• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.251-267
• /
• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

• Journal of Korean Society of Forest Science
• /
• v.85 no.2
• /
• pp.316-324
• /
• 1996

This study was conducted by relative time study to identify the allowance time and basic work time, which is together composed of the total work time, in the wage composition and work process composition. This study was done for the case of a basic one person a group from thinning treatment for some conifer species such as Japanese red pine, Japanese larch, pitch pine and Korean pine. 1. In order to apply the Japanese allowance time of four species to the general conifer case, the maximum value of allowance time composition value among those of four species was selected. The results are as follows: delay time for person 1.8%, rest time 14.1%, delay time for machine 12.5% and wating time 0.4%. The some of ratios of adjustment allowance time is 28.8%. 2. Estimated wage basis time table, which can be used for wage table or process table, was prepared by adding up adjusted general allowance time and standard work time estimated by estimation equation for each species through the time study.

• Journal of Korean Society of Forest Science
• /
• v.110 no.2
• /
• pp.155-164
• /
• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• The Korean Journal of Applied Statistics
• /
• v.35 no.4
• /
• pp.457-468
• /
• 2022

In aged society, it is important to prevent older people from being disability needing long-term care. The purpose of this study is to develop a prediction model to discover high-risk groups who are likely to be beneficiaries of Long-Term Care Insurance. This study is a retrospective study using database of National Health Insurance Service (NHIS) collected in the past of the study subjects. The study subjects are 7,724,101, the population over 65 years of age registered for medical insurance. To develop the prediction model, we used logistic regression, decision tree, random forest, and multi-layer perceptron neural network. Finally, random forest was selected as the prediction model based on the performances of models obtained through internal and external validation. Random forest could predict about 90% of the older people in need of long-term care using DB without any information from the assessment of eligibility for long-term care. The findings might be useful in evidencebased health management for prevention services and can contribute to preemptively discovering those who need preventive services in older people.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.953-966
• /
• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1119-1130
• /
• 2021

Urban flooding caused by localized heavy rainfall with unstable climate is constantly occurring, but a system that can predict spatial flood information with weather forecast has not been prepared yet. The worst flood situation in urban area can be occurred with difficulties of structural measures such as river levees, discharge capacity of urban sewage, storage basin of storm water, and pump facilities. However, identifying in advance the spatial flood information can have a decisive effect on minimizing flood damage. Therefore, this study presents a methodology that can predict the urban flood map in real-time by using rainfall data of the Korea Meteorological Administration (KMA), the results of two-dimensional flood analysis and random forest (RF) regression model. The Ujeong district in Ulsan metropolitan city, which the flood is frequently occurred, was selected for the study area. The RF regression model predicted the flood map corresponding to the 50 mm, 80 mm, and 110 mm rainfall events with 6-hours duration. And, the predicted results showed 63%, 80%, and 67% goodness of fit compared to the results of two-dimensional flood analysis model. It is judged that the suggested results of this study can be utilized as basic data for evacuation and response to urban flooding that occurs suddenly.

• Journal of the Korean Wood Science and Technology
• /
• v.24 no.4
• /
• pp.32-39
• /
• 1996

The overall purpose of this study was to investigate the dimensional changes of melamine sheet laminated medium density fiberboard(MDF) floorings by sub-heating system(Ondol). This study was also conducted to improve the properties of melamine sheet laminated MDF floorings. The effects of density, resin content, manufacturing speed of MDF and types of melamine sheet on dimensional and weight changes of floorings were investigated. The results were as followings. 1. Dimensional and weight change of melamine sheet laminated MDF flooring by heating decreased with decreasing the density of MDF. 2. Dimensional and weight change of melamine sheet laminated MDF flooring by heating decreased with increasing the resin content of MDF. 3. Dimensional and weight change of melamine sheet laminated MDF flooring by heating decreased with decreasing the manufacturing speed of MDF. 4. Dimensional change of melamine sheet laminated MDF flooring in width direction by heating was doubled than that in machine direction. 5. Dimensional change and curling of high pressure melamine laminate(HPM) laminated MDF flooring by heating was less than those of low pressure melamine laminate(LPL) flooring. 6. Weight loss of melamine sheet laminated MDF flooring by heating has linear relationship with shrinkage.

• Journal of Korea Technical Association of The Pulp and Paper Industry
• /
• v.36 no.5 s.108
• /
• pp.44-52
• /
• 2004

It is well known that Korean traditional Hanji have lots of predominant physical and optical properties such as high density, high air permeability, long lasting quality and lightness. The paper-making raw materials of traditional Korean Hanji are the bast fibre cooked from the Korean paper mulberry as a fibrous materials and sticky aqueous material from the root of Hibiscus anihot L. as additives for good dispersion of stock. Additionally, the mechanical properties of Hanji varies according to the cooking methods of bast tissues of Korean paper mulberry, the treatment methods of fibrous raw materials such as bleaching and refining, the wet formation types of sheet-making such as 'Oebal-chiji' and 'Ssangbal-choji', and the finishing treatment like stamping. This study was carried out to investigate and evaluate the quality properties of Korean traditional hand-made Hanji, and compared with commercial machine-made paper and modified prepared sheets. The physical quality comparisons of different kinds of Hanji were focused on the methods of hand-sheet making, the types of raw materials, the treatment of stamping, and the properties of ink reception and spreading.

• Journal of the Korea Furniture Society
• /
• v.22 no.1
• /
• pp.18-25
• /
• 2011

This experiment was carried out to investigate the effects of nail diameter, driving distance from end on the nail check length, and the effects of nail diameter, prehole for nail driving, and nail driving slope on the nail withdrawal resistance, by the static test of universal testing machine. The test specimen were Japanese cedar (Cryptomeria japonica D. Don.) boards grown in southern district of Korea, and the nails for test were 2.02～4.82 mm in diameter. After nail driving, the back face checks of test boards were longer than the surface checks. The optimum nail diameter without checks or loss of nail withdrawal resistance were below 10% of board width and the optimum driving distance from end of boards were ten multiple of nail diameter. The relation between nail diameter (x) and withdrawal resistance (y) was linear and the regression formulae for Japanese cedar board was y = 8.66x + 7.6 ($R^2=0.978$). As both of the prehole diameter and driving slope were increased, the withdrawal resistances were significantly decreased.

• ETRI Journal
• /
• v.42 no.3
• /
• pp.311-323
• /
• 2020

Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet's sizes, packet's inter-arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points.