• ETRI Journal
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• v.35 no.5
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• pp.757-766
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• 2013

This paper deals with the estimation of the time of arrival (TOA) of ultra-wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses a threshold that is derived analytically according to a preset false alarm probability. To highlight the effectiveness of the developed approach, simulation setups as well as real data experiments are conducted to assess the performance of the new TOA estimation algorithm. Thereby, the proposed method is compared with the cell averaging constant false alarm rate technique, the threshold comparison algorithm, and the technique based on maximum energy selection with search back. The obtained results are promising, considering both simulations and collected real-life data.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.457-461
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• 2013

This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.753-758
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• 2016

In this paper, we propose a novel method for estimating a vehicle's current position, even on roads that have similar patterns. In the proposed method, we classified the semantic information of the nodes in detail and added the semantic information of the link to solve the problem due to similar and repeated patterns. We also improved the mapping method by comparing the result of the duplicated matching with that of the only matching obtained just before corresponding duplicated matching. From the simulation results, we verify that the performance of the proposed method is better than that of the existing method.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.3
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• pp.173-182
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• 2015

A real-time, Indoor navigation systems utilize ultra-wide band (UWB), radio-frequency identification (RFID) and received signal strength (RSS) techniques that encompass WiFi, FM, mobile communications, and other similar technologies. These systems typically require surplus infrastructure for their implementation, which results in significantly increased costs and complexity. Therefore, as a solution to reduce the level of cost and complexity, an inertial measurement unit (IMU) and quick response (QR) codes are utilized in this paper to facilitate navigation with the assistance of a smartphone. The QR code helps to compensate for errors caused by the pedestrian dead reckoning (PDR) algorithm, thereby providing more accurate localization. The proposed algorithm having IMU in conjunction with QR code shows an accuracy of 0.64 m which is higher than existing indoor navigation techniques.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.686-695
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• 2012

This paper proposes a noncontact thermography technique for fatigue crack evaluation under a cyclic tensile loading. The proposed technique identifies and localizes an invisible fatigue crack without scanning, thus making it possible to instantaneously evaluate an incipient fatigue crack. Based on a thermoelastic theory, a new fatigue crack evaluation algorithm is proposed for the fatigue crack-tip localization. The performance of the proposed algorithm is experimentally validated. To achieve this, the cyclic tensile loading is applied to a dog-bone shape aluminum specimen using a universal testing machine, and the corresponding thermal responses induced by thermoelastic effects are captured by an infrared camera. The test results confirm that the fatigue crack is well identified and localized by comparing with its microscopic images.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.298-305
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• 2012

In this paper, we propose an integrated algorithm for scene recognition, which has been a challenging computer vision problem, with application to mobile robot localization. The proposed scene recognition method utilizes SIFT and visual words as local-level features and GIST as a global-level feature. As local-level and global-level features complement each other, it results in improved performance for scene recognition. This improved algorithm is of low computational complexity and robust to image distortions.

• Wind and Structures
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• v.21 no.6
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• pp.657-675
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• 2015

In this study, the feasibility of vibration-based damage detection methods for the wind turbine tower (WTT) structure is evaluated. First, a frequency-based damage detection (FBDD) is outlined. A damage-localization algorithm is visited to locate damage from changes in natural frequencies. Second, a mode-shape-based damage detection (MBDD) method is outlined. A damage index algorithm is utilized to localize damage from estimating changes in modal strain energies. Third, a finite element (FE) model based on a real WTT is established by using commercial software, Midas FEA. Several damage scenarios are numerically simulated in the FE model of the WTT. Finally, both FBDD and MBDD methods are employed to identify the damage scenarios simulated in the WTT. Damage regions are chosen close to the bolt connection of WTT segments; from there, the stiffness of damage elements are reduced.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.7-13
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• 2017

We consider a distributed estimation where many nodes remotely placed at known locations collect the measurements of the parameter of interest, quantize these measurements, and transmit the quantized data to a fusion node; this fusion node performs the parameter estimation. Noting that quantizers at nodes should operate in a non-regular framework where multiple codewords or quantization partitions can be mapped from a single measurement to improve the system performance, we propose a low-weight estimation algorithm that finds the most feasible combination of codewords. This combination is found by computing the weighted sum of the possible combinations whose weights are obtained by counting their occurrence in a learning process. Otherwise, tremendous complexity will be inevitable due to multiple codewords or partitions interpreted from non-regular quantized data. We conduct extensive experiments to demonstrate that the proposed algorithm provides a statistically significant performance gain with low complexity as compared to typical estimation techniques.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.127-130
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• 2006

This paper suggests an algorithm that can estimate the direction of the sound source with three microphones arranged on a circle. The algorithm is robust to microphones' gains because it uses only the time differences between microphones. To make this possible, a cost function which normalizes the microphone's gains is utilized and a procedure to detect the rough position of the sound source is also proposed. Through our experiments, we obtained significant performance improvement compared with the energy-based localizer.