• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1089-1098
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• 2009

Recently, many researches of making more comfortable input device based on gaze detection technology have been performed in human computer interface. Previous researches were performed on the computer environment with a large sized monitor. With recent increase of using mobile device, the necessities of interfacing by gaze detection on mobile environment were also increased. In this paper, we research about the gaze detection method by using UMPC (Ultra-Mobile PC) and an embedded camera of UMPC based on face and facial feature detection by AAM (Active Appearance Model). This paper has following three originalities. First, different from previous research, we propose a method for tracking user's gaze position in mobile device which has a small sized screen. Second, in order to detect facial feature points, we use AAM. Third, gaze detection accuracy is not degraded according to Z distance based on the normalization of input features by using the features which are obtained in an initial user calibration stage. Experimental results showed that gaze detection error was 1.77 degrees and it was reduced by mouse dragging based on the additional facial movement.

• Annals of Clinical Neurophysiology
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• v.4 no.2
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• pp.137-139
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• 2002

Background : Subtle status epilepticus (SE) is an end-stage of convulsive SE. This phenomenon might be a clinical expression of neuronal exhaustion caused by sustained electrical discharges. As subtle SE may show diverse clinical features, early detection depends on clinical suspicion. Case : A 68-year-old woman was presented with repetitive involuntary movement of right limbs after two generalized tonic-clonic seizures. She experienced right middle cerebral artery infarction 4 months ago, and after the event, left side hemiplegia sustained. These seizures were first-ever after the cerebral infarction. Orientation and verbal responses were fairly preserved but general cognitive function was minimally slowed. During the video-EEG monitoring, repetitive sharp waves were noted in the right hemisphere and these sharp waves occasionally spread to the contralateral side. Her right side involuntary movement was identifiable when the epileptic discharges were found on her right hemisphere. Conclusion : We suggested that this unexpected convulsive movement is a reflection of earlier exhaustion in the right hemisphere or deefferentation of right hemisphere because of preexisting neuronal damage.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.6
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• pp.80-86
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• 2001

This paper proposes a precision displacement measuring method of detecting fringe movement of interferograms with a nanometric resolution. It is well known that the laser interferometer plays a useful and essential role in scientific and industrial application, but they have such error sources as an unequal gain of detectors, imbalanced beams, and lack of quadrature. These error sources degrade the accuracy of the interferometer. However, the fringe movement of interferograms has little relation with these error sources. In order to investigate performance of the proposed method. analysis and simulation were executed over random noise and wavefront distorion. Results of the simulation show that the proposed method is robust against these errors. Experiment was implemented to verify this method.

• Journal of the Optical Society of Korea
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• v.11 no.1
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• pp.26-33
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• 2007

We demonstrate the capability of differential-phase optical coherence tomography (DP-OCT) to detect superparamagnetic iron oxide (SPIO) nanoparticles taken up by liver parenchymeal macrophages (Kupffer cells). We apply an external time-varying high-intensity focused magnetic field. Our experiments demonstrate a novel diagnostic modality to detect macrophages that have taken up SPIO nanoparticles. Magnetic force acting on the nanoparticles was varied by applying a sinusoidal current to a solenoid containing a conical iron core that substantially increased and focused the magnetic field strength ($B_{max}$ = 2 Tesla). $ApoE^{-/-}$ mice were sacrificed 2 days post intravenous injections of different SPIO doses (1.0, and 0.1 mmol Fe/kg body weight). Livers of $ApoE^{-/-}$ mice with and without injection of SPIO nanoparticles were investigated using DP-OCT, which detects tissue movement with nanometer resolution. Frequency response of iron-laden liver movement was twice the stimulus frequency. Movement was not observed in livers of control mice. Results of our experiments indicate DP-OCT is a candidate methodology to detect tissue based macrophages containing SPIO nanoparticles excited by an external focused magnetic field.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.664-667
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• 2009

There is strong demand to create wearable PC systems that can support the user outdoors. When we are outdoors, our movement makes it impossible to use traditional input devices such as keyboards and mice. We propose a hand gesture interface based on image processing to operate wearable PCs. The semi-transparent PC screen is displayed on the head mount display (HMD), and the user makes hand gestures to select icons on the screen. The user's hand is extracted from the images captured by a color camera mounted above the HMD. Since skin color can vary widely due to outdoor lighting effects, a key problem is accurately discrimination the hand from the background. The proposed method does not assume any fixed skin color space. First, the image is divided into blocks and blocks with similar average color are linked. Contiguous regions are then subjected to hand recognition. Blocks on the edges of the hand region are subdivided for more accurate finger discrimination. A change in hand shape is recognized as hand movement. Our current input interface associates a hand grasp with a mouse click. Tests on a prototype system confirm that the proposed method recognizes hand gestures accurately at high speed. We intend to develop a wider range of recognizable gestures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.124-132
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• 2022

With growth in intelligence of mobile robots, interaction with humans is emerging as a very important issue for mobile robots and the pedestrian tracking technique following the designated person is adopted in many cases in a way that interacts with humans. Among the existing multi-object tracking techniques for pedestrian tracking, Simple Online and Realtime Tracking (SORT) is suitable for small mobile robots that require real-time processing while having limited computational performance. However, SORT fails to reflect changes in object detection values caused by the movement of the mobile robot, resulting in poor tracking performance. In order to solve this performance degradation, this paper proposes a more stable pedestrian tracking algorithm by correcting object tracking errors caused by robot movement in real time using wheel odometry information of a mobile robot and dynamically managing the survival period of the tracker that tracks the object. In addition, the experimental results show that the proposed methodology using data collected from actual mobile robots maintains real-time and has improved tracking accuracy with resistance to the movement of the mobile robot.

• 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.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.58-66
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• 2020

The blur effect, which is generated by various external factors such as out-of-focus and object movement, degrades high-frequency components in the original sharp image. Based on this observation, we propose a novel method for blur detection using textural features. Specifically, the proposed method simultaneously adopts learning-based and watershed-based textural features, which effectively detect the blur on various situations. Moreover, we employ the region-based refinement to improve the processing time while also increasing detection accuracy. Experimental results demonstrate that the proposed method provides the competitive performance compared to previous approaches in literature.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.3
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• pp.175-181
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• 2004

The protection against transient instability and consequent out-of-step condition is a major concern for the utility industry. Unstable system may cause serious damage to system elements such as generators and transmission lines. Therefore, out-of-step detection is essential to operate a system safely. The detection of out-of-step is generally based upon the rate of movement of the apparent impedance. However such relay monitors only the apparent impedance which may not be sufficient to correctly detect all forms of out-of-step and cannot cope with out-of-step for a more complex type of instability such as very fast power swing. This paper presents the out-of-step detection algorithm using voltage frequency deviation. The digital filters based on discrete Fourier transforms (DFT) to calculate the frequency of a sinusoid voltage are used, and the generator angle is estimated using the deviation of the calculated frequency component of the voltage. The proposed out-of-step algorithm is based on the assessment of a transient stability using equal area criterion. The proposed out-of-step algorithm is verified and tested by using EMTP MODELS.