• Korean Journal of Psychosomatic Medicine
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• v.7 no.2
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• pp.241-246
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• 1999

Delirium is a syndrome characterized by impairement of consciousness, disorientation, disturbance of sleep-wake cycle, memory impairement, disturbance of perception. It is induced by many causes, which are CNS diseases(head trauma, vascular disease, brain tumor, etc), medical diseases(metabolic disorder, endocrine disturbance, cardiovascular disease) and drugs(anticholinergics, anticonvulsant, antipsychotics, cimetidine etc). Transdermal scopolamine which is usually used to prevent motion sickness has anticholinergic property, and so it can induce delirium. The authors report two cases of delirium induced by transdermal scopolamine. The cases shared common characteristics which were as follows : 1. All of two patients were elderly women. 2. Delirium symptom was abruptly occurred during trip after attaching scopolamine patches. 3. Delirium symptom was rapidly improved within 2-3 days. It is important to educate for both users and managers about directions for transdermal scopolamine patch usage to prevent delirium. And careful history taking is needed to diagnose delirium induced by transdermal scopolamine accurately.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.692-702
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• 2009

Real-time virtual studios which could run only on expensive workstations are now available for personal computers thanks to the recent development of graphics hardware. Nevertheless, graphics are rendered off-line in the post production stage in film or TV drama productions, because the graphics' quality is still restricted by the real-time hardware. Software-based camera tracking methods taking only the source video into account take much computation time, and often shows unstable results. To overcome this restriction, we propose a system that stores camera motion data from sensors at shooting time as common virtual studios and uses them in the post production stage, named as POVIS(post virtual imaging system). For seamless registration of graphics onto the camera video, precise zoom lens calibration must precede the post production. A practical method using only two planar patterns is used in this work. We present a method to reduce the camera sensor's error due to the mechanical mismatch, using the Kalman filter. POVIS was successfully used to track the camera in a documentary production and saved much of the processing time, while conventional methods failed due to lack of features to track.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.645-651
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• 2012

In this paper, we propose a new method for stabilizing the image captured from a camera mounted on a buoy robot. In this study, in order to solve the problem of cumulative errors and noise produced by a general gyro sensor measuring the orientation angle of the buoy robot, we propose new method for stabilizing the image. In this method, image processing techniques are combined with a newly designed target mounting mechanism that adapts to wave fluctuations. New target extraction and angle estimation techniques are introduced, along with the new mounting mechanism used for the camera and the target, which produce a stabilized image even if the buoy robot is on fluctuating waves.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1564-1572
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• 2011

In this paper, we propose a new histogram based hand recognition algorithm for augmented reality. Hand recognition system makes it possible a useful interaction between an user and computer. However, there is difficulty in vision-based hand gesture recognition with viewing angle dependency due to the complexity of human hand shape. A new hand recognition system proposed in this paper is based on the features from hand geometry. The proposed recognition system consists of two steps. In the first step, hand region is extracted from the image captured by a camera and then hand gestures are recognized in the second step. At first, we extract hand region by deleting background and using skin color information. Then we recognize hand shape by determining hand feature point using histogram of the obtained hand region. Finally, we design a augmented reality system by controlling a 3D object with the recognized hand gesture. Experimental results show that the proposed algorithm gives more than 91% accuracy for the hand recognition with less computational power.

• Journal of Science Education
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• v.42 no.3
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• pp.334-351
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• 2018

Teacher's gaze empathy for students in science class is considered to be effective in enhancing the learning effect. Thus, studies on gaze empathy have been conducted, but most of the studies are just to reveal the characteristics of gaze. Therefore, it is necessary to deal with a research to raise the level of science teacher's gaze empathy. The purpose of this study is to develop an instruction consulting strategy based on eye tracking for improving science teachers' gaze empathy. In this study, we selected and analyzed relevant literature on teacher's gaze empathy. We also designed a consulting strategy and then revised the design through expert reviews on validity and reliability. The developed consulting strategy was aimed to improve science teacher's gaze empathy and set quantitative goal based on eye tracking. The consulting strategy consisted of six steps: preparation for consulting, measurement and analysis of teacher's gaze empathy, instruction and feedback of gaze empathy, training for improving gaze empathy, evaluation of consulting result, and completion of the consulting. In addition, the consultation was completed or repeated again through the measurement and evaluation of gaze empathy using eye tracking. The developed consulting strategy has a value in that it provides an alternative with quantitative diagnosis and prescription for improving gaze empathy. The strategy can contribute to enhance teacher professional competency through the analysis of teaching behavior.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.798-809
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• 2014

Video stabilization is performed to remove unexpected shaky and irregular motion from a video. It is often used as preprocessing for robust feature tracking and matching in video. Typical video stabilization algorithms are developed to compensate motion from surveillance video or outdoor recordings that are captured by a hand-help camera. However, since the vehicle video contains rapid change of motion and local features, typical video stabilization algorithms are hard to be applied as it is. In this paper, we propose a novel approach to compensate shaky and irregular motion in vehicle video using linear regression model and vertical projection histogram matching. Towards this goal, we perform vertical projection histogram matching at each sub region of an input frame, and then we generate linear regression model to extract vertical translation and rotation parameters with estimated regional vertical movement vector. Multiple binarization with sub-region analysis for generating the linear regression model is effective to typical recording environments where occur rapid change of motion and local features. We demonstrated the effectiveness of our approach on blackbox videos and showed that employing the linear regression model achieved robust estimation of motion parameters and generated stabilized video in full automatic manner.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.83-93
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• 2013

Recently, the SURF algorithm, which is conjugated for object tracking system as part of many computer vision applications, is a well-known scale- and rotation-invariant feature detection algorithm. The SURF, due to its high computational complexity, there is essential to develop a hardware accelerator in order to be used on an IP in embedded environment. However, the SURF requires a huge local memory, causing many problems that increase the chip size and decrease the value of IP in ASIC and SoC system design. In this paper, we proposed a way to design a SURF algorithm in hardware with greatly reduced local memory by partitioning the algorithms into several Sub-IPs using external memory and a DMA. To justify validity of the proposed method, we developed an example of simplified object tracking algorithm. The execution speed of the hardware IP was about 31 frame/sec, the logic size was about 74Kgate in the 30nm technology with 81Kbytes local memory in the embedded system platform consisting of ARM Cortex-M0 processor, AMBA bus(AHB-lite and APB), DMA and a SDRAM controller. Hence, it can be used to the hardware IP of SoC Chip. If the image processing algorithm akin to SURF is applied to the method proposed in this paper, it is expected that it can implement an efficient hardware design for target application.

• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.202-216
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• 2022

Animals exhibit certain behaviors and movement patterns as they react to their internal needs, external stimuli, and surrounding environments. They have a bounded range in which they mostly spend their time, and it is referred to as a home range. Based on the fact that the home range is a critical area for the survival and preservation of species, there has been a growing body of research on developing more precise home range estimation methods to use the estimated ranges as a ground for establishing an effective conservation policy since the early 1940s. Recent rapid advancements in telemetry technology that resulted in the presence of autocorrelation between locations with short time intervals revealed the limitations of the existing estimators. Many novel estimators have been developed to compensate for it by incorporating autocorrelation in calculating home ranges. However, studies on the animal home range are still in their early stage in Korea, and newly developed methodologies have not yet been adopted. Therefore, this study aims to introduce the foreign home range estimation methods and foster domestic research activities on home ranges. Firstly, we compared and contemplated seven estimators by categorizing them into geometrical and statistical methodologies and then divided them into estimators that assume independent observations and those that consider autocorrelation in each category. After that, the home ranges of black-tailed gulls (Larus crassirostris) were calculated using GPS tracking data for the month of June and derived home range estimators by applying the methodology introduced in this study. We analyzed and compared the results to discuss the strengths and weaknesses of each method. Lastly, we proposed a guideline that can help researchers choose an appropriate estimator for home range calculation based on the animal location data characteristics and analysis purpose.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.161-166
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• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.