• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.973-974
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• 2008

As radar signal environments become denser and more complex, the capability of high-speed and accurate signal analysis is required for ES(Electronic warfare Support) system to identify individual radar signals at real-time. In this paper, we propose the new novel clustering algorithm of radar pulses to alleviate the load of signal analysis process and support reliable analysis. The proposed algorithm uses KDE(Kernel Density Estimation) and its CDF(Cumulative Distribution Function) to compose clusters considering the distribution characteristics of pulses. Simulation results show the good performance of the proposed clustering algorithm in clustering and classifying the emitters.

• Korean Chemical Engineering Research
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• v.52 no.2
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• pp.240-246
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• 2014

A semi-pilot biofilter inoculated with the microbes consortium of Bacillus cereus DAH-1056 and Arthrobacter sp. KDE-0311 was operated under various operating conditions in order to treat malodorous waste air containing both hydrogen sulfide and ammonia. When both hydrogen sulfide and ammonia contained in malodorous waste air were treated simultaneously by semi-pilot biofilter inoculated with Thiobacillus sp. IW and return-sludge, the removal efficiencies of hydrogen sulfide and ammonia were ca. 80% and ca. 50%, respectively. On the other hand, in this study, the removal efficiencies of hydrogen sulfide and ammonia were ca. 90% and ca. 60%, respectively. Therefore, the removal efficiencies of hydrogen sulfide and ammonia were enhanced by ca. 13% and 20%, respectively, compared to the semipilot biofilter inoculated with Thiobacillus sp. IW and return-sludge. In addition, in this study, the maximum elimination capacities of hydrogen sulfide and ammonia were enhanced by ca. 15% ($8g/m^3/h$) and 10~17% ($3{\sim}5g/m^3/h$), respectively. In this study, it was observed either that in case of even a same inlet load of hydrogen sulfide, a higher concentration of hydrogen sulfide causes more difficulties in treating ammonia containing in waste air than a lower one, or that in case of even a same inlet load of ammonia, a lower concentration of ammonia results in higher removal efficienciy and elimination capacity than a higher one. Even though hydrogen sulfide and ammonia were treated simultaneously by a biofilter in this study, the maximum elimination capacity of hydrogen sulfide in this study exceeded or was similar to that in previous study of biofilter treating only hydrogen sulfide. In addition, this study showed the higher maximum elimination capacity of ammonia than other previous investigation of biofilter treating hydrogen sulfide and ammonia simultaneously.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.580-591
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• 2019

This study analyzed roadkill hotspots in Yeongju, Mungyeong-si Andong-si and Cheongsong-gun to compare the method of searching the area of the spatial cluster for selecting the roadkill hotspots. The local spatial autocorrelation index Getis-Ord Gi* statistics were calculated by different units of analysis, drawing hotspot areas of 9% from 300 m and 14% from 1 km on the basis of the total road area. The rating of Z-score in the 1km hotspot area showed the highest Z-score in the 28th National Road section on the border between Yecheon-gun and Yeongj-si. The kernel density method performed general kernel density estimation and network kernel density estimation analysis, both of which made it easier to visualize roadkill hotspots than district unit analysis, but there were limitations that it was difficult to determine statistically significant priority. As a result, local hotspot areas were found to be different according to the cluster analysis method, and areas that are in common need of reduction measures were found to be the hotspot of 28th National Road through Yeongju-si and Yecheon-gun. It is deemed that the results of this study can be used as basic data when identifying roadkill hotspots and establishing measures to reduce roadkill.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.141-148
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• 2019

When simulating the daily rainfall amount by existing Markov Chain model, it is general to simulate the rainfall occurrence and to estimate the rainfall amount randomly from the distribution which is similar to the daily rainfall distribution characteristic using Monte Carlo simulation. At this time, there is a limitation that the characteristics of rainfall intensity and distribution by time according to the rainfall duration are not reflected in the results. In this study, 1-day, 2-day, 3-day, 4-day rainfall event are classified, and the rainfall amount is estimated by rainfall duration. In other words, the distributions of the total amount of rainfall event by the duration are set using the Kernel Density Estimation (KDE), the daily rainfall in each day are estimated from the distribution of each duration. Total rainfall amount determined for each event are divided into each daily rainfall considering the type of daily distribution of the rainfall event which has most similar rainfall amount of the observed rainfall using the k-Nearest Neighbor algorithm (KNN). This study is to develop the limitation of the existing rainfall estimation method, and it is expected that this results can use for the future rainfall estimation and as the primary data in water resource design.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.124-132
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• 2011

In a modern electronic warfare, radar signal environments become more denser and complex. Therefor the capability of reliable signal analysis techniques is required for ES(Electronic warfare Support) system to identify and analysis individual emitter signals from received signals. In this paper, we propose the new signal grouping algorithm to ensure the reliable signal analysis and to reduce the cost of the signal processing steps in the ES. The proposed grouping algorithm uses KDE(Kernel Density Estimator) and its CDF(Cumulative Distribution Function) to compose windows considering the statistical distribution characteristics based on the radar frequency modulation type. Simulation results show the good performance of the proposed technique in the signal grouping.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.184-188
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• 2008

추계학적 시계열 분석은 크게 수문자료의 장기간 합성과 실시간 예측으로 구분해 볼 수 있다. 장기간 합성은 주로 수문자료의 추계적 특성을 반영한 수자원 시스템의 운영율 개발에 이용되어 왔다. 반면에 실시간 예측은 수자원 시스템의 순응적(adaptive) 관리에 적용되고 있다. 두 개념의 차이로 전자는 시계열 자료를 합성하여 발생 가능한 모든 수문조합을 얻고자 하는 것이라면 후자는 전 시간의 수문량을 조건으로 하는 다음 시간의 값을 순응적으로 예측하는 것이라 할 수 있다. 수문자료의 합성과 예측에는 크게 결정론적, 확률론적 방법의 두 가지 대별될 수 있다. 결정론적 모델링 방법에는 인공신경망이나 Fuzzy 기법 등을 이용할 수 있으며, 확률론적 방법에는 ARMAX 등의 모수적 기법과 k-NN(k-nearest neighbor bootstrap resampling), KDE(kernel density estimates), 추계학적 인공신경망 등의 비모수적 기법으로 분류할 수 있다. 본 연구에서는 대표적 비모수적 기법인 k-NN를 이용하여 충주댐을 대상으로 월 및 일 유입량 자료의 예측 정도를 살펴보았다. 전 시간 관측치를 조건으로 하는 다음 시간의 조건부 확률분포를 구하여 평균값을 계산한 후 관측치와 비교함으로써 모형의 정도를 살펴보았다. 그리고 실시간 저수지 운영에 이 기법의 활용성과 장단점도 살펴보았다. 모형개발 절차로 모형의 보정을 거쳐 검증을 실시하였다. 결론적으로 월 및 일 유입량 예측에 k-NN 기법이 실무적으로 적용될 수 있었으며, 장점으로는 k-NN 기법이 다른 기법보다 모델링 절차가 비교적 쉬워 저수지 운영 최적화 등 타 시스템과의 연계에 수월함이 인식되었다.

• ALGAE
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• v.30 no.1
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• pp.49-58
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• 2015

In September 2007, reports of respiratory irritation and fish kills were received by the Florida Fish and Wildlife Conservation Commission (FWC) from the Jacksonville, Florida area. Water samples collected in this area indicated a bloom of Karenia brevis, the dinoflagellate that produces brevetoxin, which can cause neurotoxic shellfish poisoning. For the next four months, K. brevis was found along approximately 400 km of coastal and Intracoastal waterways from Jacksonville to Jupiter Inlet. This event represents the longest and most extensive red tide the east coast of Florida has experienced and the first time Karenia species other than K. brevis have been reported in this area. This extensive red tide influenced commercial and recreational shellfish harvesting activities along Florida's east coast. Fourteen shellfish harvesting areas (SHAs) were monitored weekly during this event and 10 SHAs were closed for an average of 53 days due to this red tide. The length of SHA closure was dependent on the shellfish species present. Interagency cooperation in monitoring this K. brevis bloom was successful in mitigating any human health impacts. Kernel density estimation was used to create geographic extent maps to help extrapolate discreet sample data points into $5km^2$ radius values for better visualization of the bloom.

• Journal of KIISE
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• v.43 no.2
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• pp.135-142
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• 2016

Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.593-596
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• 2007

웹데브(WebDAV)는 분산 저작활동을 지원하기 위한 IETF의 표준 프로토콜로서 원거리 사용자들 간의 비동기적인 저작활동을 지원하며, 웹데브 서버에 등록된 자원에 대하여 속성을 관리하고 접근을 제어할 수 있는 웹데브 메소드와 웹데브 접근제어 프로토콜을 이용하여 웹데브 서버를 다양한 방법으로 확장할 수 있다. 본 논문에서는 웹데브 표준 프로토콜과 접근제어 프로토콜을 이용하여 강좌관리 시스템을 설계한다. 강좌관리 시스템에서 사용되는 웹데브 기반 서버는 Apache 그룹의 Jakarta Slide 서버를 확장하였으며 클라이언트는 KDE 기반의 리눅스에서 동작하도록 설계하였다. 강좌관리 시스템은 HTTP 프로토콜 기반의 응용프로그램으로서 인터넷이 연결되어 있는 어떠한 곳에서라도 사용할 수 있으며 분산저작이 가능한 웹데브 서버의 특성을 살려서 웹상에서 자유로운 자원 교환을 지원한다. 또한 일반적인 강좌관리 시스템에서 사용하는 출석등록, 보고서 제출, 강의 자료 제공 등의 기본 기능을 웹데브 서버가 지원하는 기능을 이용하여 설계하며, 교수와 학생 각자의 역할에 맞는 인터페이스를 제공하여 인터페이스에서 발생할 수 있는 오류를 줄이고 사용자 중심의 인터페이스로 설계하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.283-287
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• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.