• Journal of Veterinary Clinics
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• v.21 no.1
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• pp.29-34
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• 2004

Serial ultrasonographic examinations were daily performed on 12 Shih-tzu bitches from 15 days after ovulation until parturition to determine the time of first detection and ultrasonographic appearance of the fetal and extra-fetal structures and to determine the size of gestational structures. Gestational age was timed from the day of ovulation (day 0), which was estimated to occur when plasma progesterone concentration was first increased above 4.0 ng/ml. The gestational length in 12 Shih-tzu bitches was 62.1 (range:60-64) days. The initial detection of the fetal and extra-fetal structures were: gestational sac at days 19.2 (16-21); zonary placenta in the uterine wall at days 25.9 (24-28); yolk sac membrane at days 24.1 (22-26); amniotic membrane at days 26.6 (24-28); embryo initial detection at days 22.3 (21-24); heartbeat at days 23.8 (23-25); limb buds at days 29.3 (27-31); fetal movement at days 32.3 (31-34); stomach at days 32.6 (29-34); urinary bladder at days 33.1 (29-35); skeleton at days 35.9 (34-38) and kidney at days 45.8 (45-48).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1690-1696
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• 1999

Video sequence should be hierachically classified for the content-based retrieval. Cut detection algorithm is an essential process to classify shots. It is generally difficult for cut detection algorithms to detect cut points since a current frame is compared with a previous one, because movement of camera or object made adrupt scene change. We reduce ratio of failed cut detection so that compare the difference between frames of predicted cut point and their neighbors. In this paper, first we get predicted cut point, then we judge that the predicted cut point is true point or not. And we extracted DC images in MPEG video sequence for comparison. As a result of experiments. We confirmed that the cut detection ratio of the proposed algorithm is higher than of any other algorithms.

• Smart Media Journal
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• v.5 no.3
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• pp.93-98
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• 2016

The literal meaning of loitering is "to lingering aimlessly or as if aimless in or about a place". And most criminals show this kind of act before they actually commit crime. Therefore, detecting this kind of loitering can effectively prevent a variety of crime. In this paper, we propose a loitering-detection algorithm using the Raspberry Pi. Proposed algorithm uses an adaptive difference image to detect moving objects and morphology opening operation to enhance the accuracy of detection. The loitering- behavior is being detected by using the center of gravity of the object to see the changes of angle; and pixel movement distance to determine the height of the object. When the loitering-behavior is detected, it outputs the alarm to tell the users by using the Raspberry Pi.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.481-493
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• 2011

As the development of mobile devices, mobile phones are equipped with many features. Video-call feature is one of them. In this paper, we present distance measurement between speaker and smart-phone using multiple color spaces and multiple thresholds. first, detect face based on skin color information. and second, measure distance between speaker and smart-phone using the detected face region. Especially, the first considering point in the development of face area detection is real-time processing and the second point is robustness to solve the problems of face detection errors due to rapid change of object movement, lighting and background between adjacent frames.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.167-177
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• 2024

With advancements in satellite technology, interest in target detection and identification is increasing quantitatively and qualitatively. Synthetic Aperture Radar(SAR) images, which can be acquired regardless of weather conditions, have been applied to various areas combined with machine learning based detection algorithms. However, conventional studies primarily focused on the detection of stationary targets. In this study, we proposed a method to identify moving targets using an algorithm that integrates sub-aperture SAR images and cosine similarity calculations. Utilizing a transformer-based deep learning target detection model, we extracted the bounding box of each target, designated the area as a region of interest (ROI), estimated the similarity between sub-aperture SAR images, and determined movement based on a predefined similarity threshold. Through the proposed algorithm, the quantitative evaluation of target identification capability enhanced its accuracy compared to when training with the targets with two different classes. It signified the effectiveness of our approach in maintaining accuracy while reliably discerning whether a target is in motion.

• ETRI Journal
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• v.40 no.4
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• pp.522-530
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• 2018

In this study, we developed a robotic walker that actively controls its speed and direction of movement according to the user's gait intention. Sensor fusion between a low-cost light detection and ranging (LiDAR) sensor and inertia measurement units (IMUs) helps determine the user's gait intention. The LiDAR determines the walking direction by detecting both knees, and the IMUs attached on each foot obtain the angular rate of the gait. The user's gait intention is given as the directional angle and the speed of movement. The two motors in the robotic walker are controlled with these two variables, which represent the user's gait intention. The estimated direction angle is verified by comparison with a Kinect sensor that detects the centroid trajectory of both the user's feet. We validated the robotic walker with an experiment by controlling it using the estimated gait intention.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.594-596
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• 2006

In this thesis, we propose a algorithm that is able to detect a user's motion. A minute noise of accelerometer arises cumulative error in case of converting velocity and distance, which makes it difficult to detect movement of sensor. We use a lowpass filter, ALS algorithm, and motion detection block to minimize such cumulative error. we experiment using a motion-detective module which is composed of accelerometer, micro-controller, and serial interface. Our scheme is capable of detecting such as up, down, left, right, forward, and backward movement of the module. It is expected that our scheme is applied to a game controller or user interface of a next generation PC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.713-716
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• 2000

In this study, we present a mobile robot for ultrasonic scanning of weldment. magnetic Caterpillar mechanism is selected in order to travel on the inclined surface and vertical wall. A motion control board and motor driver are developed to control four DC-servo motors. A virtual device driver is also developed for the purpose of communicating between the control board and a host PC with Dual 'port ram. To provide the mobile robot with stable and accurate movement, PID control algorithm is applied to the mobile robot control. And a vision system for detecting the weld-line are developed with laser slit beam as a light source. In the experiments, movement of the mobile robot is tested inclined on a surface and a vertical wall.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.70-74
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• 2018

In order to ensure the flow of goods available and more flexible, reduce labor costs, many factories and industrial zones around the world are gradually moving to use automated solutions. One of them is to use Automated guided vehicles (AGV). Currently, there are a line tracing method as an AGV operating method, and a method of estimating the current position of the AGV and matching with a factory map and knowing the moving direction of the AGV. In this paper, we propose ceiling Light and guided line based moving direction estimation algorithm using multi-camera on the AGV in smart factory that can operate stable AGV by compensating the disadvantages of existing AGV operation method. The proposed algorithm is able to estimate its position and direction using a general - purpose camera instead of a sensor. Based on this, it can correct its movement error and estimate its own movement path.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.1
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• pp.72-80
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• 1988

In this paper, new adaptive interframe filtering methods have been proposed to separate the luminance and chrominance components in NTSC composite signals. In thess methods, the composite signals are adaptivelty processed in three dimensions according to the local change or movement of the picture. For interframe processing, two algorithms have been proposed which adapt three filters in the horizontal, vertical and temporal directions to the magnitude f detection signal dependent upon both the movement and local change of the picture. The three kind of filters have been used at the sampling rate of four times the subcarrier frequency. The various quantitative measures have been introduced to compare the objective performance of the conventional methods and that of proposed ones by computer simulation.