• International Journal of Computer Science & Network Security
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• 제23권11호
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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.

• 한국대기환경학회지
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• 제18권1호
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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.

• 대한교통학회지
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• 제33권1호
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• pp.61-69
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• 2015

이 연구에서는 국내 총 화물물동량에 대한 5개 계량경제모형들의 예측정확도를 비교한다. 적용된 5개 모형은 통상최소자승모형, 부분조정모형, 축소된 자기회귀분포시차모형, 벡터자기회귀 모형, 시간변동계수모형이다. 모형의 추정과 예측은 1970-2011년 동안의 연간 국내 화물물동량 자료와 광공업생산지수를 이용하여 수행되었다. 5개 모형은 반복적인 예측방법을 이용하여 1년 후, 3년 후, 5년 후 예측성능이 비교되었다. 추가적으로 장래변동성의 크기에 따라 두 예측기간으로 나누어 예측정확도를 비교하였고, 결과적으로 시간변동계수모형은 변동을 갖는 예측기간에 대해서 가장 높은 정확도를, 반면에 벡터자기회귀 모형은 점진적인 변화를 갖는 예측기간에 대해서 다른 모형에 비해 우수한 성능을 보여주는 것으로 분석되었다.

• 한국환경과학회지
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• 제25권9호
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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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• 제7권2호
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• pp.39-47
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• 2018

• 한국광학회지
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• 제20권1호
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• pp.29-33
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• 2009

본 연구에서는 저간섭성 반사계(Optical low-coherence reflectometry)를 이용하여 한 번의 측정으로 투명 시료의 두께와 굴절률을 동시에 측정하는 기술을 제안하였다. 제안된 방법은 유한 반경을 가진 시준된 광속을 시료의 경계 영역으로 입사시키는 것으로써, 시료가 있는 부분과 없는 부분으로부터 반사된 광속에 의한 간섭 무늬들을 한 번에 획득할 수 있다. 한번의 측정을 통해 얻어진 두 종류 간섭 무늬들의 상대적인 위치 차이를 이용하여 시료의 두께와 굴절률을 동시에 결정할 수 있었다. 굴절률의 정밀도는 이송장치의 위치 정밀도가 향상되고, 시료의 두께가 두꺼워 질수록 개선될 수 있으며, 본 실험에서는 약 $10^{-3}$의 정밀도로 굴절률을 결정할 수 있었다.

• 한국작물학회지
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• 제44권2호
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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.

• 한국환경과학회지
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• 제24권4호
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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.

• 한국측량학회지
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• 제32권4_1호
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• pp.281-292
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• 2014

본 연구는 산사태 관련 인자를 달리하여 산사태 취약성을 분석한 후, 정확도를 비교하고자 한다. 이를 위해 항공사진을 이용하여 산사태 위치를 추출하였고, 항공 LiDAR와 수치지도를 이용한 지형인자, 각종 주제도를 이용한 토양, 임상, 토지피복 인자를 추출하여 공간데이터베이스를 구축하였다. 산사태 취약성 지도는 로지스틱 회귀분석과 빈도비를 이용하여 산사태 취약지수를 산정하는 것에 의해 작성되었다. 분석결과, 항공 LiDAR와 수치지도의 상관관계는 거의 일치하였으며, 각 방법별로 작성된 산사태 취약성 지도 사이에는 강한 상관관계가 존재하였다. 각 방법별로 작성된 산사태 취약성 지도는 높은 예측 정확도를 보였다. 특히, 빈도비와 항공 LiDAR를 이용할 경우 성능이 더욱 향상되었다. 이를 통해 항공 LiDAR 자료는 효과적인 산사태 발생 예측 및 피해저감대책을 수립하는데 기여할 것으로 판단된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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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.