• Journal of KIISE
• /
• v.42 no.1
• /
• pp.54-67
• /
• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Journal of IKEEE
• /
• v.18 no.1
• /
• pp.57-63
• /
• 2014

This paper is to technically implement the sensing platform for Home-Wellness Robot. The self-Localization of indoor mobile robot is very important for the sophisticated trajectory control. In this paper, the robot's self-localization algorithm is designed by RF sensor network and fuzzy inference. The robot realizes its self-localization, using RFID sensors, through the collaboration algorithm which uses fuzzy inference for combining the strengths of triangulation and triangulation. For the triangulation self-Localization, RSSI is implemented. TOA method is used for realizing the triangulation self-localization. The final improved position is, through fuzzy inference, made by the fusion algorithm of the resultant coordinates from trilateration and triangulation in real time. In this paper, good performance of the proposed self-localization algorithm is confirmed through the results of a variety of experiments in the base of RFID sensor network and reader system.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.2
• /
• pp.865-871
• /
• 2011

Presently, the location and direction information are certainly needed for the autonomous vehicle of the ship. Among them, the direction information is a essential elements to automatic steering system. And the gyro-compass, the magnetic-compass and the GPS compass are the sensor indicating the direction. The gyro-compasses are mainly used in the large-sized ship of the GMDSS(Global Maritime Distress & Safety System). The precision and the reliability of the gyro-compasses are excellent but big volume and high price are disadvantage. The magnetic-compass has relatively fine precision and inexpensive price. However, the disadvantage is in the influence by the magnetism object including the steel structure of a ship, and etc. In the case of the GPS compass, the true north is indicated according to the change of the location information but in case of the minimum number of satellites or stopping of a ship or exercise in the error range, the exact direction cannot be obtained. In this paper, the performance of the GPS compass was improved by using the least-square curve fitting method for the mutual trade off of the angle sensor. The algorithm which improves the precision of an azimuth by applying the weighted value according to the size of covariance error was proposed with GPS-compass and magnetic compass. The characteristic and the performance of the proposed algorithm were analyzed and verified through experimentation. The applicability of the proposed algorithm was shown through the experimental result.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.43 no.6 s.312
• /
• pp.9-15
• /
• 2006

The recent development in robot field shows that service robot which interacts with human and provides specific service to human has been researched continually. Especially, robot for human welfare becomes the center of public concern. At present time, guide robot is priority field of general welfare robot and helps the blind keep safe path when he walks outdoor. In this paper, guide robot provides not only collision avoidance but also the best walking direction and velocity to blind people while recognizing environment information from various kinds of sensors. In addition, it is able to provide the most safe path planing on behalf of blind people.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.351-357
• /
• 2019

UAV (Unmanned Aerial Vehicle) images can be exploited for rapid forest fire damage assessment by virtue of UAV systems' advantages. In 2019, catastrophic forest fire occurred in Goseong and Sokcho, Korea and burned 1,757 hectares of forests. We visited the town in Goseong where suffered the most severe damage and conducted UAV flights for forest fire damage assessment. In this study, economic and rapid damage assessment method for forest fire has been proposed using UAV systems equipped with only a RGB sensor. First, forest masking was performed using automatic elevation thresholding to extract forest area. Then ExG (Excess Green) vegetation index which can be calculated without near-infrared band was adopted to extract damaged forests. In addition, entropy filtering was applied to ExG for better differentiation between damaged and non-damaged forest. We could confirm that the proposed forest masking can screen out non-forest land covers such as bare soil, agriculture lands, and artificial objects. In addition, entropy filtering enhanced the ExG homogeneity difference between damaged and non-damaged forests. The automatically detected damaged forests of the proposed method showed high accuracy of 87%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.422-424
• /
• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.04a
• /
• pp.653-655
• /
• 2016

본 논문에서는 키넥트와 압력센서를 이용하여 수면자의 수면자세를 인식할 수 있는 수면자세 모니터링 시스템을 제안하였다. 기존 수면 모니터링 시스템은 장시간 착용해야 하는 불편함과 구속감으로 인해 수면의 질을 저하시킬 우려가 있다. 이러한 점을 해소하기 위해 압력센서와 키넥트 카메라를 이용하여 무구속 저비용의 효율적인 시스템을 구현하였고, 수면 매트형식으로 제작하여 그 유효성을 평가하였다. 본 연구에서 제안한 수면자세 모니터링 시스템은 실시간으로 수면자세를 감지하고 사용자의 수면시간 및 상태를 파악하여 건강한 수면습관을 들이는 방법을 권고할 수 있다. 향후에는 수집된 데이터를 이용하여 웰니스 및 헬스케어 모바일 응용 서비스로의 활용이 가능할 것이다.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.6
• /
• pp.267-275
• /
• 2013

EKF has been widely used for GPS/INS integration as standard method but EKF has one well-known drawback. if the errors are not within the bounded region, the filter may be divergent. The particle filter has the advantage of the nonlinear and non-gaussian system. This paper proposes a method for compensating the GPS position errors based on the particle filter and presents loosely-coupled GPS/INS integration using proposed algorithm. We used GPS position pattern with particle filter and added attitude kalman filter for improving attitude accuracy. To verify the performance, the proposed method is compared with high cost GPS as reference. In the experimental result, we verified that the accuracy and robust were well improved by the proposed method filter effectively and robustness than by original loosely-coupled integration when vehicle turns at corner.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.05a
• /
• pp.867-868
• /
• 2013

본 논문은 물류창고와 같은 실내 공간에서 RFID와 초음파 센서등을 이용하여 이동로봇이 자율적으로 자신의 위치를 파악하고 관리자가 지정한 목표 물체를 인식하여 간단한 업무를 보조할 수 있는 기법을 제안한다. 실내공간엔 RFID를 지면과 목표물체에 설치하고 로봇은 RFID의 리더기와 물체 접근시 활용이 가능한 추가적 센서를 갖춤으로써 이동시 자기 위치를 실시간으로 파악하고 물체로부터도 고유정보를 얻는다. 초음파 센서 신호의 귀환시간을 활용하여 근접한 물체와의 상대 거리를 추출하고 바닥의 RFID로부터 이미 획득한 자기 위치를 조합하여 목표 물체의 절대 위치를 구한다. 이는 이동 로봇을 중심으로 한 경로지도를 실시간으로 작성하며 동시에 실내의 이동 가능 구조 및 목표 물체의 파악이 가능하여 이동로봇의 자율적 탐색을 위한 최적 경로 계획 수립에 활용 가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.131-147
• /
• 2020

RFID (Radio Frequency Identification) identifies a specific object by radio signals. As the tag provides a unique ID for the purpose of identification, RFID technology effectively solves the ambiguity and occlusion problem that challenges the laser or camera-based approach. This paper proposes an approach to track a moving object based on the integration of RFID and laser ranging information using a particle filter. To be precise, we split laser scan points into different clusters which contain the potential moving objects and calculate the radial velocity of each cluster. The velocity information is compared with the radial velocity estimated from RFID phase difference. In order to achieve the positioning of the moving object, we select a number of K best matching clusters to update the weights of the particle filter. To further improve the positioning accuracy, we incorporate RFID signal strength information into the particle filter using a pre-trained sensor model. The proposed approach is tested on a SCITOS service robot under different types of tags and various human velocities. The results show that fusion of signal strength and laser ranging information has significantly increased the positioning accuracy when compared to radial velocity matching-based or signal strength-based approaches. The proposed approach provides a solution for human machine interaction and object tracking, which has potential applications in many fields for example supermarkets, libraries, shopping malls, and exhibitions.