• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.4
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• pp.456-462
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• 2014

Infrared(IR) guided heat-seeking missiles uses IR emissions from aircraft to detect and track a target. Due to passive characteristic of the IR guidance, early detection of the missile is difficult and it is significant threat to aircraft survivability. Therefore, IR signature prediction of the aircraft is an important aspect of the stealth technology. In this study, we simulated IR signature of the aircraft in real atmospheric conditions. Aircraft surface temperature distribution was calculated by using RadthermIR code. Based on temperature distribution, IR radiance and BRDF(Bidirectional Reflectance Distribution Function) image were simulated for different weather(seasonal) and background(sky/soil) conditions. The IR contrast tendencies are not aligned with surface temperature or magnitude of target IR radiance. Therefore, it is essential to simulate IR signature with various conditions and background to acquire reliable database.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1424-1429
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• 2007

This paper presents a simple and reliable one-touch type multi-immunosensing lab-on-a-chip (LOC) detecting antibodies as multi-disease markers using electrochemical method suitable for a portable point-of-care system (POCS). The multi-stacked LOC consists of a PDMS space layer for liquids loading, a PDMS valve layer with 50 im in height for the membrane, a PDMS channel layer for the fluid paths, and a glass layer for multi electrodes. For the disposable immunoassay which needs sequential flow control of sample and buffer liquids according to the designed strategies, reliable and easy-controlled on-chip operation mechanisms without any electric power are necessary. The driving forces of sequential liquids transfer are the capillary attraction force and the pneumatic pressure generated by air bladder push. These passive fluid transport mechanisms are suitable for single-use LOC module. Prior to the application of detection of the antibody as a disease marker, the model experiments were performed with anti-DNP antibody and anti-biotin antibody as target analytes. The flow test results demonstrate that we can control the fluid flow easily by using the capillary stop valve and the PDMS check valves. By the model tests, we confirmed that the proposed LOC is easily applicable to the bioanalytic immunosensors using bioelectrocatalysis.

• Parasites, Hosts and Diseases
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• v.38 no.3
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• pp.119-138
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• 2000

Korean vivax malaria had been prevalent for longtime throughout the country with low endemicity. As a result of the Korean war (1950-1953), malaria became epidemic. In 1959-1969 when the National Malaria Eradication Service (NMES) was implemented, malaria rates declined, with low endemicity in the south-west and south plain areas and high endemic foci in north Kyongsangbuk-do (province) and north and east Kyonggi-do. NMES activities greatly contributed in accelerating the control and later eradication of malaria. The Republic of Korea (South Korea) was designated malaria free in 1979. However, malaria re-emerged in 1993 and an outbreak occurred in north Kyonggi-do and north-west Kangwon-do (in and/or near the Demilitarized Zone, DMZ) , bordering North Korea. It has been postulated that most of the malaria cases resulted from bites of sporozoite-infected females of An. sinensis dispersed from North Korea across the DMZ. Judging from epidemiological and socio-ecological factors, vivax malaria would not be possible to be endemic in South Korea. Historical data show that vivax malaria in Korea is a typical unstable malaria. Epidemics may occur when environmental, socio-economical, and/or political factors change in favor to malaria transmission, and when such factors change to normal conditions malaria rates become low and may disappear. Passive case detection is a most feasible and recommendable control measure against the unstable vivax malaria in Korea in cost-effect point of view.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.371-376
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• 2020

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.

• Archives of Pharmacal Research
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• v.13 no.3
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• pp.227-232
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• 1990

A Physiologically based pharmacokinetic model was used to describe the distribition and elimination of cefriazone in the rat. To validate the practical application of the model, the effect of cffeine on the model was also examined. The model consisted of eleven compartments representing the major sites for ceftriaxone distribution including carcass which served as a residual compartment. Elimination was represented by renal and hepatic (metabolic biliary )excretion with GI secretion and re-absorption. The drug concentrations in most of the tissues were simulated using flow limited equations while brain levels were simulated using membrane limited passive diffusion distribution. The experimental data were obtained by averaging the concentration of drug in the plasma and tissues of five rats after i. v. injection of cefriazone 100 mg/kg without and with caffeine 20 mg/kg. The data for the amount of ceftriazone excreted in urine and gut contents were used to apportion total body clearance. HPLC with UV detection was used for the assay with 0.1-0.2 $\mu$g/ml sensitivity. The great majority of drug concentrations with and without caffeine show reasonably good agreements to the simulation results within 20%. The effect of caffeine on renal and hepatic clearances was apparent with 18.8% and 18.6% increase in the model values, respectively.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.60-64
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• 2022

In the automotive industry, the development of automobiles to meet safety requirements is becoming increasingly complex. This is because quality evaluation agencies in each country are continually strengthening new safety standards for vehicles. Among these various requirements, collision safety must be satisfied by controlling airbags, seat belts, etc., and can be defined as post-crash safety. Apart from this safety system, the Advanced Driver Assistance Systems (ADAS) use advanced detection sensors, GPS, communication, and video equipment to detect the hazard and notify driver before the collision. However, research to improve passenger safety in case of an accident by using the sensor of active safety represented by ADAS in the existing passive safety is limited to the level that utilizes the sudden braking level of the FCA (Forward Collision-avoidance Assist) system. Therefore, this study aims to develop logic that can improve passenger protection in case of an accident by using ADAS information and driving information secured before a collision. The proposed logic was constructed based on LSTM deep learning techniques and trained using crash test data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.21-29
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• 2011

At existing skin detection methods using skin color information defined based on the prior knowldege, threshold value to be used at the stage of dividing the backround and the skin region was decided on a subjective point of view through experiments. Also, threshold value was selected in a passive manner according to their background and illumination environments in these existing methods. These existing methods displayed a drawback in that their performance was fully influenced by the threshold value estimated through repetitive experiments. To overcome the drawback of existing methods, this paper propose a skin region detection method using a histogram approximation based on the mean shift algorithm. The proposed method is to divide the background region and the skin region by using the mean shift method at the histogram of the skin-map of the input image generated by the comparison of the similarity with the standard skin color at the CbCr color space and actively finding the maximum value converged by brightness level. Since the histogram has a form of discontinuous function accumulated according to the brightness value of the pixel, it gets approximated as a Gaussian Mixture Model (GMM) using the Bezier Curve method. Thus, the proposed method detects the skin region by using the mean shift method and actively finding the maximum value which eventually becomes the dividing point, not by using the manually selected threshold value unlike other existing methods. This method detects the skin region high performance effectively through experiments.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.15-30
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• 2023

Recently, various services using artificial intelligence(AI) are emerging in the media field as well However, most of the video editing, which involves finding an editing point and attaching the video, is carried out in a passive manner, requiring a lot of time and human resources. Therefore, this study proposes a methodology that can detect the edit points of video according to whether person in video are spoken by using Video Swin Transformer. First, facial keypoints are detected through face alignment. To this end, the proposed structure first detects facial keypoints through face alignment. Through this process, the temporal and spatial changes of the face are reflected from the input video data. And, through the Video Swin Transformer-based model proposed in this study, the behavior of the person in the video is classified. Specifically, after combining the feature map generated through Video Swin Transformer from video data and the facial keypoints detected through Face Alignment, utterance is classified through convolution layers. In conclusion, the performance of the image editing point detection model using facial keypoints proposed in this paper improved from 87.46% to 89.17% compared to the model without facial keypoints.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.3
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• pp.133-146
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• 2015

This study is to investigate the causal relationship between exchange hedge and the net income of the shipbuilder through the unit root test and co-integration and vector autoregressive model(Vector Autoregressive Model: VAR). First, quarter net income of shipbuilders to order a unit root tests from 2000 to 2013 was used as a value after the Johnson transformation. In the same period, the return on bond futures(KTBF), three years bond yield(KTB3Y), America-Korea exchange differences are weekly data for each quarterly difference in value was converted by utilization, shipbuilding shares after log transformation which it was used. Also, structural change point investigation analysis to verify that looked to take advantage of the structural changes occur in the exchange hedge strategies affecting net income in the shipbuilding industry. Between the exchange hedge and net income of shipbuilders in structural change points detection and analysis showed that structural changes occur starting in 2004. In other words, strategy of shipbuilders about exchange hedge has occurred from "passive exchange hedge" to "active exchange hedge". The exchange hedge of the Korea shipbuilders through the estimation of the VAR was able to grasp that affect the profitability of mutual shipbuilders. Macroeconomic variables and stock prices could also check to see that affected the net income of the shipbuilding industry.