• Journal of Sensor Science and Technology
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• v.4 no.4
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• pp.1-9
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• 1995

The ${\gamma}$-ray deposition spectra were calculated by Monte Calro method to obtain the scintillation characteristics of the ${\gamma}$-ray for BGO scintillation detector with the spherical shape of 1.25 cm radius. The code used in calculating the ${\gamma}$-ray deposition spectra was made for personal computer with qbasic language. Also the ${\gamma}$-ray energy spectra of $^{22}Na$, $^{137}Cs$ and $^{207}Bi$ were measured with the detector. The energy dependent resolution below 2000 keV for the detector was determined by estimating the standard deviation of the photopeak fitted with gaussian function, and $X^{2}$ fitting using Nardi's empirical formula. The measured spectra of $^{22}Na$ and $^{137}Cs$ were compared with the broadened spectra which were obtained by broadening the calculated ${\gamma}$-ray deposition spectra with the energy dependent resolution. The absolute efficiency and the intrinsic peak efficiency of the detector were obtained by calculating the ${\gamma}$-ray deposition spectrum with the code.

• Asian Journal of Atmospheric Environment
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• v.6 no.1
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• pp.53-66
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• 2012

A comparison of analytical approaches for Levoglucosan ($C_6H_{10}O_5$, commonly formed from the pyrolysis of carbohydrates such as cellulose) and used for a molecular marker in biomass burning is made between the four different analytical systems. 1) Spectrothermography technique as the evaluation of thermograms of carbon using Elemental Carbon & Organic Carbon Analyzer, 2) mass spectrometry technique using Gas Chromatography/mass spectrometer (GC/MS), 3) Aerosol Mass Spectrometer (AMS) for the identification of the particle size distribution and chemical composition, and 4) two dimensional Gas Chromatography with Time of Flight mass spectrometry (GC${\times}$GC-TOFMS) for defining the signature of Levoglucosan in terms of chemical analytical process. First, a Spectrothermography, which is defined as the graphical representation of the carbon, can be measured as a function of temperature during the thermal separation process and spectrothermographic analysis. GC/MS can detect mass fragment ions of Levoglucosan characterized by its base peak at m/z 60, 73 in mass fragment-grams by methylation and m/z 217, 204 by trimethylsilylderivatives (TMS-derivatives). AMS can be used to analyze the base peak at m/z 60.021, 73.029 in mass fragment-grams with a multiple-peak Gaussian curve fit algorithm. In the analysis of TMS derivatives by GC${\times}$GC-TOFMS, it can detect m/z 73 as the base ion for the identification of Levoglucosan. It can also observe m/z 217 and 204 with existence of m/z 333. Although the ratios of m/z 217 and m/z 204 to the base ion (m/z 73) in the mass spectrum of GC${\times}$GC-TOFMS lower than those of GC/MS, Levoglucosan can be separated and characterized from D (-) +Ribose in the mixture of sugar compounds. At last, the environmental significance of Levoglucosan will be discussed with respect to the health effect to offer important opportunities for clinical and potential epidemiological research for reducing incidence of cardiovascular and respiratory diseases.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.61-67
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• 2017

Recently, CCTV has been combined with areas such as big data, artificial intelligence, and image analysis to detect various abnormal behaviors and to detect and analyze the overall situation of objects such as people. Image analysis research for this intelligent video surveillance function is progressing actively. However, CCTV images using 2D information generally have limitations such as object misrecognition due to lack of topological information. This problem can be solved by adding the depth information of the object created by using two cameras to the image. In this paper, we perform background modeling using Mixture of Gaussian technique and detect whether there are moving objects by segmenting the foreground from the modeled background. In order to perform the depth information-based segmentation using the RGB information-based segmentation results, stereo-based depth maps are generated using two cameras. Next, the RGB-based segmented region is set as a domain for extracting depth information, and depth-based segmentation is performed within the domain. In order to detect the center point of a robustly segmented object and to track the direction, the movement of the object is tracked by applying the CAMShift technique, which is the most basic object tracking method. From the experiments, we prove the efficiency of the proposed object detection and tracking method using the RGB-D model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.21-29
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• 2011

At existing skin detection methods using skin color information defined based on the prior knowldege, threshold value to be used at the stage of dividing the backround and the skin region was decided on a subjective point of view through experiments. Also, threshold value was selected in a passive manner according to their background and illumination environments in these existing methods. These existing methods displayed a drawback in that their performance was fully influenced by the threshold value estimated through repetitive experiments. To overcome the drawback of existing methods, this paper propose a skin region detection method using a histogram approximation based on the mean shift algorithm. The proposed method is to divide the background region and the skin region by using the mean shift method at the histogram of the skin-map of the input image generated by the comparison of the similarity with the standard skin color at the CbCr color space and actively finding the maximum value converged by brightness level. Since the histogram has a form of discontinuous function accumulated according to the brightness value of the pixel, it gets approximated as a Gaussian Mixture Model (GMM) using the Bezier Curve method. Thus, the proposed method detects the skin region by using the mean shift method and actively finding the maximum value which eventually becomes the dividing point, not by using the manually selected threshold value unlike other existing methods. This method detects the skin region high performance effectively through experiments.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Korean Journal of Optics and Photonics
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• v.10 no.6
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• pp.500-506
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• 1999

We designed and fabricated a bidirectional optical transceiver module for low cost access network. An integrated chip forming a pin-PD on an 1.3 urn FP-LD was assembled by flip-chip bonding on a Si optical bench, a single mode fiber with an angled end facet was aligned passively with the integrated chip on V-groove of Si-optical bench. Gaussian beam theory was applied to evaluate the coupling coefficients as a function of some parameters such as alignment distance, angle of fiber end facet, vertical alignment error. The theory is also used to search the bottle-neck between transmittance and receiving coupling efficiency in the bi-directional optical system. Tn this paper, we confirmed that reduction of coupling efficiency by the vertical alignment error between laser beam and fiber core axis can be compensated by controlling the fiber facet angle. In the fabrication of sub-module, a'||'&'||' we made such that the fiber facet have a corn shape with an angled facet only core part, the reflection of transmitted laser beam from the fiber facet could be minimized below -35 dE in alignment distance of 2: 30 /J.m. In the same condition, transmitted output power of -12.1 dEm and responsivity of 0.2. AIW were obtained.