• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1919-1925
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• 2013

This paper proposes a background image extraction algorithm for traffic environment analysis in a school zone. The proposed algorithm solves the problems by level changes and stationary objects to be occurred frequently in traffic environment. For the former, it renews rapidly the background image toward the current frame using a fast Sima-Delta algorithm and for the latter, it excludes the stationary objects from the background image by detecting dynamic regions using a just previous frame and a background image averaged for a long time. The results of experiments show that the proposed algorithm adapts quickly itself to level change well, and reduces about 40~80% of SAD in background region in comparison with the conventional algorithms.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.43-49
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• 2022

FMCW(Frequency Modulated Continuous Wave) Radar is very useful for vehicle collision warning system and autonomous driving sensor. In this paper, the design and implementation of FMCW radar based on two chip MMIC developed as an autonomous driving sensor was described. Especially, generation of frame-based and chirp-based waveform generation and signal processing are mixed to have the strength of maximum detection speed and compensation of speed. This implemented system was analyzed for performance and commercialization potential through lab. test and driving test in K-city.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• ETRI Journal
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• v.40 no.2
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• pp.188-196
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• 2018

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.157-157
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• 2017

To minimize the damage by wild birds and acquire the benefits such as protection against weeds and maintenance of water content in soil, the mulching black color vinyl after seeding should be carried out. Non-contact and non-destructive methods that can continuously determine the locations are necessary. In this study, a crop position detection method was studied that uses infrared thermal image sensor to determine the cotyledon position under vinyl mulch. The moving system for acquiring image arrays has been developed for continuously detecting crop locations under plastic mulching on the field. A sliding mechanical device was developed to move the sensor, which were arranged in the form of a linear array, perpendicular to the array using a micro-controller integrated with a stepping motor. The experiments were conducted while moving 4.00 cm/s speed of the IR sensor by the rotational speed of the stepping motor based on a digital pulse width modulation signal from the micro-controller. The acquired images were calibrated with the spatial image correlation. The collected data were processed using moving averaging on interpolation to determine the frame where the variance was the smallest in resolution units of 1.02 cm. Non-linear integral interpolation was one of method for analyzing the frequency using the normalization image and then arbitrarily increasing the limited data value of $16{\times}4pixels$ in one frame. It was a method to relatively reduce the size of overlapping pixels by arbitrarily increasing the limited data value. The splitted frames into 0.1 units instead of 1 pixel can propose more than 10 times more accurate and original method than the existing correction method. The non-integral calibration method was conducted by applying the subdivision method to the pixels to find the optimal correction resolution based on the first reversed frequency. In order to find a correct resolution, the expected location of the first crop was indicated on near pixel 4 in the inversion frequency. For the most optimized resolution, the pixel was divided by 0.4 pixel instead of one pixel to find out where the lowest frequency exists.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.163-167
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• 2017

Utilizing a unique capability of AKARI that allows deep spectroscopy at $2.5-5.0{\mu}m$, we performed a spectroscopy study of more than 200 quasars through one of the AKARI mission programs, QSONG (Quasar Spectroscopic Observation with NIR Grism). QSONG targeted 155 high redshift (3.3 < z < 6.42) quasars and 90 low redshift active galactic nuclei (0.002 < z < 0.48). In order to provide black hole mass estimates based on the rest-frame optical spectra, the high redshift part of QSONG is designed to detect the $H{\alpha}$ line and the rest-frame optical spectra of quasars at z > 3.3. The low redshift part of QSONG is geared to uncover the rest-frame $2.5-5.0{\mu}m$ spectral features of active galactic nuclei to gain useful information such as the dust-extinction-free black hole mass estimators based on the Brackett lines and the temperatures of the hot dust torus. We outline the program strategy, and present some of the scientific highlights from QSONG, including the detection of the $H{\alpha}$ line from a quasar at z > 4.5 which indicates a rigorous growth of black holes in the early universe, and the $Br{\beta}$-based black hole mass estimators and the hot dust temperatures (~ 1100 K) of low redshift AGNs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1283-1288
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• 2011

In this paper we collect data concerning sleep environments in a bedroom and analyze the relationship between the collected condition data and sleep. In addition, this paper detects scene changes from the subjects in a sleeping state and presents the physical conditions, reactions during sleep, and physical sensations and stimuli. To detect scene changes in image sequences, we used color histogram for the difference between the preceding frame and the current frame. In addition, to extract the tossing and turning for different situations, the subjects were instructed to enter the level of fatigue, the level of drinking, and the level of stomach emptiness. For the sleep experiment system, we used the H-MOTE2420 Sensor composed of temperature, humidity, and light sensors. This paper is intended to provide the best sleep environment that enhances sleep quality, thus inducing people today to get regular and comfortable sleep.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.65-76
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• 2005

A digital video is a very long data that requires large-capacity storage space. As such, prior to watching a long original video, video watchers want to watch a summarized version of the video. In the field of sports, in particular, highlights videos are frequently watched. In short, a highlights video allows a video watcher to determine whether the highlights video is well worth watching. This paper proposes a scheme for creating soccer video highlights using the structural features of captions in terms of time and space. Such structural features are used to extract caption frame intervals and caption keyframes. A highlights video is created through resetting shots for caption keyframes, by means of logical indexing, and through the use of the rule for creating highlights. Finally, highlights videos and video segments can be searched and browsed in a way that allows the video watcher to select his/her desired items from the browser.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.39-47
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• 2005

This paper proposes and analyzes the MAC(Message Authentication Code) algorithm that is used for the transition integrity and the entity authentication of message. The MAC algorithm uses the DES algorithm which has 64-bit block and 56-bit key and we compare the security according to 64-bit and 32-bit length of MAC value. Moreover, we use the SEED algorithm which has 128-bit block and 128-bit key and compare the security according to 128-bit and 64-bit length of MAC value. We analyze the security the forgery attack according to length of message and length of MAC value. this paper, a coarse-to-fine optical flow detection method is proposed. Provided that optical flow gives reliable approximation to two-dimensional image motion, it can be used to recover the three-dimensional motion. but usually to get the reliable optical flows are difficult. The proposed algorithm uses Horn's algorithm (or detecting initial optical flow, then Thin Plate Spline is introduced to warp a image frame of the initial optical flow to the next image frame. The optical flow for the warped image frame is again used iteratively until the mean square error between two image sequence frames is lowered. The proposed method is experimented for the real moving Picture image sequence. The proposed algorithm gives dense optical flow vectors.