• Journal of electromagnetic engineering and science
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• v.14 no.4
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• pp.321-328
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• 2014

Software defined radar (SDR) is a multi-purpose radar system where most of the hardware processing is performed by software. This paper introduces a concept and technology trends of software defined radar, and addresses the advantages and limitations of the current SDR radar systems. For the advanced SDR concept, the KAU SDR Model (KSM) is presented for the multimode and multiband radar system operating in S-, X-, and K-bands. This SDR consists of a replaceable multiband antenna and RF hardware, common digital processor module with multimode, and open software platform based on MATLAB and LabVIEW. The new concept of the SDR radar can be useful in various applications of the education, traffic monitoring and safety, security, and surveillance depending on the various radar environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.141-143
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• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.714-721
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• 2011

In this paper, developing management programs for EMI tracking can navigate the site quickly and solve EMI tracking cause and location to use materials such as analysis of air accidents, EMI site location data of 1000 RF companys, radio wave spectrum analysis and audio data. these data are databased and used comparable data. Also, EMI has been prevented by establishing continuous monitoring system through a 24-hour surveillance. Therefore we were able to provide high quality air waves in order to prevent aircraft accidents. In addition, radar control staff of Korea Airports Corporation against passenger aircraft that will prevent the worst aircraft accident have been established based to continue periodic aviation frequency protection and Portable Electronic Devices(PED) on board aircraft to prevent the culture of safety campaign.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1200-1207
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• 2015

A gateway buoy system connects a underwater network to a terrestrial network, which enables to efficiently monitor the underwater network on a land station. In this paper, we introduce an implemented gateway buoy system which relays gathered data from multiple underwater nodes to a land station in a real time. The gateway buoy hardware system is composed of a underwater acoustic modem system, a radio frequency modem system, and a gateway operating system. in additional, we have implemented a land operating program and a land monitoring program for gateway system and states of underwater network, respectively. We also perform real-sea experiments to verify the performance of the gateway buoy system which real-time monitors underwater network states and gateway system states.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.263-267
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• 2015

current mine detector can't division the section if it is conducted and it needs too much labor force and time. in addition to, if the user don't move the head of sensor in regular speed or move it too fast, it is hard to detect a mine exactly. according to this, to improve the problem using one direction ultrasonic wave sensing signal, that is made up of human body antenna part, main micro processor unit part, smart glasses part, body equipped LCD monitor part, wireless data transmit part, belt type power supply part, black box type camera, Security Communication headset. the user can equip this at head, body, arm, waist and leg in removable type. so it is able to detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal and it can express the 2D or 3D film about distance, form and material of the mine. so the battle combats can avoid the mine and move fast. also, through the portable battery and twin self power supply system of the power supply part, combat troops can fight without extra recharge and we can monitoring the battle situation of distant place at the command center server on real-time. and then, it makes able to sharing the information of battle among battle combats one on one. as a result, the purpose of this study is researching a smart wearable mine detector which can establish a smart battle system as if the commander is in the site of the battle.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.120-130
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• 2012

As long span and complex bridges are constructed often recently, safety estimation became a big issue. Various types of measuring instruments are installed in case of long span bridge. New wireless technologies for long span bridges such as sending information through a gateway at the field or sending it through cables by signal processing the sensing data are applied these days. However, The case of occurred accidents related to bridge in the world have been reported that serious accidents occur due to lack of real-time proactive, intelligent action based on recognition accidents. To solve this problem in this study, the idea of "communication among things", which is the basic method of RFID/USN technology, is applied to the bridge monitoring system. A sensor node module for USN based intelligent bridge system in which sensor are utilized on the bridge and communicates interactively to prevent accidents when it captures the alert signals and urgent events, sends RF wireless signal to the nearest traffic signal to block the traffic and prevent massive accidents, is designed and tested by performing TinyOS based middleware design and sensor test free Space trans-receiving distance.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.387-390
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• 2006

TinyOS 기반 무선 센서 노드를 사용한 실시간 계측 데이터 측정 및 제어 기술은 특히 홈 네트워크 분야에 널리 적용되고 있다. 본 논문에서는 ATMegal128L을 장착한 최소 8대 이상의 Micaz Mote 센서 노드 모듈을 사용하여 각 방의 노도 값을 실시간 측정하는 시스템을 구현하였다. 특히 TinyOS에서 제공되는 OscilloscopeRF의 메시지 구조를 분석하여, 각 노드의 ID 및 계측 데이터를 추출하였다. 또한 이렇게 추출된 계측 데이터를 센서 네트워크의 싱크 노드로부터 데이터 통제 센터(Doc)로 효율적으로 전송하기 위한 TCP 기반 네트워크 프로그래밍을 구현하였다. 실험 결과 센서 노드 수 및 샘플링 주기에 상관없이 안정적으로 계측 데이터 수신이 이루어짐을 확인하였다. 한편, IEEE802.11a/g 기반 무선 네트워크를 통해 실시간 계측 상황을 휴대용 단말기인 PDA에서 확인할 수 있도록 이를 구현하였다.

• 한국정보통신설비학회:학술대회논문집
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• 2002.08a
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• pp.39-42
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• 2002

RS-232C는 정보기기들을 연결하는 직렬통신 표준으로, 간단한 구조로 인해, 복잡한 요구 조건이 없는 상용 기기들의 제어 및 감시용으로 사용되고 있다. 최근 무선동신기술이 발전함에 따라, 저가격의 무선통신 기술을 활용하여 무선 RS-232C에 대한 요구가 늘어나고 있다. 무선 RS-232C로 기존 유선의 RS-232C를 대체함으로서, 기존 시스템의 변경없이 편리한 접속기능을 대체한 수 있을 것이다. 본 논문에서는 유선 RS-232C를 대체하는 무선 RS-232C 모듈을 채용하여, 전력제어에 사용하는 배전자동화 장치의 관리를 간편하게 하는 시스템의 구현에 대해서 다루고 있다. 배전 자동화시스템은 일종의 SCADA시스템의 일부로서, 통신 프로토콜로서 산업용 DNP3.0을 사용하고 있다. 무선 RS-232C 링크의 신뢰성을 확보하기 위하여, 무선 구간에 DNP3.0을 채택하였으며, 무선 통신방식으로는 무선 RF, 블루투스(Bluetooth), 무선랜(wireless LAN)등 3가지 방식을 채용하여, 설계, 구현내용을 기술하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.95-100
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• 2006

최근 반도체 기술과 무선 통신기술의 발전으로 센서 네트워크를 이용한 응용 분야가 다양하게 나타나고 있다. 본 논문에서는 센서 네트워크를 이용하여 산악지역의 자동인원계수시스템과 환경모니터링시스템을 개발했다. 이 시스템은 저전력으로 동작하고 이중 RF 보드를 이용하여 최대한 신뢰성 있는 데이터 전송이 가능하도록 개발하였다. 또한 산악지역에서 데이터 전송율과 통신 신뢰성을 만족시킬 수 있는 433MHz 대역이 적합함을 실험을 통하여 확인하였다. 기존의 CCD 카메라를 이용한 자동인원계수기는 전원공급과 네트워크 부재로 인하여 산악지역에서는 사용하기에 부적합하다. 본 논문에서는 산악지역의 탐방로 폭이 협소함을 이용하여 포토 빔 센서를 이용한 양방향 계수센서를 개발하였다. 산악지역에서 실험한 결과 1.5m 이하의 도로 폭에서 95% 이상의 계수 신뢰도를 보임을 확인할 수 있다.