• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.273-283
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• 2008

This article describes a cooperative localization technique of multiple robots sharing position information of each robot. In case of conventional methods such as EKF, they need to linearization process. Consequently, they are not able to guarantee that their result is range containing true value. In this paper, we propose a method to merge the data of redundant sensors based on constraints propagation techniques on intervals. The proposed method has a merit guaranteeing true value. Especially, we apply the constraints propagation technique fusing wheel encoders, a gyro, and an inexpensive GPS receiver. In addition, we utilize the correlation between GPS data in common workspace to improve localization performance for multiple robots. Simulation results show that proposed method improve considerably localization performance of multiple robots.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.5-36
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• 2001

Localization is one of the key problems in the navigation of autonomous mobile robots. The probabilistic Markov localization approaches offer a good mathematical framework to deal with the uncertainty of environment and sensor readings but their use for realtime applications is limited by their computational complexity. This paper aims to reduce the high computational cost associated with the probabilistic Markov localization algorithm. We propose a hybrid landmark-based localization method combining triangulation and probabilistic approaches, which can efficiently update position probability grid, while the probabilistic framework allows to make use of any available sensor data to refine robot´s belief about its current location. The simulation results show the effectiveness and robustness of the method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.916-921
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• 2011

Mobile robot localization is the task of estimating the robot pose in a given environment. This research deals with outdoor localization based on an elevation map. Since outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose. This paper proposes a Hausdorff distance-based map matching method. The Hausdorff distance is exploited to measure the similarity between extracted features obtained from the robot and elevation map. The experiments and simulations show that the proposed Hausdorff distance-based map matching is useful for robust outdoor localization using an elevation map. Also, it can be easily applied to other probabilistic approaches such as a Markov localization method.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.433-438
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• 2007

This paper presents a distance mapping-based localization method with incomplete data which means partially observed data. We make three contributions. First, we propose the use of Multi Dimensional Scaling (MDS) for multi-robot localization. Second, we formulate the problem to accomodate partial observations common in multi-robot settings. We solve the resulting optimization problem using #Scaling by Majorizing a Complicated function (SMACOF)#, a popular algorithm fur iterative MDS. Third, we not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.731-740
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• 2012

To offer indoor location-aware services, the needs for efficient and accurate indoor localization system has been increased. In order to meet these requirement, we presented the BLIDx(Bidirectional Location ID exchange) protocol that is efficient localization system based on sensor network. The BLIDx protocol can cope with numerous mobile nodes simultaneously but the precision of the localization is too coarse because that uses cell based localization method. In this paper, in order to compensate for these disadvantage, we propose the fingerprint overlapping method by modifying a fingerprinting methods in WLAN, and localization system using proposed method was designed and implemented. Our experiments show that the proposed method is more accurate and robust to noise than fingerprinting method in WLAN. In this way, it was improved that low location precision of BLIDx protocol.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.883-887
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• 2011

Location-based service (LBS) is becoming an important part of the information technology (IT) business. Localization is a core technology for LBS because LBS is based on the position of each device or user. In case of outdoor, GPS - which is used to determine the position of a moving user - is the dominant technology. As satellite signal cannot reach indoor, GPS cannot be used in indoor environment. Therefore, research and study about indoor localization technology, which has the same accuracy as an outdoor GPS, is needed for "seamless LBS". For indoor localization, we consider the IEEE802.11 WLAN environment. Generally, received signal strength indicator (RSSI) is used to obtain a specific position of the user under the WLAN environment. RSSI has a characteristic that is decreased over distance. To use RSSI at indoor localization, a mathematical model of RSSI, which reflects its characteristic, is used. However, this RSSI of the mathematical model is different from a real RSSI, which, in reality, has a sensitive parameter that is much affected by the propagation environment. This difference causes the occurrence of localization error. Thus, it is necessary to set a proper RSSI model in order to obtain an accurate localization result. We propose a method in which the parameters of the propagation environment are determined using only RSSI measurements obtained during localization.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1182-1188
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• 2010

This paper proposes a precise localization algorithm for a quickly moving mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the flight time of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high speed mobile robot operations, instead of using two active beacons for localization an active beacon and dual compass are utilized to localize the mobile robot. This new approach resolves the high localization error caused by the speed of the mobile robot. The performance of the precise localization algorithm was verified by comparing it to the conventional method through real-world experiments.

• Smart Structures and Systems
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• v.10 no.3
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• pp.229-251
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• 2012

Guided waves have shown a great potential for structural health monitoring (SHM) applications. In contrast to traditional non-destructive testing (NDT) methodologies, a key element of SHM approaches is the high process of automation. The monitoring system should decide autonomously whether the host structure is intact or not. A basic requirement for the realization of such a system is that the sensors are permanently installed on the host structure. Thus, baseline measurements become available that can be used for diagnostic purposes, i.e., damage detection, localization, etc. This paper contributes to guided wave-based inspection in anisotropic materials for SHM purposes. Therefore, computational strategies are described for both, the solution of the complex equations for wave propagation analysis in composite materials based on exact elasticity theory and the popular global matrix method, as well as the underlying equations of two active damage localization algorithms for anisotropic structures. The result of the global matrix method is an angular and frequency dependent wave velocity characteristic that is used subsequently in the localization procedures. Numerical simulations and experimental investigations through time-delay measurements are carried out in order to validate the proposed theoretical model. An exemplary case study including the calculation of dispersion curves and damage localization is conducted on an exemplary unidirectional composite structure where the ultrasonic signals processed in the localization step are simulated with the spectral element method. The proposed study demonstrates the capabilities of the proposed algorithms for accurate damage localization in anisotropic structures.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.1-10
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• 2007

In this paper, we propose a face recognition system using AAM and Gabor feature vectors. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization of facial feature points employed in EBGM is based on Gator jet similarity and is sensitive to initial points. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we propose a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based localization method with initial points set by the facial feature points estimated from AAM, and propose a face recognition system based on the proposed localization method. It is verified through experiments that the proposed face recognition system using the combined localization performs better than the conventional face recognition system using the Gabor similarity-based localization only like EBGM.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.203-211
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• 2021

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.