• Aerospace Engineering and Technology
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• v.3 no.2
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• pp.229-237
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• 2004

In this paper, the design method and algorithms of the signal processor for a multipurpose radar system are analyzed. The signal processor, operating at the two modes-collision avoidance mode and weather mode, has 4 steps of ADC, NCI, STC, CFAR. Several algorithms of NCI and CFAR are analyzed and the optimal design is proposed to the system. CVI and CMLD algorithm have good performance in decreasing the false alarm rate and increasing detection probability, Regarding processor computational capacity, K=12 for CVI, M=16~20, Ko=M-4 for CMLD is suggested. CVI processing needs much time, two or more processors need to be allocated to CVI. So, for the system with four processors, two processors should be allocated to VID of NCI with ADC input and CFAR with STC, and two processors are should be allocated to CVI.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.172-182
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• 2014

In this paper, we investigate the fundamental performance limits of the cooperative sensing using energy detection by considering the unlimited number of sensing nodes. Although a lot of cognitive radio research so far proposed various uses of energy detection because of its simplicity, the performance limits of energy detection have not been studied when a large number of sensing nodes exist. First, we show that when the sensing nodes see the independent and identically distributed channel conditions, then as the number of sensing nodes N goes to infinity, the OR rule of hard decision achieves zero of false alarm Pf for any given target probability of detection $\bar{P_d}$ irrespective of the non-zero received primary user signal to noise ratio ${\gamma}$. Second, we show that under the same condition, when the AND rule of hard decision is used, there exists a lower bound of $P_f$. Interestingly, however, for given $\bar{P_d}$, $P_f$ goes to 1 as N goes to infinity. Third, we show that when the soft decision is used, there exists a way of achieving 100% utilization of secondary user, i.e., the sensing time overhead ratio goes to zero so does $P_f$.We verify our analyses by performing extensive simulations of the proposed unlimited cooperative sensing. Finally, we suggest a way of incorporating the unlimited cooperative sensing into a practical cellular system such as long term evolutionadvanced by exploiting the existing frame structure of absolute blank subframe to implement the in-band sensing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.3 s.106
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• pp.272-279
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• 2006

In this paper, we evaluate the performance of frame synchronization words of pilot bit patterns which are used for the channel estimation and frame synchronization confirmation in W-CDMA(Wide band Code Division Multiple Access) system. W-CDMA system also uses compressed mode to make measurements from another frequency without a full dual receiver terminal. It is confirmed by using computer simulation that the proposed frame synchronization words also maintain the frame synchronization property in the compressed mode by using the complementary mapping relationship of preferred pair ROC(Receiver Operating Characteristic) curves with probability of detection and false alarm are used to analized the performance of the proposed frame synchronization words by using the various detection metrics such as LLRT(Log Likelyhood Ratio Test), GLRT(Generalied Likelyhood Ratio Test), soft and hard correlation tests in AWGN and Rayleigh fading channels. It is expected that the research results fer the performance of pilot bit patterns of this paper can be useful reference for the design and implementation of frame synchronization in 3rd generation W-CDMA system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.4
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• pp.423-429
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• 2011

Cooperative spectrum sensing is proposed to overcome some problem such as multipath fading and shadowing and to improve spectrum sensing performance. There are different combining methods for cooperative spectrum sensing: hard decision method and soft decision method. In this paper, we analysis the performance of cooperative spectrum sensing with distance based weight that is kind of a soft decision rule for cognitive radio(CR) systems and CR systems sense the spectrum of the licensed user by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate(CFAR) algorithm for energy detection. The signal of licensed user is OFDM signal and the wireless channel between a licensed user and CR systems is modeled as Gaussian channel. From the simulation results, the cooperative spectrum sensing with distance based weight combining(DWC) and equal gain combing(EGC) methods shows higher spectrum sensing performance than single spectrum sensing does. And the detection probability performance with the DWC is higher than that with the EGC.

• Atmosphere
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• v.16 no.2
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• pp.125-139
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• 2006

Precipitation forecasts from MM5 have been verified for the period 1989-2001 over Yeongdong region to show a tendency of model forecast. We select 57 events which are related with the heavy snowfall in Yeongdong region. They are classified into three precipitation types; mountain type, cold-coastal type, and warm type. The threat score (TS), the probability of detection (POD), and the false-alarm rate (FAR) are computed for categorical verification and the mean squared error (MSE) is also computed for scalar accuracy measures. In the case of POD, warm, mountain, and cold-coastal precipitation type are 0.71, 0.69, and 0.55 in turn, respectively. In aspect of quantitative verification, mountain and cold-coastal type are relatively well matched between forecasts and observations, while for warm type MM5 tends to overestimate precipitation. There are 12 events for the POD below 0.2, mountain, cold-coastal, warm type are 2, 7, 3 events, respectively. Most of their precipitation are distributed over the East Sea nearby Yeongdong region. These events are also shown when there are no or very weak easterlies in the lower troposphere. Even in the case that we use high resolution sea surface temperature (about 18 km) for the boundary condition, there are not much changes in the wind direction to compare that with low resolution sea surface temperature (about 100 km).

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9A
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• pp.859-867
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• 2010

In this paper, we consider a novel spectrum sensing system in which firstly, the fusion center (FC) senses and makes the own decision then if its sensing result is not useful for achieving the final decision, the local observations from the cognitive users (CUs) will be required. Moreover, in case that FC needs the results from CUs, we will choose only CU having the highest collected energy to send its local decision to FC. Based on this selecting method, the number of sensing bits can be reduced; hence, we can save the power and the bandwidth for reporting stage in the cognitive radio network (CRN). The mathematical analysis of the key metrics of the sensing schemes (probability of detection, false alarm, e.g.) will be investigated and confirmed by the Monte-Carlo simulation results to show the performance enhancement of the proposed schemes.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.1
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• pp.90-95
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• 2014

The system design and the system performance analysis of 3D imaging laser radar system for the mapping purpose is addressed in this article. For the mapping, a push-bloom scanning method is utilized. The pulsed fiber laser with high pulse energy and high pulse repetition rate is used for the light source of laser radar system. The high sensitive linear mode InGaAs avalanche photo-diode is used for the laser receiver module. The time-of-flight of laser pulse from the laser to the receiver is calculated by using high speed FPGA based signal processing board. To reduce the walk error of laser pulse regardless of the intensity differences between pulses, the time of flight is measured from peak to peak of laser pulses. To get 3D image with a single pixel detector, Risley scanner which stirs the laser beam in an ellipsoidal pattern is used. The system laser energy budget characteristics is modeled using LADAR equation, from which the system performances such as the pulse detection probability, false alarm and etc. are analyzed and predicted. The test results of the system performances are acquired and compared with the predicted system performance. According to test results, all the system requirements are satisfied. The 3D image which was acquired by using the laser radar system is also presented in this article.