• Journal of the Korea institute for structural maintenance and inspection
• /
• v.14 no.3
• /
• pp.196-200
• /
• 2010

The number of railway tunnels has been increasing rapidly. Although fires in long railway tunnels are rare, the consequences can be devastating. Prior to this study, there were no adequate time-temperature curves for the fire safety assessment of Korean railway tunnels. We studied a standard foreign time-temperature curve for which the heat rate is based on the traffic and the types of vehicles. We then proposed a hydrocarbon curve as a fire design model for railway tunnels in Korea. We examined the implications of this proposed model on railway tunnel structures using numerical analysis.

• Research in Plant Disease
• /
• v.19 no.3
• /
• pp.196-200
• /
• 2013

This study was carried out to investigate yield loss due to soybean leaf spot disease caused by Cercospora sojina Hara and to determine the economic threshold level. The investigations revealed highly significant correlations between disease severity (diseased leaf area) and yield components (pod number per plant, total grain number per plant, total grain weight per plant, percent of ripened grain, weight of hundred seed, and yield). The correlation coefficients between leaf spot severity and each component were -0.90, -0.90, -0.92, -0.99, -0.90 and -0.94, respectively. The yield was inversely proportional to the diseased leaf area increased. The regression equation, yield prediction model, between disease severity (x) and yield (y) was obtained as y = -3.7213x + 354.99 ($R^2$ = 0.9047). Based on the yield prediction model, economic injury level and economic threshold level could be set as 3.3% and 2.6% of diseased leaf area of soybean.

• Journal of the Korea Society for Simulation
• /
• v.33 no.2
• /
• pp.37-44
• /
• 2024

The Korean Helicopter Development Program has successfully introduced the Surion helicopter, a versatile multi-domain operational aircraft that replaces the aging UH-1 and 500MD helicopters. Specifically designed for maneuverability, the Surion plays a crucial role in low-altitude tactical maneuvers for personnel transportation and specific missions, emphasizing the helicopter's survivability. Despite the significance of its low-altitude tactical maneuver capability, there is a notable gap in research focusing on multi-mission tactical maneuvers that consider the risk factors associated with deploying the Surion in the presence of enemy air defenses. This study addresses this gap by exploring a method to enhance the Surion's low-altitude maneuvering paths, incorporating information about enemy air defenses. Leveraging the Proximal Policy Optimization (PPO) algorithm, a reinforcement learning-based approach, the research aims to optimize the helicopter's path planning. Visualized experiments were conducted using a Surion model implemented in the Unity environment and ML-Agents library. The proposed method resulted in a rapid and stable policy convergence for generating optimal maneuvering paths for the Surion. The experiments, based on two key criteria, "operation time" and "minimum damage," revealed distinct optimal paths. This divergence suggests the potential for effective tactical maneuvers in low-altitude situations, considering the risk factors associated with enemy air defenses. Importantly, the Surion's capability for remote control in all directions enhances its adaptability in complex operational environments.

• Journal of Korean Society of Forest Science
• /
• v.100 no.4
• /
• pp.664-671
• /
• 2011

Random Walk Model can predict the sediment areas of debris flow but it must be extracted three parameters fitted topographical environment. This study developed the method to extract the optimal values of three parameters - Once flowing volume, Stopping slope and Gravity weight - for Random Walk Model. And the extracted parameters were validated by aerial photographs of the debris flowed area. To extract the optimal parameters was randomly performed, limiting the range values of three parameters and developing an accuracy decision method that is called the rate of concordance. The set of the optimal parameters was decided on highest the rate of concordance and a consistency. As a result, the optimal parameters in Bonghwa county were showed that the once flowing volume is $1.0m^3$, the stopping slope is $4.2^{\circ}$ and the gravity weight is 2 when the rate of concordance is -0.2. The validating result of the optimal parameters showed closely that the rate of concordance is average -0.2.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.6
• /
• pp.93-101
• /
• 2022

RC Slab bridges in Korea account for more than 70% of the total bridges for more than 20 years of service. As the number of aging structures increases, the importance of safety diagnosis and maintenance of structures increases. For highway bridges, cracks are a main cause of deck deterioration, which is very closely related to the decrease in bridge durability and service life. In addition, the damage rate of expansion joints and bearings accounts for approximately 73% higher than that of major members. Therefore, this study defined damage scenarios combined with devices damages and deck deterioration. The stress distribution and maximum stress on the deck were then evaluated using design vehicle load and daily temperature gradient for single and combined damage scenarios. Furthermore, this study performed damage-spread analysis and predicted condition ratings according to a deck deterioration model generated from the inspection and diagnosis history data of cracks. The heterogeneous damages combined with the member damages of expansion joints and bearings increased the rate of crack area and damage spread, which accelerated the time to reach the condition rating of C. Therefore, damage to bridge members requires proper and prompt repair and replacement, and otherwise it can cause the damage to bridge deck and the spread of the damage.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.29-39
• /
• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.316-328
• /
• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.2
• /
• pp.227-235
• /
• 2018

Along with the recent increase in traffic volume of vehicles, accidents involving rollover of vehicles have been rapidly increased, resulting in an increase casualties. And to prevent this, various technologies such as vehicle crash test equipment and analysis program development have been advanced. In this study, the applied vehicle model is FORD EXPLORER model, and PC-Crash program for vehicle collision analysis is used to predict the rollover accident behavior of SUV and the collision velocity. Compared with the actual rollover behavior of SUV through the FMVSS No 208 regulations, the analysis results showed similar results, the characteristics of the collision velocity and roll angle showed a tendency that the error rate slightly increased after 1000 msec. Then, as a result of considering using the database of NHTSA, it is shown that the rollover accident occur most frequently in the range of the collision velocity of 15~77 km/h and the collision angle of $22{\sim}74^{\circ}$. And it is possible to estimate the vehicle speed and collision time when the vehicle roof is broken by reconstructing the vehicle starting position, the roof failure position and the stop position by applying the actual accident case.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.3
• /
• pp.149-155
• /
• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.66 no.1
• /
• pp.72-79
• /
• 2021

The increasing use of deicing salts has caused various environmental problems, including crop damage along the motorway where deicing salts are sprayed during winter. Deicing salts used on roads have been reported to negatively affect crops, but little information is known about their impact on crops. A seedling emergence assay was conducted to evaluate the effects of deicing salts on crops using oilseed rape (Brassica napus) as a model plant. We tested five chloride deicing salts consisting of NaCl, CaCl2, or MgCl2 and 1 non-chloride deicing salt (SM-3) at a range of concentrations (25, 50, 100, 200, and 400 mM), and untreated control. Regardless of deicing salts, they significantly delayed and reduced seedling emergence of oilseed rape with increasing salt concentration. Non-linear regression analysis of seedling emergence with a range of salt concentrations by fitting to the log-logistic model revealed that the chloride deicing salts reduced seedling emergence more than the non-chloride deicing salt SM-3. The GR50 value, the concentration causing 50% seedling emergence, of SM-3 was 47.1 mM, while those of the chloride deicing salts ranged from 30.7 mM (PC-10) to 37.5 mM (ES-1), showing approximately 10 mM difference between non-chloride and chloride deicing salts. Our findings suggest that seedling emergence assay is a useful tool to estimate the potential damage caused by deicing salts on crops.