• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.323-329
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• 2002

This paper proposes a frame reliability weighting method to compensate for a time-selective noise that occurs at random positions of speech signal contaminating certain parts of the speech signal. Speech frames have different degrees of reliability and the reliability is proportional to SNR (signal-to noise ratio). While it is feasible to estimate frame Sl? by using the noise information from non-speech interval under a stationary noisy situation, it is difficult to obtain noise spectrum for a time-selective noise. Therefore, we used statistical models of clean speech for the estimation of the frame reliability. The proposed MFR (model-based frame reliability) approximates frame SNR values using filterbank energy vectors that are obtained by the inverse transformation of input MFCC (mal-frequency cepstral coefficient) vectors and mean vectors of a reference model. Experiments on various burnt noises revealed that the proposed method could represent the frame reliability effectively. We could improve the recognition performance by using MFR values as weighting factors at the likelihood calculation step.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.6
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• pp.465-474
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• 2014

To validate the accuracy of the boiling heat flux partitioning model, an experiment was performed to investigate how the wall heat flux is divided into the three heat transfer modes of evaporation, quenching, and single-phase convection during subcooled nucleate boiling on a vertical wall. For the experimental partitioning of the wall heat flux, the wall heat flux and liquid-vapor distributions were simultaneously obtained using synchronized infrared thermometry and the total reflection technique. Boiling experiments of water with subcooling of $10^{\circ}C$ were conducted under atmospheric pressure, and the results obtained at the wall superheat of $12^{\circ}C$ and average heat flux of $283kW/m^2$were analyzed. There was a large difference in the heat flux partitioning results between the experiment and correlation, and the bubble departure diameter and bubble influence factor, which account for a portion of the surrounding superheated liquid layer detached by the departure of a bubble, were found to be important fundamental boiling parameters.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.3 s.309
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• pp.78-84
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• 2006

Our study aimed at quantitative assessment of a cleft palate nose deformity condition by analyzing the following parameters gathered from a photographic image of a cleft palate patient: (1) angle difference between two nostril axes, (2) center of the nostril and distance between two centers, (3) overlapped area of two nostrils, and (4) the overlapped area ratio of the two nostrils. A regression equation of doctor's grades was obtained using the eight parameters. Three plastic surgeons gave us the glades for the each photographic image by to increments with maximum grade of 100. The average reproducibility of the grades given by the three plastic surgeons and the three laymen using the developed program was $10.8{\pm}4.6%\;and\;7.4{\pm}1.8%$, respectively. Kappa values representing the degree of consensus of the plastic surgeons and the three laymen were 0.43 and 0.83, respectively. Correlation coefficient of the grades evaluated by the surgeons and obtained by the regression equation was 0.642 and that of the grades by the surgeons and by the neural network was 0.798. In conclusion, the developed neural network model provided us better reproducibility, much better consensus, and better correlation than doctor's subjective evaluation in addition to objectiveness and easy application.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1209-1219
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• 2019

For consistent vegetation monitoring, it is necessary to generate time-series vegetation index datasets at fine temporal and spatial scales by fusing the complementary characteristics between temporal and spatial scales of multiple satellite data. In this study, we quantitatively and qualitatively analyzed the prediction accuracy of time-series change information extracted from spatio-temporal fusion models of multiple satellite data for vegetation monitoring. As for the spatio-temporal fusion models, we applied two models that have been widely employed to vegetation monitoring, including a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). To quantitatively evaluate the prediction accuracy, we first generated simulated data sets from MODIS data with fine temporal scales and then used them as inputs for the spatio-temporal fusion models. We observed from the comparative experiment that ESTARFM showed better prediction performance than STARFM, but the prediction performance for the two models became degraded as the difference between the prediction date and the simultaneous acquisition date of the input data increased. This result indicates that multiple data acquired close to the prediction date should be used to improve the prediction accuracy. When considering the limited availability of optical images, it is necessary to develop an advanced spatio-temporal model that can reflect the suggestions of this study for vegetation monitoring.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.43-53
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• 2014

Gunnery has been used to detect and classify artilleries. In this paper, we used electro-optical data to get the information of muzzle flash from the artilleries. Feature based approach was applied; we first defined features and sub-features. The number of sub-features was 38~40 generic sub-features, and 2 model-based sub-features. To classify multiclass data, we introduced tree structure with clustering the classes according to the similarity of them. SVM was used for each non-leaf nodes in the tree, as a sub-classifier. From the data, we extracted features and sub-features and classified them by the tree structure SVM classifier. The results showed that the performance of our classifier was good for our muzzle flash classification problem.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.10
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• pp.798-805
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• 2018

This study introduces an adaptive design of experiment (DoE) approach for the hypersonic shock-tunnel testing. A series of experiments are conducted to model the pitch moment coefficient of a cone as the function of the angle of attack and the pitch rate. An algorithm to construct the trajectory of the test model from the images obtained by the high-speed camera is developed to effectively analyze multiple time series experimental data. An adaptive DoE procedure to determine the experimental point based on the analysis results of the past experiments using the algorithm is proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4B
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• pp.226-234
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• 2005

Context-Aware is the most important facts to reason a personalized and optimized service and to provide it to user. In the previous researches, user and surrounding environment were main facts of Context-Aware and middleware or center server has been proposed to support Context-Aware. In the daily space(for example, home, office, Car, etc), interactions between user and service can be a important facts of Context-Aware. In this paper, Context Autogenesis service model is introduced, simplified the Context-Aware process and designed the middleware which performs decentralize management for Context-Aware information of user's portable devices, so that problems occurred during the management and operation of existing Context-Aware system can be minimized and supporting user anonymity

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.2
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• pp.129-138
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• 2017

This paper has been shown for the extraction of Doppler signature from the radar signal for an air-breathing targets tracked in the ground radar. For the extractions, a Doppler resolution is confirmed from mathematical modeling of PT(pulse train) waveform. Doppler signatures of air-breathing target are varied to radar aspect angle of engine and are determined from physical parameter of jet engine. To confirm such Doppler signatures, the radar signal reflected from the air-breathing target is obtained by our radar signal storage. After this extraction, radar aspect angle of engine has estimated from tracking information. Relative differences of Doppler signatures to radar aspect angle of engine is verified from these results and Doppler profiles for radar target identification appliance are presented.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.79-88
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• 1999

In this paper we address the tracking method which tracks only target object in image sequence including moving object. We use a contour tracking algorithm based on intensity and motion boundaries. The motion of the moving object contour in the image is assumed to be well describable by an affine motion model with a translation, a change in scale and a rotation. The moving object contour is represented by B-spline, the position and motion of which is estimated along the image sequence. we use pattern recognition to identify target object. In order to use linear Kalman Filters we decompose the estimation process into two filters. One is estimating the affine motion parameters and the other the shape of moving object contour. In some experiments with dial plate we show that this method enables us to obtain the robust motion estimates and tracking trajectories even in case of including obstructive object.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.