• Journal of IKEEE
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• v.28 no.3
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• pp.296-302
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• 2024

In this paper, we propose a driver condition monitoring system using FMCW in-cabin radar, which is free from wearing inconvenience and privacy issues. Using 77GHz high-precision radar, the system detects changes in eye blinking patterns according to changes in the driving environment and the driver's condition using an adaptive multiple filtering algorithm, and accurately determines drowsy driving by measuring the number of eye blinks and the time it takes to open and close the eyes through the detected data. With the emergence of high-performance radars that are becoming more and more miniaturized, it is possible to embed them in the instrument panel or rearview mirror of the vehicle, and if the driver is judged to be drowsy, it can wake up the driver through an alarm or interlock with the vehicle's driving system to slow down and make an emergency stop to prevent accidents and promote driver safety.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.802-810
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• 2012

This paper describes RFID(Radio Frequency Identification) based target acquisition and docking system. RFID is non-contact identification system, which can send relatively large amount of information using RF signal. Robot employing RFID reader can identify neighboring tag attached objects without any other sensing or supporting systems such as vision sensor. However, the current RFID does not provide spatial information of the identified object, the target docking problem remains in order to execute a task in a real environment. For the problem, the direction sensing RFID reader is developed using a dual-directional antenna. The dual-directional antenna is an antenna set, which is composed of perpendicularly positioned two identical directional antennas. By comparing the received signal strength in each antenna, the robot can know the DOA (Direction of Arrival) of transmitted RF signal. In practice, the DOA estimation poses a significant technical challenge, since the RF signal is easily distorted by the surrounded environmental conditions. Therefore, the robot loses its way to the target in an electromagnetically disturbed environment. For the problem, the g-filter based error correction algorithm is developed in this paper. The algorithm reduces the error using the difference of variances between current estimated and the previously filtered directions. The simulation and experiment results clearly demonstrate that the robot equipped with the developed system can successfully dock to a target tag in obstacles-cluttered environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.51-64
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• 2009

A location tracking sensor such as GPS, INS, Radar, and optical equipments is used in tracking Maneuvering Targets with a multi-sensor, and such systems are used to track, detect, and control UAV, guided missile, and spaceship. Until now, Most of the studies related to tracking Maneuvering Targets are on fusing multiple Radars, or adding a supplementary sensor to INS and GPS. However, A study is required to change the degree of application in fusions since the system property and error property are different from sensors. In this paper, we perform the error analysis of the sensor properties by adding a ground radar to GPS and INS for improving the tracking performance by multi-sensor fusion, and suggest the tracking algorithm that improves the precision and stability by changing the sensor probability of each sensor according to the error. For evaluation, we extract the altitude values in a simulation for the trajectory of UAV and apply the suggested algorithm to carry out the performance analysis. In this study, we change the weight of the evaluated values according to the degree of error between the navigation information of each sensor to improve the precision of navigation information, and made it possible to have a strong tracking which is not affected by external purposed environmental change and disturbance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.555-564
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• 2016

This paper proposes a time slot allocation scheme for military patrol environments. This proposal comes from analysis of traffic properties in a military patrol environment. In the near future, robots are expected to explore enemy grounds and measure threat, taking the place of human patrol. In order to control such robots, control messages must be extremely accurate. One mistake from the control center could cause a tragedy. Thus, high reliability must be guaranteed. Another goal is to maintain a continual flow of multimedia data sent from patrol robots. That is, QoS (Quality of Service) must be guaranteed. In order to transmit data while fulfilling both attributes, the per-path based centralized TDMA slot allocation scheme is recommended. The control center allocates slots to robots allowing synchronization among robots. Slot allocation collisions can also be avoided. The proposed scheme was verified through the ns-3 simulator. The scheme showed a higher packet delivery ratio than the algorithm in comparison. It also performed with shorter delay time in the downlink traffic transmission scenario than the algorithm in comparison.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.399-406
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• 2004

최근 급속한 도시의 팽창과 산업의 발전으로 인하여 가스시설은 급속히 확대되고 있는 실정이다. 이러한 가스시설물의 중요성을 인식하고 많은 도시가스업체에서는 가스관망 시설정보를 전산화하여 항상 최신의 현황을 유지할 수 있는 가스시설물관리시스템을 개발하여 사용하고 있다. 그러나 이러한 시스템은 가스시설물의 현황파악 및 유지관리를 위한 기본적인 기능만을 제공하는 관계로, 가스 누출사고 발생시 정확한 사태의 파악과 함께 신속한 대책 마련을 위한 의사결정 지원이 어려운 실정이다. 따라서 체계적인 가스사고관리를 수행할 수 있는 응용시스템의 필요성이 증대되고 있다. 이러한 시점에서 본 연구에서는 가스사고분석을 신속하고 체계적으로 수행할 수 있는 가스사고관리 적용알고리즘 분석 및 최적의 알고리즘을 정립하여 가스사고관리시스템을 구현하였다. 본 연구를 통한 결과는 1ㆍ2차 차단밸브의 산정이 가능해짐으로써 빈번한 가스 누출사고 발생시 실시간으로 적정대처방안의 제시가 가능하게 되었다. 또한, 누출 최대가스량을 제시함으로써 누출에 대한 피해예상 분석을 위한 정보 제공 및 가스의 신속한 재공급을 위해 필요한 의사결정 지원 정보의 제공이 가능하게 되었다. 아울러, 가스누출사고에 의한 가스공급중단 관로 및 수용가에 대한 속성현황의 파악은 물론 시각적인 도식을 통한 전체적 현황파악이 가능하였다. 이러한 가스사고관리시스템의 개발을 통하여 사고 발생시 신속한 사고방안 제시 및 사고피해의 최소화를 위해 필요한 의사결정 지원 정보의 제공이 가능하게 됨으로써 국민의 안전 및 복지와 도시가스업체의 업무 효율화로 인한 예산절감 효과를 기대할 수 있다. 가시권 분석기능을 이용하여 실제 지형공간상에서 전파경로 손실치를 도시화함으로써 전파관리자가 무선서비스지역 설계, 전파음영지역 판단, 최적 중계기와 기지국 위치 선정에 기여할 것으로 판단된다.하지 않은 지역과 서로 다른 분광특성을 나타내므로 별도의 Segment를 형성하게 된다. 따라서 임상도의 경계선으로부터 획득된 Super-Object의 분광반사 값과 그 안에서 형성된 Sub-Object의 분광반사값의 차이를 이용하여 임상도의 갱신을 위한 변화지역을 탐지하였다.라서 획득한 시추코아에 대해서도 각 연구기관이 전 구간에 대해 동일하게 25%의 소유권을 가지고 있다. ？스굴 시추사업은 2008년까지 수행될 계획이며, 시추작업은 2005년까지 완료될 계획이다. 연구 진행과 관련하여, 공동연구의 명분을 높이고 분석의 효율성을 높이기 위해서 시료채취 및 기초자료 획득은 4개국의 연구원이 모여 공동으로 수행한 후의 결과물을 서로 공유하고, 자세한 전문분야 연구는 각 국의 대표기관이 독립적으로 수행하는 방식을 택하였다 ？스굴에 대한 제1차 시추작업은 2004년 3월 말에 실시하였다. 시추작업 결과, 약 80m의 시추 코아가 성공적으로 회수되어 현재 러시아 이르쿠츠크 지구화학연구소에 보관중이다. 이 시추코아는 2004년 8월 중순경에 4개국 연구팀원들에 의해 공동으로 기재된 후에 분할될 계획이다. 분할된 시료는 국내로 운반되어 다양한 전문분야별 연구에 이용될 것이다. 한편, 제2차 시추작업은 2004년 12월에서 2005년 2월 사이에 실시될 계획이다. 수백만년에 이르는 장기간에 걸쳐 지구환경변화 기록이 보존되어 있는 ？스굴호에 대한

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.634-638
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• 2018

Haze is a factor that degrades the performance of various image processing algorithms, such as those for detection, tracking, and recognition using an electro-optical sensor. For robust operation of an electro-optical sensor-based unmanned system used outdoors, an algorithm capable of effectively removing haze is needed. As a haze removal method using a single electro-optical sensor, the dark channel prior using statistical properties of the electro-optical sensor is most widely known. Previous methods used a square filter in the process of obtaining a transmission using the dark channel prior. When a square filter is used, the effect of removing haze becomes smaller as the size of the filter becomes larger. When the size of the filter becomes excessively small, over-saturation occurs, and color information in the image is lost. Since the size of the filter greatly affects the performance of the algorithm, a relatively large filter is generally used, or a small filter is used so that no over-saturation occurs, depending on the image. In this paper, we propose an improved haze removal method using color image segmentation. The parameters of the color image segmentation are automatically set according to the information complexity of the image, and the over-saturation phenomenon does not occur by estimating the amount of transmission based on the parameters.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.651-664
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• 2014

Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.431-447
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• 2021

Recently, the use of high-resolution satellites is increasing in many areas. In order to supply useful satellite images stably, it is necessary to establish automatic precision geometric correction technic. Geometric correction is the process that corrected geometric errors of satellite imagery based on the GCP (Ground Control Point), which is correspondence point between accurate ground coordinates and image coordinates. Therefore, in the automatic geometric correction process, it is the key to acquire high-quality GCPs automatically. In this paper, we proposed iterative precision geometry correction method. we constructed an image pyramid and repeatedly performed GCP chip matching, outlier detection, and precision sensor modeling in each layer of the image pyramid. Through this method, we were able to acquire high-quality GCPs automatically. we then improved the performance of geometric correction of high-resolution satellite images. To analyze the performance of the proposed method, we used KOMPSAT-3 and 3A Level 1R 8 scenes. As a result of the experiment, the proposed method showed the geometric correction accuracy of 1.5 pixels on average and a maximum of 2 pixels.