• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.418-419
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• 2022

In this paper, the design of the fusion sensor mechanism was divided into primary and secondary design, and the device design and case design were divided to facilitate the safety of the sensor, sensor coupling, and connection with power.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.

• Journal of Sensor Science and Technology
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• v.5 no.4
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• pp.57-70
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• 1996

Motion can be estimated continuously from each sensor through the analysis of the instantaneous states of an object. This paper is aimed to introduce a method to estimate the general 3D motion of a planar object from the instantaneous states of an object using multi-sensor data fusion. The instantaneous states of an object is estimated using the linear feedback estimation algorithm. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown planar object. We present a fusion algorithm which combines averaging and deciding. With the assumption that the motion is smooth, the approach can handle the data sequences from multiple sensors with different sampling times. Simulation results show proposed algorithm is advantageous in terms of accuracy, speed, and versatility.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.639-645
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• 2010

This paper presents the sensor fusion algorithm that recognizes a primary vehicle by fusing radar and monocular vision data. In general, most of commercial radars may lose tracking of the primary vehicle, i.e., the closest preceding vehicle in the same lane, when it stops or goes with other preceding vehicles in the adjacent lane with similar velocity and range. In order to improve the performance degradation of radar, vehicle detection information from vision sensor and path prediction predicted by ego vehicle sensors will be combined for target classification. Then, the target classification will work with probabilistic association filters to track a primary vehicle. Finally the performance of the proposed sensor fusion algorithm is validated using field test data on highway.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.173-180
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• 2000

During the last decade many researches have been working on multiple target tracking problem in the area of radar application, Various approaches have been proposed to solve the tracking problem and the concept of sensor fusion was established as an effort. In this paper utilization of geographic information for ground target tracking is investigated and performance comparison with the results of applying sensor fusion is described. Geographic information is used in three aspects: association masking target measurement and re-striction of removing true target. Simulation results indicate that using two sensors shows better performance with respect to tracking but a single with geographic information is a winner in reducing the number of false tracks.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.257-262
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• 2004

The purpose of data fusion is collecting maximized information from combining the data attained from more than two same or different kind sensor systems. Data fusion of same kind sensor systems like optical imagery has been on focus, but recently, LIDAR emerged as a new technology for capturing rapidally data on physical surfaces and the high accuray results derived from the LIDAR data. Considering the nature of aerial imagery and LIDAR data, it is clear that the two systems provide complementary information. Data fusion is consisted of two steps, alignment and matching. However, the complementary information can only be fully utilized after sucessful alignment of the aerial imagery and lidar data. In this research, deal with centroid of building extracted from lidar data as control information for estimating exterior orientation parameters of aerial imagery relative to the LIDAR reference frame.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1040-1047
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• 2011

This paper proposes a novel method based on error back propagation neural networks to fuse laser sensor data and ultrasonic sensor data for enhancing the accuracy of mapping. For navigation of single robot, the robot has to know its initial position and accurate environment information around it. However, due to the inherent properties of sensors, each sensor has its own advantages and drawbacks. In our system, the robot equipped with seven ultrasonic sensors and a laser sensor navigates to map two different corridor environments. The experimental results show the effectiveness of the heterogeneous sensor fusion using an error backpropagation algorithm for mapping.