• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.124-132
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• 2022

With growth in intelligence of mobile robots, interaction with humans is emerging as a very important issue for mobile robots and the pedestrian tracking technique following the designated person is adopted in many cases in a way that interacts with humans. Among the existing multi-object tracking techniques for pedestrian tracking, Simple Online and Realtime Tracking (SORT) is suitable for small mobile robots that require real-time processing while having limited computational performance. However, SORT fails to reflect changes in object detection values caused by the movement of the mobile robot, resulting in poor tracking performance. In order to solve this performance degradation, this paper proposes a more stable pedestrian tracking algorithm by correcting object tracking errors caused by robot movement in real time using wheel odometry information of a mobile robot and dynamically managing the survival period of the tracker that tracks the object. In addition, the experimental results show that the proposed methodology using data collected from actual mobile robots maintains real-time and has improved tracking accuracy with resistance to the movement of the mobile robot.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.167-176
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• 2023

This study presents the performance analysis of real-time orbit determination and prediction for navigation message generation of Regional Navigation Satellite System (RNSS). Since the accuracy of ephemeris and clock correction in navigation message affects the positioning accuracy of the user, it is essential to construct a ground segment that can generate this information precisely when designing a new navigation satellite system. Based on a real-time architecture by an extended Kalman filter, we simulated orbit determination and prediction of RNSS satellites in order to assess the accuracy of orbit and clock prediction and signal-in-space ranging errors (SISRE). As a result of the simulation, the orbit and clock accuracy was at 0.5 m and 2 m levels for 24 hour determination and six hour prediction after the determination, respectively. From the prediction result, we verified that the SISRE of RNSS for six hour prediction was at a 1 m level.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.9-16
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• 2010

Moving object detection is to detect foreground object different from background scene in a new incoming image frame and is an essential ingredient process in some image processing applications such as intelligent visual surveillance, HCI, object-based video compression and etc. Most of previous object detection algorithms are still computationally heavy so that it is difficult to develop real-time multi-channel moving object detection in a workstation or even one-channel real-time moving object detection in an embedded system using them. Foreground mask correction necessary for a more precise object detection is usually accomplished using morphological operations like opening and closing. Morphological operations are not computationally cheap and moreover, they are difficult to be rendered to run simultaneously with the subsequent connected component labeling routine since they need quite different type of processing from what the connected component labeling does. In this paper, we first devise a fast and precise foreground mask correction algorithm, "Neighbor Foreground Pixel Propagation (NFPP)" which utilizes neighbor pixel checking employed in the connected component labeling. Next, we propose a novel moving object detection method based on the devised foreground mask correction algorithm, NFPP where the connected component labeling routine can be executed simultaneously with the foreground mask correction. Through experiments, it is verified that the proposed moving object detection method shows more precise object detection and more than 4 times faster processing speed for a image frame and videos in the given the experiments than the previous moving object detection method using morphological operations.

• Structural Engineering and Mechanics
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• v.50 no.1
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• pp.37-52
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• 2014

The accurate determination of cable tension is important to the monitoring of the condition of a cable-stayed bridge. When applying a vibration-based formula to identify the tension of a real cable under sag, stiffness and boundary conditions, the resulting error must not be overlooked. In this work, by resolving the implicit frequency function of a real cable under the above conditions numerically, indirect methods of determining the cable force and a method to calculate the corresponding cable mode frequency are investigated. The error in the tension is studied by numerical simulation, and an empirical error correction formula is presented by fitting the relationship between the cable force error and cable parameters ${\lambda}^2$ and ${\xi}$. A case study on two real cables of the Shanghai Changjiang Bridge shows that employing the method proposed in this paper can increase the accuracy of the determined cable force and reduce the computing time relative to the time required for the finite element model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1116-1132
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• 2010

In this paper, a new forward error correction (FEC) protocol is proposed for point-to-multipoint satellite links. Link-layer error control protocols in point-to-multipoint satellite links impose several problems such as unreliability and receiver-heterogeneity. To resolve the problem of heterogeneous error rates at different receivers, the proposed scheme exploits multiple multicast channels to which each receiver tunes. The more channels a receiver tunes to, the more powerful error correcting capability it achieves. Based on its own channel condition, each receiver tunes to as many channels as it needs, which prevents from receiving unwanted parities. Furthermore, each receiver saves the decoding time, processing overhead, and processing energy. Performance evaluation shows that the proposed scheme guarantees the target PER while saving energy. The proposed technique is highly adaptive to the channel variation with respect to the throughput efficiency, and provides scalable PER and throughput efficiency.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.4
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• pp.17-26
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• 2013

In this paper, a configuration procedure of a transportation infrastructure system for GNSS based very precise real-time positioning is proposed. This infrastructure system consists of several receiving station, a central station, and communication sub-systems. The required performance, design, implementation and verification of each sub-system are explained respectively. The required performance can be broken down into accuracy, integrity, stability, processing time. The design of the each sub-system is performed in accordance with the required performance and each sub-system is built with regard to the design. Lastly the implemented system is verified in comparison with the required performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.940-947
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• 2015

A Sitting posture is a very important issue for moderns who is mostly sedentary. Also, a wrong sitting posture causes back-pain and spinal disease. Many researchers have been proposed numerous approaches that classifying and monitoring for a sitting posture. In this paper, we proposed a real-time sitting posture monitoring system that was developed to measure pressure distribution in the human body. The proposed system consists of a pressure sensing module (six pressure sensors), data acquisition and processing module, a communication module and a display module for an individual sitting posture monitoring. The developed monitoring system can classify into five sitting postures, such as a correct sitting, sitting on forward inclination, leaning back sitting, sitting with a right leg crossed and a left leg crossed. In addition, when a user deviates from the correct posture, an alarm function is activated. We selected two kinds of chairs, one is rigid material and fixed form, the other one is a soft material and can adjust the height of a chair. In the experiments, we observed appearance changes for subjects in consequence of a comparison between before the correction of posture and after the correction of posture when using the proposed system. The data from twenty four subjects has been classified with a proposed classifier, achieving an average accuracy of 83.85%, 94.56% when the rigid chair and the soft chair, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.14-19
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• 2020

This paper proposes a precise drone-positioning technique using a differential global positioning system (DGPS). The proposed system consists of a reference station for error correction data production, and a mobile station (a drone), which is the target for real-time positioning. The precise coordinates of the reference station were acquired by post-processing of received satellite data together with the reference station location data provided by government infrastructure. For the system's implementation, low-cost commercial GPS receivers were used. Furthermore, a Zigbee transmitter/receiver pair was used to wirelessly send control signals and error correction data, making the whole system affordable for personal use. To validate the system, a drone-tracking experiment was conducted. The results show that the average real-time position error is less than 0.8 m.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.71-77
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• 2006

MSAS satellite is the geostationary satellite for realizing WADGPS that can get the position of moving object in a wide area receiving the correction signal created from a ground using satellite. In this study, we analyzed two different data. One is using the correction signal transmitted from MTSAT-2 satellite of MSAS and the other is receiving the data of DGPS using BEACON receiver. As we compared both data, we could get the conclusion that the position accuracy of both data is also can get up to the standard or the conventional real-time code DGPS. As a result, we can expect that if we use MTSAT-2 satellite and BEACON receiver together, we can apply them LBS part that require real-time data or the obtaining geospatial information that does not require high accuracy much regardless of topography.