• The Korean Journal of Blood Transfusion
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• v.23 no.2
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• pp.169-172
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• 2012

Lutheran a antigen ($Lu^a$) is detected in 6 to 8% of Caucasians and Africans. In Korean and other Asian populations, it is very rare or nearly absent. Therefore, although $Lu^a$ has a considerable immunizing capacity, sensitization to $Lu^a$ is a rare event. Here we report on a rare case of anti-$Lu^a$ in a 70 year-old female patient with Lu (a-/b+) phenotype and review the relevant literature. Due to the paucity of $Lu^a$ positive panel cells in antibody screening and identification tests, detection of this rare antibody to $Lu^a$ antigen is not feasible. Therefore, we should keep in mind the possibility of the misleading false negative result in detection of antibody to this low incidence antigen.

• 재활복지
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• v.21 no.3
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• pp.129-146
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• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.20-27
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• 2022

This article aims to develop an algorithm that detects fire pump defects by analyzing the current signals of an induction motor, which are triggered by changes in the flow rate and pressure of multistage volute pumps that are used for fire services. The operational status of the pumps was categorized into three: first, normal operation; second, a defect that is caused by a change in the current value; and third, a defect occasioned by a change in current, pressure, and flow rate. When a fire pump was in normal operation, the motor's operating current was measured between 5.06 A and 6.9 A, the flow rate was estimated at 0-0.27 m³/min, and the pressure ranged from 0 to 0.47 MPa. In the event that a defect was caused by an abnormal current value in the motor, it was attributed to the pump's adherence. Furthermore, if there was no source of water, the defect was considered to have been induced by phase-loss operation, no-load operation, or run-stop operation, with the current value of each scenario being measured at > 52.8 A, < 4.13 A, > 45.15 A, and < 3.8 A, respectively, placing its overall range between 0 and 50 A. The sources of defects were detected based on an analysis of the flow rate, pressure, and current, which represent the following causes: air inflow into the casing, inadequate suction of water, and reverse-phase operation, respectively. Each cause entailed the following values: when air seeped into the casing, the pressure was measured at 0.24 MPa irrespective of changes in the flow rate; when there was inadequate suction of water, the pressure was recorded between 0 and 0.05 MPa despite changes in the flow rate; and when the power line's reverse-phase loss was the cause of the defect, the pressure was measured at 0.33 MPa for a flow rate of 0 L/min, and a higher flow rate decreased the pressure to nearly 0 MPa. The results of this study will enable engineers to develop a pump defect detection algorithm that is based on an analysis of current, and this algorithm will facilitate the execution of a program that will control a fire pump defect detection system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.119-129
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• 2023

Sleep apnea causes various secondary disease such as hypertension, stroke, myocardial infarction, depression and cognitive impairment. Early detection and continuous management of sleep apnea are urgently needed since it causes cardio-cerebrovascular diseases. In this study, wearable device for monitoring respiration during sleep using PVDF film was developed to detect vibration through trachea caused by breathing, which determines normal breathing and sleep apnea. Variables such as respiration rate and apnea were extracted based on the detected breathing sound data, and a noise reduction algorithm was established to minimize the effect even when there is a noise signal. In addition, it was confirmed that irregular breathing patterns can be analyzed by establishing a moving threshold algorithm. The results show that the accuracy of the respiratory rate from the developed device was 98.7% comparing with the polysomnogrphy result. Accuracy of detection for sleep apnea event was 92.6% and that of the sleep apnea duration was 94.0%. The results of this study will be of great help to the management of sleep disorders and confirmation of treatment by commercialization of wearable devices that can monitor sleep information easily and accurately at home during daily life and confirm the progress of treatment.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.59-64
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• 2008

D. W. KANG, J. S. CHOI, and G. R. TACK, A Study on Real-Time Sports Activity Classification & Monitoring Using a Tri-axial Accelerometer. Korean Jouranl of Sport Biomechanics, Vol. 18, No. 2, pp. 59-64, 2008. This study was conducted to study the real-time sports activity classification and monitoring using single waist mounted tri-axial accelerometer. This monitoring system detects events of sports activities such as walking, running, cycling, transitions between movements, resting and emergency event of falls. Accelerometer module was developed small and easily attachable on waist using wireless communication system which does not constrain sports activities. The sensor signal was transferred to PC and each movement pattern was classified using the developed algorithm in real-time environment. To evaluate proposed algorithm, experiment was performed with several sports activities such as walking, running, cycling movement for 100sec each and falls, transition movements(sit to stand, lie to stand, stand to sit, lie to sit, stand to lie and sit to lie) for 20 times each with 5 healthy subjects. The results showed that successful detection rate of the system for all activities was 95.4%. In this study, through sports activity monitoring. it was possible to classify accurate sports activities and to notify emergency event such as falls. For further study, the accurate energy consumption algorithm for each sports activity is under development.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.242-250
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• 2016

This study was conducted to investigate whether Stroop-related cognitive task will be affected according to the preceding affective valence factored by matchedness in response time(RT) and whether facial recognition will be indexed by specific event-related potentials(ERPs) signature in normal person as in patients suffering from affective disorder. ERPs primed by subliminal(30ms) facial stimuli were recorded when presented with four pairs of affect(positive or negative) and cognitive task(matched or mismatched) to get ERP effects(N2 and P300) in terms of its amplitude and peak latency variations. Behavioral response analysis based on RTs confirmed that subliminal affective stimuli primed the target processing in all affective condition except for the neutral stimulus. Additional results for the ERPs performed in the negative affect with mismatched condition reached significance of emotional-face specificity named N2 showing more amplitude and delayed peak latency compared to the positive counterpart. Furthermore the condition shows more positive amplitude and earlier peak latency of P300 effect denoting cognitive closure than the corresponding positive affect condition. These results are suggested to reflect that negative affect stimulus in subliminal level is automatically inhibited such that this effect had influence on accelerating detection of the affect and facilitating response allowing adequate reallocation of attentional resources. The functional and cognitive significance with these findings was implied in terms of subliminal effect and affect-related recognition modulating the cognitive tasks.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.727-743
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• 2003

This paper proposes an automatic highlight building algorithm for soccer video by using the structural characteristics of broadcasted sports video that an interesting (or important) event (such as goal or foul) in sports video has a continuous replay shot surrounded by gradual shot change effect like wipe. This shot editing rule is used in this paper to analyze the structure of broadcated soccer video and extracts shot involving the important events to build a highlight. It first uses the spatial-temporal image of video to detect wipe transition effects and zoom out/in shot changes. They are used to detect the replay shot. However, using spatial-temporal image alone to detect the wipe transition effect requires too much computational resources and need to change algorithm if the wipe pattern is changed. For solving these problems, a two-pass detection algorithm and a pixel sub-sampling technique are proposed in this paper. Furthermore, to detect the zoom out/in shot change and replay shots more precisely, the green-area-ratio and the motion energy are also computed in the proposed scheme. Finally, highlight shots composed of event and player shot are extracted by using these pre-detected replay shot and zoom out/in shot change point. Proposed algorithm will be useful for web services or broadcasting services requiring abstracted soccer video.

• Convergence Security Journal
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• v.17 no.3
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• pp.41-49
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• 2017

According to a research by global IT market research institute IDC, CSIM(Converged Security Information Management) market of Korea was estimated to be 1.7 trillion KRW in 2010, and it has grown approximately 32% every year since. IDC forcasts this size to grow to 12.8 trillion KRW by 2018. Moreover, this case study exemplifies growing importance of CSIM market worldwide. Traditional CSIM solution consists of various security solutions(e.g. firewall, network intrusion detection system, etc.) and devices(e.g. CCTV, Access Control System, etc.). With this traditional solution, the the data collected from these is used to create events, which are then used by the on-site agents to determine and handle the situation. Recent development of IoT industry, however, has come with massive growth of IoT devices, and as these can be used for security command and control, it is expected that the overall amount of event created from these devices will increase as well. While massive amount of events could help determine and handle more situations, this also creates burden of having to process excessive amount of events. Therefore, in this paper, we discuss potential events that can happen in CSIM system and classify them into 3 groups, and present a model that can categorize and process these events effectively to increase overall efficieny of CSIM system.

• Journal of Plant Biotechnology
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• v.45 no.1
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• pp.77-82
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• 2018

In South Korea, LM crops are not allowed to grow locally, but have been allowed to be imported as food and feed purposes. Currently, the typical LMO imports are continuously increasing in the region of South Korea. In 2014, we carried out a review of the environmental release monitoring of LM maize (Zea mays L.) in Gyeonggi-do of South Korea, and analyzed volunteer samples using strip test kits and polymerase chain reaction (PCR) methods. We thereby collected 44 volunteers of released LM maize in 169 locations around ports, from roadsides, feed factories and stockbreeding farmhouses. We found 4 positive samples at 3 sites using strip test kits. Based on the PCR analysis, the LM maize plants were found using event-specific primers. These results suggested that our monitoring is necessary to detect the presence of released LM maize in the natural environment of South Korea.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.96-110
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• 2020

Recently, due to global warming, it is easily exposed to various disasters such as fire, flood, and earthquake. In particular, large-scale disasters have continuously been occurring in crowded areas such as traditional markets, facilities for the elderly and children, and public facilities where various people stay. Purpose: This study aims to detect a fire occurred in crowded facilities early in the event to analyze and provide an optimal evacuation route using big data and advanced technology. Method: The researchers propose a new algorithm through context-aware 3D object model technology and A* algorithm optimization and propose a scenario-based optimal evacuation route selection technique. Result: Using the HPA* E algorithm, the evacuation simulation in the event of a fire was reproduced as a 3D model and the optimal evacuation route and evacuation time were calculated for each scenario. Conclusion: It is expected to reduce fatalities and injuries through the evacuation induction technique that enables evacuation of the building in the shortest path by analyzing in real-time via fire detection sensors that detects the temperature, flame, and smoke.