• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1386-1391
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• 2018

The radar in the moving platform is interfered by the mainlobe clutter as well as the altitude clutter that is received from sidelobe. The altitude clutter is relatively short range compared to mainlobe clutter and therefore enters the radar with a strong signal. As these clutters are major reason making the probability of false alarm high, it is required to suppress both altitude clutter and mainlobe clutter. In this paper, It is proposed the clutter suppression method consisted of two pulse canceller to suppress the clutters being two frequency area in moving platform. It is analyzed the correlation of output signals according to the use of pulse canceller and provided the structure of staggered pulse canceller considered the correlation. Finally, it shows that altitude clutter and mainlobe clutter are suppressed by proposed staggered pulse canceller using the simulation.

• Journal of KIISE
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• v.43 no.1
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• pp.119-125
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• 2016

Cognitive radio technology can allow secondary users (SUs) to access unused licensed spectrums in an opportunistic manner without interfering with primary users (PUs). Spectrum sensing is a key technology for cognitive radio (CR). However, few studies have examined energy-efficient spectrum sensing in cognitive radio sensor networks (CRSNs). In this paper, we propose an energy-efficient cooperative spectrum sensing nodes selection scheme for cluster-based cognitive radio sensor networks. In our proposed scheme, false alarm probability and energy consumption are considered to minimize the number of spectrum sensing nodes in a cluster. Simulation results show that by applying the proposed scheme, spectrum sensing efficiency is improved with a decreased number of spectrum sensing nodes. Furthermore, network energy efficiency is guaranteed and network lifetime is substantially prolonged.

• The Plant Pathology Journal
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• v.31 no.4
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• pp.350-362
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• 2015

A disease forecast model for bacterial grain rot (BGR) of rice, which is caused by Burkholderia glumae, was developed in this study. The model, which was named 'BGRcast', determined daily conduciveness of weather conditions to epidemic development of BGR and forecasted risk of BGR development. All data that were used to develop and validate the BGRcast model were collected from field observations on disease incidence at Naju, Korea during 1998-2004 and 2010. In this study, we have proposed the environmental conduciveness as a measure of conduciveness of weather conditions for population growth of B. glumae and panicle infection in the field. The BGRcast calculated daily environmental conduciveness, $C_i$, based on daily minimum temperature and daily average relative humidity. With regard to the developmental stages of rice plants, the epidemic development of BGR was divided into three phases, i.e., lag, inoculum build-up and infection phases. Daily average of $C_i$ was calculated for the inoculum build-up phase ($C_{inf}$) and the infection phase ($C_{inc}$). The $C_{inc}$ and $C_{inf}$ were considered environmental conduciveness for the periods of inoculum build-up in association with rice plants and panicle infection during the heading stage, respectively. The BGRcast model was able to forecast actual occurrence of BGR at the probability of 71.4% and its false alarm ratio was 47.6%. With the thresholds of $C_{inc}=0.3$ and $C_{inf}=0.5$, the model was able to provide advisories that could be used to make decisions on whether to spray bactericide at the preand post-heading stage.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.10-14
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• 2015

Recently, the collection of the sensor data and its analysis become important as the smart buildings equipped with the various sensors appear as a usual scene. The interconnection through the wire cable among the sensors is indispensible because of the information collections such as the temperature, the humidity, and the luminance in the rooms and the hallways for the effective management of the in-building energies. However, these interconnections through the cabling will be very costly, time-consuming, and a difficult task since they will cause some damages to the buildings. Therefore, the interconnections through the unwired connections are required in terms of the deployment effectiveness such as time and cost In this paper, the design and the short sequence operation appropriateness are confirmed through the simulation of the signal measurement system for in-building propagation characteristics based on short signature sequence and the analysis of the system characteristics based on the false alarm probability is performed thereafter.

• Atmosphere
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• v.31 no.5
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.3
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• pp.69-78
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• 2024

This study evaluates the detection and utilization of wind shear using data from the Low-Level Wind Shear Alert System (LLWAS) and the Aerodrome Meteorological Observation System (AMOS) for the year 2023 at Incheon International Airport. A comparison of wind shear occurrence days revealed that LLWAS recorded 57 days, the reproduced LLWAS recorded 84 days, and AMOS recorded 163 days, with AMOS and the reproduced LLWAS showing higher occurrences. Performance metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and True Skill Statistic (TSS), were analyzed to evaluate detection capabilities. For the reproduced LLWAS, most wind shear events were detected, but the FAR was high, indicating lower performance. AMOS detected about 50% of actual wind shear events, with a lower FAR than the reproduced LLWAS but still relatively high. To improve detection performance, optimal thresholds for wind shear warnings were analyzed and adjusted, resulting in an increase in the CSI from 0.53 to 0.68 for the reproduced LLWAS and from 0.25 to 0.28 for AMOS. By adjusting the wind shear warning thresholds, the balance between POD and FAR was improved, confirming the potential for ground-based equipment to issue wind shear warnings effectively.

• Journal of Astronomy and Space Sciences
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• v.26 no.3
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• pp.305-316
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• 2009

In this study, we suggest an empirical forecast of CIR (Corotating Interaction Regions) and geomagnetic storm based on the information of coronal holes (CH). For this we used CH data obtained from He I $10830{\AA}$ maps at National Solar Observatory-Kitt Peak from January 1996 to November 2003 and the CIR and storm data that Choi et al. (2009) identified. Considering the relationship among coronal holes, CIRs, and geomagnetic storms (Choi et al. 2009), we propose the criteria for geoeffective coronal holes; the center of CH is located between $N40^{\circ}$ and $S40^{\circ}$ and between $E40^{\circ}$ and $W20^{\circ}$, and its area in percentage of solar hemispheric area is larger than the following areas: (1) case 1: 0.36%, (2) case 2: 0.66%, (3) case 3: 0.36% for 1996-2000, and 0.66% for 2001-2003. Then we present contingency tables between prediction and observation for three cases and their dependence on solar cycle phase. From the contingency tables, we determined several statistical parameters for forecast evaluation such as PODy (the probability of detection yes), FAR (the false alarm ratio), Bias (the ratio of "yes" predictions to "yes" observations) and CSI (critical success index). Considering the importance of PODy and CSI, we found that the best criterion is case 3; CH-CIR: PODy=0.77, FAR=0.66, Bias=2.28, CSI=0.30. CH-storm: PODy=0.81, FAR=0.84, Bias=5.00, CSI=0.16. It is also found that the parameters after the solar maximum are much better than those before the solar maximum. Our results show that the forecasting of CIR based on coronal hole information is meaningful but the forecast of goemagnetic storm is challenging.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.10
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• pp.28-33
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• 2009

In this paper, we propose the receiver algorithms suitable for the VHF (Very High Frequency) digital link mode-2(VDL Mode-2) system. Unlike conventional digital communication systems using the root raised cosine filter as a transmit and receive filter, raised cosine filter is used as a transmit filter in the VDL Mode-2 system. Hence, it is crucial to design and implement the optimum lowpass receive filter by considering inter-symbol interference and noise performance. On the other hand, due to the short preamble pattern, it is crucial to develop an efficient packet detection algorithm for reliable communication link for the VDL Mode-2 system. Also, frequency offset due to the carrier frequency difference between transmitter and receiver and doppler frequency shift must be estimated and compensated for reliable communication. In this paper, the optimum receive filter, packet detection and frequency offset compensation algorithms are proposed and the performance of the VDL system employing the proposed algorithms are evaluated.