbulence and roughness usually made it nearly impossible to utilize most of intrusive instrumentation for streamflow discharge measurements. Instead, a non-intrusive fixed electro-magnetic wave surface velocimetry (fixed EWSV: Kalesto) became alternatively popular in many representative streams to measure stream discharge seamlessly. Currently, Kalesto has shown noteworthy performance with little loss in flood discharge measurements and also has successfully provided discharge for every minute. However, Kalesto has been operated to regard its measured one-point velocity as the representative mean velocity for the given cross-section. Therefore, it could be highly possible to potentially encompass discharge measurements errors. In this study, we analyzed the difference between such Kalesto discharge measurements and other alternative concurrent discharge measurements such as Acoustic Doppler Current Profiler (ADCP) and mobile EWSV which were able to measure velocity in multi-points in the cross-section. Consequently, Kalesto discharge deviated from ADCP discharge in amount of 48% for relatively low flow, and more than 20% difference for high flow compared with mobile EWSV discharge measurements. These results indicated that the one-point velocity measured by Kalesto should be used as a cross-sectional mean velocity, rather it should be accounted for as an index-velocity in conjunction with directly measured cross-sectional mean velocity by using more reliable instrumentations. After inducing Kalesto Discharge Correction Coefficient (KDCC) that actually means relationship between index velocity and cross-sectional mean velocity, the corrected discharge from Kalesto was significantly improved. Therefore, we found that index velocity method should be applied to obtain better accuracy of discharge measurement in case of Kalesto operation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.281-292
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• 2014

This study examined the accuracy that produced using various types and combinations of landslide-related factors from landslide susceptibility index maps. A database of landslide-related factors was adopted by the landslide locations that obtained from aerial photographs, and the topographic factors that derived from airborne LiDAR observations and digital maps, and various soil, forest, and land cover. Landslide susceptibility index maps were calculated by logistic regression and frequency ratio from the landslide susceptibility index. The correlation between airborne LiDAR data and digital map was shown strong similarities with one another. Landslide susceptibility index maps indicated the existence of a strong correlation and high prediction accuracy, especially when the frequency ratio and airborne LiDAR were used. Therefore, we concluded that the Airborne LiDAR will contribute to the development of effective landslide prediction methods and damage reduction measures.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.