• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.485-491
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• 2015

The technology review about risk of hypothermia of victim according to heat transfer characteristic of life raft and sea state can use accident correspondence of standing and sinking of ship. This study studied heat transfer characteristics required for the design of life raft and thermal insulation property analysis and evaluation methods. In addition, it is study for comprehend the risk of hypothermia and suggest analysis result that is experiment of thermal insulation property and body temperature property for decide of prediction the body temperature decline Thermal Analysis apply the finite element analysis method is comprehended the property of heat conductivity, convective effect of sea water and properties changes according to property of insulation material. it measure the heat flux with attach temperature sensor on body in order to comprehend the variation of body temperature with boarding a life raft experiment on a human body. This study validate results by comparing variation of temperature measured from experiment on a body with variation of temperature from finite element analysis model. Also, the criteria of hypothermia was discussed through result of finite element analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.851-861
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• 2022

In this study, we analyzed the effects of trees planted in urban areas on PM_2.5 reduction using a computational fluid dynamics (CFD) model. For realistic numerical simulations, the meteorological components(e.g., wind velocity components and air temperatures) predicted by the local data assimilation and prediction system (LDAPS), an operational model of the Korea Meteorological Administration, were used as the initial and boundary conditions of the CFD model. The CFD model was validated against, the PM_2.5 concentrations measured by the sensor networks. To investigate the effects of trees on the PM_2.5 reduction, we conducted the numerical simulations for three configurations of the buildings and trees: i) no tree (NT), ii) trees with only drag effect (TD), and iii) trees with the drag and dry-deposition effects (DD). The results showed that the trees in the target area significantly reduced the PM_2.5 concentrations via the dry-deposition process. The PM_2.5 concentration averaged over the domain in DD was reduced by 5.7 ㎍ m^-3 compared to that in TD.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.278-286
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• 2022

In this study, a 33-mode Free-Flooded Ring (FFR) transducer was designed to apply piezoelectric single crystal PIN-PMN-PT, which has high piezoelectric constants and electromechanical coupling coefficient. To ensure low-frequency high transmitting sensitivity characteristics with a small size of FFR transducer, the commercial FFR transducer based on piezoelectric ceramics was compared. To develop the FFR transducer with broadband characteristics, a piezoelectric segmented ring structure inserted with inactive elements was applied. The oil-filled structure was applied to minimize the change of acoustic characteristics of the ring transducer. It was verified that the transmitting voltage response, underwater impedance, and beam pattern matched the finite element numerical simulation results well through an acoustic test. The difference in the transmitting voltage response between the measured and the simulated results is about 1.3 dB in cavity mode and about 0.3 dB in radial mode. The fabricated FFR transducer had a higher transmitting voltage response compared to the commercial transducer, but the diameter was reduced by about 17 %. From this study, it was confirmed that the feasibility of a single crystal-applied FFR transducer with compact size and high-power characteristics. The effectiveness of the performance prediction by simulation was also confirmed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.699-707
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• 2008

For most structural evaluation of bridge integrity, it is very important to measure the geometric profile, which is a major factor representing the global behavior of civil structures, especially bridges. In the past, because of the lack of appropriate methods to measure the deflection profile of bridges on site, the measurement of deflection has been restricted to just a few discrete points along the bridge, and the measuring points have been limited to the locations installed with displacement transducers. Thus, some methods for predicting the static deflection by using fiber optic strain sensors has been applied to simply supported bridges. In this study, a method of estimating the static deflection profile by using strains measured from suspension bridges was proposed. Based on the classical deflection theory of suspension bridges, an equation of deflection profile was derived and applied to obtain the actual deflection profile on Namhae suspension bridge. Field load tests were carried out to measure strains from FBG strain sensors attached inside the stiffening girder of the bridge. The predicted deflection profiles were compared with both precise surveying data and numerical analysis results. Thus, it is found that the equation of predicting the deflection profiles proposed in this study could be applicable to suspension bridges and the FBG strain sensors could be reliable on acquiring the strain data from bridges on site.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.27-35
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• 2024

Fuel gases such as methane and propane are used in explosion hazardous area of domestic plants and can form non-uniform mixtures with the influence of process conditions due to leakage. The fire-explosion risk assessment using literature data measured under uniform mixtures, damage prediction can be obtained the different results from actual explosion accidents by gas leaks. An explosion characteristics such as explosion pressure and flame velocity of non-uniform gas mixtures with concentration change similar to that of facility leak were examined. The experiments were conducted in a closed 0.82 m long stainless steel duct with observation recorded by color high speed camera and piezo pressure sensor. Also we proposed the quantification method of non-uniform mixtures from a regression analysis model on the change of concentration difference with time in explosion duct. For the non-uniform condition of this study, the area of flame surface enlarged with increasing the concentration non-uniform in the flame propagation of methane and was similar to the wrinkled flame structure existing in a turbulent flame. The time to peak pressure of methane decreased as the non-uniform increased and the explosion pressure increased with increasing the non-uniform. The ranges of K_G (Deflagration index) of methane with the concentration non-uniform were 1.30 to 1.58 [MPa·m/s] and the increase rate of K_G was 17.7% in methane with changing from uniform to non-uniform.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.63-74
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• 2024

In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.33-39
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• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Economic and Environmental Geology
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• v.57 no.4
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• pp.353-362
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• 2024

This study examined the spectral characteristics of sediments according to moisture content using an unmanned aerial vehicle (UAV)-based hyperspectral sensor and evaluated the efficiency of moisture content detection at different flight altitudes. For this purpose, hyperspectral images in the 400-1000nm wavelength range were acquired and analyzed at altitudes of 40m and 80m for sediment samples with various moisture contents. The reflectance of the sediments generally showed a decreasing trend as the moisture content increased. Correlation analysis between moisture content and reflectance showed a strong negative correlation (r < -0.8) across the entire 400-900nm range. The moisture content detection model constructed using the Random Forest technique showed detection accuracies of RMSE 2.6%, R² 0.92 at 40m altitude and RMSE 2.2%, R² 0.95 at 80m altitude, confirming that the difference in accuracy between altitudes was minimal. Variable importance analysis revealed that the 600-700nm band played a crucial role in moisture content detection. This study is expected to be utilized in efficient sediment moisture management and natural disaster prediction in the field of environmental monitoring in the future.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. 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, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. 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 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.