• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.93-95
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• 2010

기존의 비디오 부호화 표준에서는 참조영상을 보간하여 해상도를 증가시킨 후, 고정된 움직임 벡터 해상도로 영상 전체를 부호화 한다. 참조 영상의 해상도를 증가시킨 만큼 움직임 보상에 의하여 예측에러가 줄어들지만, 움직임 벡터 해상도가 증가한 만큼 움직임 벡터의 부호화 비트량이 증가한다. 고정된 해상도의 움직임 벡터로 부호화하는 경우, 영상의 지역적인 움직임 특성이 다른 경우 부호화 효율이 떨어질 수 있다. 따라서 본 논문에서는 기존의 비디오 부호화 표준들이 영상의 지역적인 특성을 고려하지 않고 고정된 해상도의 움직임 벡터를 사용하여 부호화하는 문제점을 극복하기 위하여 슬라이스 단위로 1/4 화소 해상도 또는 1/8 화소 해상도 또는 움직임 벡터 단위로 적응적으로 화소 해상도를 결정하는 것 중에서 최적의 슬라이스 움직임 벡터 해상도를 결정하여 부호화하는 방법을 제안한다. 제안한 방법을 사용하여 부호화하면 움직임 벡터의 부호화 비트의 낭비를 막고, 예측 에러도 줄어들어 부호화 효율을 높일 수 있다. 제안하는 방법을 사용하여 부호화 하는 경우 H.264/AVC와 비교하여 평균 1.97%의 BD-RATE을 감소한다.

• Journal of Astronomy and Space Sciences
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• v.34 no.1
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• pp.19-30
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• 2017

The optical wide-field patrol network (OWL-Net) is a Korean optical surveillance system that tracks and monitors domestic satellites. In this study, a batch least squares algorithm was developed for optical measurements and verified by Monte Carlo simulation and covariance analysis. Potential error sources of OWL-Net, such as noise, bias, and clock errors, were analyzed. There is a linear relation between the estimation accuracy and the noise level, and the accuracy significantly depends on the declination bias. In addition, the time-tagging error significantly degrades the observation accuracy, while the time-synchronization offset corresponds to the orbital motion. The Cartesian state vector and measurement bias were determined using the OWL-Net tracking data of the KOMPSAT-1 and Cryosat-2 satellites. The comparison with known orbital information based on two-line elements (TLE) and the consolidated prediction format (CPF) shows that the orbit determination accuracy is similar to that of TLE. Furthermore, the precision and accuracy of OWL-Net observation data were determined to be tens of arcsec and sub-degree level, respectively.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Journal of Broadcast Engineering
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• v.14 no.1
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• pp.3-14
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• 2009

Currently, H.264/AVC is used in many multimedia applications. Also, The MPEG-2 main profile which supports B pictures for bi-directional motion prediction is widely used in applications such as HDTV and DVD’s. Therefore, transcoding the MPEG-2 main profile to the H.264/AVC baseline is necessary for universal multimedia access. In this transcoding architecture including the GOP structure conversion, the proposed algorithms adopt the adaptive search range selection through the linearity test of a predictive motion vector and adaptive mode selection using the reference region complexity information. The proposed algorithms extremely reduce the computational complexity while maintaining the video quality.

• Atmosphere
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• v.27 no.1
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• pp.93-104
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• 2017

The effect of Advanced Microwave Sounding Unit-A (AMSU-A) observations on the short-range forecast in East Asia (EA) was investigated for the Northern Hemispheric (NH) summer and winter months, using the Forecast Sensitivity to Observations (FSO) method. For both periods, the contribution of radiosonde (TEMP) to the EA forecast was largest, followed by AIRCRAFT, AMSU-A, Infrared Atmospheric Sounding Interferometer (IASI), and the atmospheric motion vector of Communication, Ocean and Meteorological Satellite (COMS) or Multi-functional Transport Satellite (MTSAT). The contribution of AMSU-A sensor was largely originated from the NOAA 19, NOAA 18, and MetOp-A (NOAA 19 and 18) satellites in the NH summer (winter). The contribution of AMSU-A sensor on the MetOp-A (NOAA 18 and 19) satellites was large at 00 and 12 UTC (06 and 18 UTC) analysis times, which was associated with the scanning track of four satellites. The MetOp-A provided the radiance data over the Korea Peninsula in the morning (08:00~11:30 LST), which was important to the morning forecast. In the NH summer, the channel 5 observations on MetOp-A, NOAA 18, 19 along the seaside (along the ridge of the subtropical high) increased (decreased) the forecast error slightly (largely). In the NH winter, the channel 8 observations on NOAA 18 (NOAA 15 and MetOp-A) over the Eastern China (Tibetan Plateau) decreased (increased) the forecast error. The FSO provides useful information on the effect of each AMSU-A sensor on the EA forecasts, which leads guidance to better use of AMSU-A observations for EA regional numerical weather prediction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.321-329
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• 2008

H.264/AVC video coding standard enables a considerably higher improvement in coding efficiency compared with previous standards such as MPEG-2, H.263 and MPEG-4. To achieve this, for each macro-block in H.264/AVC, Rate-Distortion Optimization (RDO) technique is employed to select the best motion vector, reference frame, and macro-block mode. As a result, computational complexity is increased significantly whereas RDO achieve higher improvement. This paper presents fast intra mode selection algorithm based on constraints of frequency characteristics which are derived from intra coding modes of H.264/AVC. First of all, we observe the features of each intra mode through the frequency analysis of image. And then proposed Frequency Error Costs (FECs) are calculated to select the best mode which has minimum cost. Computational complexity is considerably reduced because rate-distortion costs only calculate the candidate modes which are set of best mode and its neighbouring two modes. Experimental results show that proposed algorithm reduces the complexity dramatically maintaining the rate-distortion performance compared with H.264/AVC reference software.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.53-60
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• 2009

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking; and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.603-607
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• 2004

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.