• Korean Journal of Remote Sensing
• /
• v.26 no.6
• /
• pp.721-730
• /
• 2010

In satellite remote sensing, irregular temporal sampling is a common feature of geophysical and biological process on the earth's surface. Lee (2008) proposed a feed-back system using a harmonic model of single period to adaptively reconstruct observation image series contaminated by noises resulted from mechanical problems or environmental conditions. However, the simple sinusoidal model of single period may not be appropriate for temporal physical processes of land surface. A complex model of multiple periods would be more proper to represent inter-annual and inner-annual variations of surface parameters. This study extended to use a multi-periodic harmonic model, which is expressed as the sum of a series of sine waves, for the adaptive system. For the system assessment, simulation data were generated from a model of negative errors, based on the fact that the observation is mainly suppressed by bad weather. The experimental results of this simulation study show the potentiality of the proposed system for real-time monitoring on the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.7
• /
• pp.540-549
• /
• 2018

Classifying the front body of the missile and debris of a high-speed missile in intercepting a high-speed missile is an important issue. The motion of the front body of the missile is characterized by precession, but the motion of the debris in the boosting part separation phase is characterized by tumbling. There are periodic patterns caused by the precession or tumbling motion on the dynamic radar cross section (RCS). In addition, there are statistical properties caused by the change pattern of the dynamic RCS. A method is proposed to classify the front body of the missile and debris using periodic and statistical properties of the dynamic RCS. Three kinds of feature vector are extracted from the periodic and statistical properties of the dynamic RCS. The front body of the missiles and debris was classified using a support vector machine.

• Journal of Korean Library and Information Science Society
• /
• v.22
• /
• pp.95-137
• /
• 1995

Christianity Newspapers related to Korea was started from 'The Christian Advocate' launched in 1897 by A ppenzeller, H.G, American. From here, through the division of the newspaper related to Korean Christianity, the periodic background of the time, contents of the newspaper published and the bibliographic feature are as follows. 1. The time of Introduction (1885-1910) 2. The time of Establishment (1911-1934) 3. The time of sufferings (1935-1945) 4. The time of upheaval (1946-1960) 5. The time of preparation

• MALSORI
• /
• no.45
• /
• pp.35-45
• /
• 2003

Laryngeal diseases cause significant changes in the quality of speech production. Automatic detection of laryngeal diseases by voice is attractive because of its nonintrusive nature. In this paper, we apply speech recognition techniques to detection of laryngeal cancer, and investigate which feature parameters and classification methods are appropriate for this purpose. Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC) are examined as feature parameters, and parameters reflecting the periodicity of speech and its perturbation are also considered. As for classifier, multilayer perceptron neural networks and Gaussian Mixture Models (GMM) are employed. According to our experiments, higher order LPCC with the periodic information parameters yields the best performance.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.6
• /
• pp.67-75
• /
• 1993

ECG pattern was classified using a back-propagation neural network. An improved feature extractor of ECG is proposed for better classification capability. It is consisted of preprocessing ECG signal by an FIR filter faster than conventional one by a factor of 5. QRS complex recognition by moving-window integration, and peak extraction by quadratic approximation. Since the FIR filter had a periodic frequency spectrum, only one-fifth of usual processing time was required. Also, segmentation of ECG signal followed by quadratic approximation of each segment enabled accurate detection of both P and T waves. When improtant features were extracted and fed into back-propagation neural network for pattern classification, the required number of nodes in hidden and input layers was reduced compared to using raw data as an input, also reducing the necessary time for study. Accurate pattern classification was possible by an appropriate feature selection.

• Journal of Korea Multimedia Society
• /
• v.20 no.6
• /
• pp.893-910
• /
• 2017

In this paper, we propose a simple online multiple object (human) tracking method, LKDeep (Lucas-Kanade feature and Detection based Simple Online Multiple Object Tracker), which can run in fast online enough on CPU core only with acceptable tracking performance for embedded surveillance purpose. The proposed LKDeep is a pragmatic hybrid approach which tracks multiple objects (humans) mainly based on LK features but is compensated by detection on periodic times or on necessity times. Compared to other state-of-the-art multiple object tracking methods based on 'Tracking-By-Detection (TBD)' approach, the proposed LKDeep is faster since it does not have to detect object on every frame and it utilizes simple association rule, but it shows a good object tracking performance. Through experiments in comparison with other multiple object tracking (MOT) methods using the public DPM detector among online state-of-the-art MOT methods reported in MOT challenge [1], it is shown that the proposed simple online MOT method, LKDeep runs faster but with good tracking performance for surveillance purpose. It is further observed through single object tracking (SOT) visual tracker benchmark experiment [2] that LKDeep with an optimized deep learning detector can run in online fast with comparable tracking performance to other state-of-the-art SOT methods.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.3
• /
• pp.325-331
• /
• 2018

This paper deals with the dynamical analysis of a vessel that leads to capsize in regular beam seas. The complete investigation of nonlinear behaviors includes sub-harmonic motion, bifurcation, and chaos under variations of control parameters. The vessel rolling motions can exhibit various undesirable nonlinear phenomena. We have employed a linear-plus-cubic type damping term (LPCD) in a nonlinear rolling equation. Using the fourth order Runge-Kutta algorithm with the phase portraits, various dynamical behaviors (limit cycles, bifurcations, and chaos) are presented in beam seas. On increasing the value of control parameter ${\Omega}$, chaotic behavior interspersed with intermittent periodic windows are clearly observed in the numerical simulations. The chaotic region is widely spread according to system parameter ${\Omega}$ in the range of 0.1 to 0.9. When the value of the control parameter is increased beyond the chaotic region, periodic solutions are dominant in the range of frequency ratio ${\Omega}=1.01{\sim}1.6$. In addition, one more important feature is that different types of stable harmonic motions such as periodicity of 2T, 3T, 4T and 5T exist in the range of ${\Omega}=0.34{\sim}0.83$.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.12 s.177
• /
• pp.184-189
• /
• 2005

An experimental study of the femtosecond laser machining of Si materials was carried out. Direct laser machining of the materials for the feature size of a few micron scale has the advantage of low cost and simple process comparing to the semiconductor process, E-beam lithography, ECM and other machining process. Further, the femtosecond laser is the better tool to machine the micro parts due to its characteristics of minimizing the heat affected zone(HAZ). As a result of line cutting of Si, the optimal condition had the region of the effective energy of 2mJ/mm-2.5mJ/mm with the power of 0.5mW-1.5mW. The polarization effects of the incident beam existed in the machining qualities, therefore the sample motion should be perpendicular to the projection of the electric vector. We also observed the periodic ripple patterns which come out in condition of the pulse overlap with the threshold energy. Finally, we could machined the groove with the linewidth of below $2{\mu}m$ for the application of MEMS device repairing, scribing and arbitrary patterning.

• Publications of The Korean Astronomical Society
• /
• v.30 no.2
• /
• pp.587-589
• /
• 2015

We present the results from analysis of the Hilbert-Huang transform (HHT) for the 4 Hz quasi-periodic oscillations (QPO) around the black hole X-ray binary XTE J1550-564. The resultant Hilbert spectra demonstrate that the QPO is composed of a series of intermittent signals appearing occasionally. From the analysis of the HHT, we further found the distribution of the lifetimes for the intermittent oscillations and the distribution for the time intervals with no significant signal (the break time). The mean lifetime is 1.45 s and 90% of the oscillation segments have lifetimes less than 3.1 s whereas the mean break time is 0.42 s and 90% of break times are less than 0.73 s. We conclude that the intermittent feature of the QPO could be explained by the Lense-Thirring precession model and rules out interpretations of continual frequency modulation.

• Journal of Multimedia Information System
• /
• v.5 no.3
• /
• pp.195-200
• /
• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.