• Title, Summary, Keyword: 센서융합

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Pre-Filtering based Post-Load Shedding Method for Improving Spatial Queries Accuracy in GeoSensor Environment (GeoSensor 환경에서 공간 질의 정확도 향상을 위한 선-필터링을 이용한 후-부하제한 기법)

  • Kim, Ho;Baek, Sung-Ha;Lee, Dong-Wook;Kim, Gyoung-Bae;Bae, Hae-Young
    • Journal of Korea Spatial Information System Society
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    • v.12 no.1
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    • pp.18-27
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    • 2010
  • In u-GIS environment, GeoSensor environment requires that dynamic data captured from various sensors and static information in terms of features in 2D or 3D are fused together. GeoSensors, the core of this environment, are distributed over a wide area sporadically, and are collected in any size constantly. As a result, storage space could be exceeded because of restricted memory in DSMS. To solve this kind of problems, a lot of related studies are being researched actively. There are typically 3 different methods - Random Load Shedding, Semantic Load Shedding, and Sampling. Random Load Shedding chooses and deletes data in random. Semantic Load Shedding prioritizes data, then deletes it first which has lower priority. Sampling uses statistical operation, computes sampling rate, and sheds load. However, they are not high accuracy because traditional ones do not consider spatial characteristics. In this paper 'Pre-Filtering based Post Load Shedding' are suggested to improve the accuracy of spatial query and to restrict load shedding in DSMS. This method, at first, limits unnecessarily increased loads in stream queue with 'Pre-Filtering'. And then, it processes 'Post-Load Shedding', considering data and spatial status to guarantee the accuracy of result. The suggested method effectively reduces the number of the performance of load shedding, and improves the accuracy of spatial query.

Analysis of Patents regarding Stabilization Technology for Steep Slope Hazards (급경사지재해 안정화기술에 대한 특허분석)

  • Song, Young-Suk;Kim, Jae-Gon
    • The Journal of Engineering Geology
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    • v.20 no.3
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    • pp.257-269
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    • 2010
  • We analyzed patent trends regarding stabilization technology for steep slope hazards, focusing on patents applied for and registered in Korea, the USA, Japan, and Europe. The technology was classified into four groups at the second classification step: prediction techniques, instrumentation techniques, countermeasure/reinforcement/mitigation techniques, and laboratory tests. A total of 2,134 patents were selected for the final effective analysis. As a result of portfolio analysis using the correlation between the number of patents and the applicant for each patent, the Korean and USA situations were classified as belonging to the developing period, and the Japanese and European situations were classified as belonging to the ebbing period. In particular, patent activity in Korea has been enlivened by government-led research. As a result of technology analysis at the second classification step, prediction techniques arising from Japan are evaluated as a competitive power technique, and laboratory tests arising from the USA are evaluated as a competitive power technique. However, prediction techniques and laboratory tests arising from Korea are evaluated as a blank technique. According to the prediction results regarding future research and developments, a new finite element analysis method and a numerical model should be established as part of prediction techniques, as well as sensors, and hazard prediction should be developed by integrating information and equipment using IT technology as part of instrumentation techniques. In addition, improvements to existing structures for erosion control and the development of new slope-reinforcement methods are required as part of countermeasure/reinforcement/mitigation techniques, and new laboratory apparatus and methods with an optimizing structure should be developed as part of laboratory tests.

MR-Tree: A Mapping-based R-Tree for Efficient Spatial Searching (Mr-Tree: 효율적인 공간 검색을 위한 매핑 기반 R-Tree)

  • Kang, Hong-Koo;Shin, In-Su;Kim, Joung-Joon;Han, Ki-Joon
    • Spatial Information Research
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    • v.18 no.4
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    • pp.109-120
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    • 2010
  • Recently, due to rapid increasement of spatial data collected from various geosensors in u-GIS environments, the importance of spatial index for efficient search of large spatial data is rising gradually. Especially, researches based R-Tree to improve search performance of spatial data have been actively performed. These previous researches focus on reducing overlaps between nodes or the height of the R -Tree. However, these can not solve an unnecessary node access problem efficiently occurred in tree traversal. In this paper, we propose a MR-Tree(Mapping-based R-Tree) to solve this problem and to support efficient search of large spatial data. The MR-Tree can improve search performance by using a mapping tree for direct access to leaf nodes of the R-Tree without tree traversal. The mapping tree is composed with MBRs and pointers of R-Tree leaf nodes associating each partition which is made by splitting data area repeatedly along dimensions. Especially, the MR-Tree can be adopted in various variations of the R-Tree easily without a modification of the R-Tree structure. In addition, because the mapping tree is constructed in main memory, search time can be greatly reduced. Finally, we proved superiority of MR-Tree performance through experiments.

A Study on the Development of Field Management System for Underground utility using Self-levelling marker and DGPS. (자동수평마커와 DGPS를 이용한 지하시설물의 현장관리시스템 개발에 관한 연구)

  • Kim, In-Seup
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.27 no.6
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    • pp.733-739
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    • 2009
  • Recently it is being increased rapidly to install magnetic marker and RFID tag on the underground utility lines before backfilling to ensure effective it's management. However, due to changes an attitude and damages of sensors. By pressure and vibration during soil compacting, detecting rate is significantly reduced as well as it allows to use only one line of various pipes since it has an unique frequency. Also it is required too long time to reach to target point with an non-accurate navigational GPS receiver and hard to update existing data base with a manual input of new data in the field. To improve these problems, the researcher developed the field management system that apply with ball typed self-levelling marker which is free from the changes of attitude for sensors during compaction as well as has various radio frequency applicable to many kind of pipes and ensure fast positioning to target point using an incorporated system with PDA based DGPS receiver which allows loading a field GIS software and RFID detector. Further, it provides with viewing all of RFID data on the DGPS receiver screen directly and also input new data to existing data base in the field automatically.

An contention-aware ordered sequential collaborative spectrum sensing scheme for CRAHN (무선인지 애드 혹 네트워크를 위한 순차적 협력 스펙트럼 센싱 기법)

  • Nguyen-Thanh, Nhan;Koo, In-Soo
    • Journal of Internet Computing and Services
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    • v.12 no.4
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    • pp.35-43
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    • 2011
  • Cognitive Radio (CR) ad hoc network is highly considered as one of promising future ad hoc networks, which enables opportunistic access to under-utilized licensed spectrum. Similarly to other CR networks, the spectrum sensing is a prerequisite in CR ad hoc network. Collaborative spectrum sensing can help increasing sensing performance. For such an infrastructureless network, however the coordination for the sensing collaboration is really complicated due to the lack of a central controller. In this paper, we propose a novel collaborative spectrum sensing scheme in which the final decision is made by the node with the highest data reliability based on a sequential Dempster Shafer theory. The collaboration of sensing data is also executed by the proposed contention-aware reporting mechanism which utilizes the sensing data reliability order for broadcasting spectrum sensing result. The proposed method reduces the collecting time and the overhead of the control channel due to the efficiency of the ordered sequential combination while keeping the same sensing performance in comparison with the conventional cooperative centralized spectrum sensing scheme.

Development of Driver's Emotion and Attention Recognition System using Multi-modal Sensor Fusion Algorithm (다중 센서 융합 알고리즘을 이용한 운전자의 감정 및 주의력 인식 기술 개발)

  • Han, Cheol-Hun;Sim, Kwee-Bo
    • Journal of Korean Institute of Intelligent Systems
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    • v.18 no.6
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    • pp.754-761
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    • 2008
  • As the automobile industry and technologies are developed, driver's tend to more concern about service matters than mechanical matters. For this reason, interests about recognition of human knowledge and emotion to make safe and convenient driving environment for driver are increasing more and more. recognition of human knowledge and emotion are emotion engineering technology which has been studied since the late 1980s to provide people with human-friendly services. Emotion engineering technology analyzes people's emotion through their faces, voices and gestures, so if we use this technology for automobile, we can supply drivels with various kinds of service for each driver's situation and help them drive safely. Furthermore, we can prevent accidents which are caused by careless driving or dozing off while driving by recognizing driver's gestures. the purpose of this paper is to develop a system which can recognize states of driver's emotion and attention for safe driving. First of all, we detect a signals of driver's emotion by using bio-motion signals, sleepiness and attention, and then we build several types of databases. by analyzing this databases, we find some special features about drivers' emotion, sleepiness and attention, and fuse the results through Multi-Modal method so that it is possible to develop the system.

Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems (딥러닝 기반의 초분광영상 분류를 사용한 환경공간정보시스템 활용)

  • Song, Ahram;Kim, Yongil
    • Korean Journal of Remote Sensing
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    • v.33 no.6_2
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    • pp.1061-1073
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    • 2017
  • In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.

A Benchmark of Open Source Data Mining Package for Thermal Environment Modeling in Smart Farm(R, OpenCV, OpenNN and Orange) (스마트팜 열환경 모델링을 위한 Open source 기반 Data mining 기법 분석)

  • Lee, Jun-Yeob;Oh, Jong-wo;Lee, DongHoon
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • pp.168-168
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    • 2017
  • ICT 융합 스마트팜 내의 환경계측 센서, 영상 및 사양관리 시스템의 증가에도 불구하고 이들 장비에서 확보되는 데이터를 적절히 유효하게 활용하는 기술이 미흡한 실정이다. 돈사의 경우 가축의 복지수준, 성장 변화를 실시간으로 모니터링 및 예측할 수 있는 데이터 분석 및 모델링 기술 확보가 필요하다. 이를 위해선 가축의 생리적 변화 및 행동적 변화를 조기에 감지하고 가축의 복지수준을 실시간으로 감시하고 분석 및 예측 기술이 필요한데 이를 위한 대표적인 정보 통신 공학적 접근법 중에 하나가 Data mining 이다. Data mining에 대한 연구 수행에 필요한 다양한 소프트웨어 중에서 Open source로 제공이 되는 4가지 도구를 비교 분석하였다. 스마트 돈사 내에서 열환경 모델링을 목표로 한 데이터 분석에서 고려해야할 요인으로 데이터 분석 알고리즘 도출 시간, 시각화 기능, 타 라이브러리와 연계 기능 등을 중점 적으로 분석하였다. 선정된 4가지 분석 도구는 1) R(https://cran.r-project.org), 2) OpenCV(http://opencv.org), 3) OpenNN (http://www.opennn.net), 4) Orange(http://orange.biolab.si) 이다. 비교 분석을 수행한 운영체제는 Linux-Ubuntu 16.04.4 LTS(X64)이며, CPU의 클럭속도는 3.6 Ghz, 메모리는 64 Gb를 설치하였다. 개발언어 측면에서 살펴보면 1) R 스크립트, 2) C/C++, Python, Java, 3) C++, 4) C/C++, Python, Cython을 지원하여 C/C++ 언어와 Python 개발 언어가 상대적으로 유리하였다. 데이터 분석 알고리즘의 경우 소스코드 범위에서 라이브러리를 제공하는 경우 Cross-Platform 개발이 가능하여 여러 운영체제에서 개발한 결과를 별도의 Porting 과정을 거치지 않고 사용할 수 있었다. 빌트인 라이브러리 경우 순서대로 R 의 경우 가장 많은 수의 Data mining 알고리즘을 제공하고 있다. 이는 R 운영 환경 자체가 개방형으로 되어 있어 온라인에서 추가되는 새로운 라이브러리를 클라우드를 통하여 공유하기 때문인 것으로 판단되었다. OpenCV의 경우 영상 처리에 강점이 있었으며, OpenNN은 신경망학습과 관련된 라이브러리를 소스코드 레벨에서 공개한 것이 강점이라 할 수 있다. Orage의 경우 라이브러리 집합을 제공하는 것에 중점을 둔 다른 패키지와 달리 시각화 기능 및 망 구성 등 사용자 인터페이스를 통합하여 운영한 것이 강점이라 할 수 있다. 열환경 모델링에 요구되는 시간 복잡도에 대응하기 위한 부가 정보 처리 기술에 대한 연구를 수행하여 스마트팜 열환경 모델링을 실시간으로 구현할 수 있는 방안 연구를 수행할 것이다.

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A USN Based Mobile Object Tracking System for the Prevention of Missing Child (미아방지를 위한 USN 기반 보호대상 이동체 위치확인 시스템)

  • Cha, Maeng-Q;Jung, Dae-Kyo;Kim, Yoon-Kee;Chong, Hak-Jin
    • Journal of KIISE:Information Networking
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    • v.35 no.5
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    • pp.453-463
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    • 2008
  • The missing child problem is no more a personal problem. It became a social problem that all parents must consider. To this, this study applies USN/RFID technology integrated with GIS for the prevention of missing child. Although RFID is not designed for location sensing, but now it is regarded as a device to facilitate real time location awareness. Such advantages of RFID can be integrated with 4S(GIS/GPS/LBS/GNSS) achieving much synergy effects. In order to prevent kidnapping and missing child, it is necessary to provide a missing child preventing system using a ubiquitous computing system. Therefore, the missing child preventing system has been developed using high-tech such as RFID, GPS network, CCTV, and mobile communication. The effectiveness of the missing child prevention system can be improved through an accurate location tracking technology. This study propose and test a location sensing system using the active RFID tags. This study verifies technical applied service, and presents a system configuration model. Finally, this paper confirms missing child prevention system utilization possibility.

Construction of a Transgenic Plant to Develop a New Method for the Isolation of Calmodulin-Binding Proteins (새로운 방법을 이용한 칼모둘린 결합 단백질 분리를 위한 형질 전환 식물체의 구축)

  • Kim, Sun-Ho;Lee, Kyung-Hee;Kim, Kyung-Eun;Jung, Mi-Soon;Lim, Chae-Oh;Lee, Shin-Woo;Chung, Woo-Sik
    • Journal of Life Science
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    • v.17 no.9
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    • pp.1177-1181
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    • 2007
  • Calmodulin (CaM), a ubiquitous calcium-binding protein, regulates diverse cellular functions by modulating the activity of a variety CaM-binding proteins (CaMBPs). Because eukaryotes have multiple CaMBPs, it is important to isolate and characterize them in different tissues and conditions. So far a number of CaMBPs have been identified through classical screening methods. Many classes of proteins have been predicted to bind CaMs based on their structural homology with already known targets. In an effort to develop a method for large-scale analysis of CaMBPs in Arabidopsis, we have generated a transgenic plants overexpressing AtCaM2-GFP. We performed protein pull-down assay to test whether exogenously expressed AtCaM2-GFP proteins can interact with CaMBPs. The exogenously expressed AtCaM2-GFP could strongly interact with a CaMBP, AS1 protein. This result suggests that AtCaM2-GFP in transgenic plants may interact with many CaMBPs in plant cell. Therefore, we will be able to isolate kinds of CaMBPs by using these transgenic plants in many different tissue and environments.