• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.221-224
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• 2001

In this paper, we propose the initial optimized structure of the Radial Basis Function Network which is more simple in the part on the structure and converges more faster than Neural Network with the analysis method using Time-Frequency Localization and genetic algorithm. When we construct the hidden node with the Radial Basis Function whose localization is similar with an approximation target function in the plane of the Time and Frequency, we have initial structure of RBFN, After that, we evaluate the parameters of RBF in the network and the parameters needed for the network is more a few. Finally, we make a good decision of the initial structure having an ability of approximation.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.91-92
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• 2008

Localization and its applications are very important area of the mobile robot technology. Especially, accurate localization is needed when we move the mobile robot to the goal position. In indoor cases, Global Positioning System(GPS) is not suitable but Radio Frequency Identification(RFID) technology can provide position data to the robot. A proposed algorithm in this paper uses not only odometry data but also RFID data to improve estimation of true position of the robot with the particle filtering.

• Earthquakes and Structures
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• v.8 no.5
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• pp.1113-1126
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• 2015

A damage localization algorithm based on Operational Deformed Shapes and known as Interpolation Damage Detection Method, is herein applied to the finite element model of a cable stayed bridge for detecting and localizing damages in the stays and the supporting steel beams under the bridge deck. Frequency Response Functions have been calculated basing on the responses of the bridge model to low intensity seismic excitations and used to recover the Operational Deformed Shapes both in the transversal and in the vertical direction. The analyses have been carried in the undamaged configuration and repeated in several different damaged configurations. Results show that the method is able to detect the damage and its correct location, provided an accurate estimation of the Operational Deformed Shapes is available. Furthermore, the damage detection algorithm results effective also when damages coexist at the same time at several location of the cable-stayed bridge members.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.180-191
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• 2002

We present a new text localization method in images using a multi-layer perceptron(MLP) and a multiple continuously adaptive mean shift (MultiCAMShift) algorithm. An automatically constructed MLP-based texture classifier generates a text probability image for various types of images without an explicit feature extraction. The MultiCAMShift algorithm, which operates on the text probability Image produced by an MLP, can place bounding boxes efficiently without analyzing the texture properties of an entire image.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.550-557
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• 2004

In this paper, structural damage in PSC bridges is monitored by using model-based damage detection methods. First numerical experiments on the test structure are described. Dynamic responses of the test structures are obtained fur several damage scenarios. The change in natural frequency and the change in nude shape curvature are selected as features to represent the states of the structure. Next a damage localization algorithm from monitoring the changes in natural frequency is outlined. Also, the damage localization algorithm from monitoring the changes in nude shapes is outlined. Finally, the damage localization algorithms are used to predict damage in the test structure. The results of the analysis indicate that the model-based damage detection methods correctly predicted damage in the test structure.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.69-78
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• 2009

Localization is a very important technique for the mobile robot to navigate in outdoor environment. In this paper, the development of the sensor fusion algorithm for controlling mobile robots in outdoor environments is presented. The multi-sensorial dead-reckoning subsystem is established based on the optimal filtering by first fusing a heading angle reading data from a magnetic compass, a rate-gyro, and two encoders mounted on the robot wheels, thereby computing the dead-reckoned location. These data and the position data provided by a global sensing system are fused together by means of an extended Kalman filter. The proposed algorithm is proved by simulation studies of controlling a mobile robot controlled by a backstepping controller and a cascaded controller. Performances of each controller are compared.

• Journal of IKEEE
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• v.11 no.4
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• pp.297-306
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• 2007

A sensor network is composed of a large number of sensor nodes that are densely deployed in a field. Each sensor performs a sensing task for detection specific events. After detecting this event, location information of the sensor node is very important. Range-based scheme of the proposed approaches typically achieve high accuracy on either node-to-node distances or angles, but this scheme have a drawback because all sensor nodes have the special hardware. On the other hand, range-free scheme provides economic advantage because of no needed hardware even if that leads to coarse positioning accuracy. In this paper, we propose a range-free localization algorithm without range information in wireless sensor networks. This is a range-free approach and uses a small number of anchor nodes and known sensor nodes. This paper develops a localization mechanism using the geometry conjecture (perpendicular bisector of a chord). The conjecture states that a perpendicular bisector of a chord passes through the center of the circle.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.709-715
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• 2010

This paper represents a map building and localization system for mobile robot. Map building and navigation is a complex problem because map integrity cannot be sustained by odometry alone due to errors introduced by wheel slippage, distortion and simple linealized odometry equation. For accurate localization, we propose sensor fusion system using encoder sensor and indoor GPS module as relative sensor and absolute sensor, respectively. To build a map, we developed a sensor based navigation algorithm and grid based map building algorithm based on Embedded Linux O.S. A wall following decision engine like an expert system was proposed for map building navigation. We proved this system's validity through field test.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.133-138
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• 2011

RFID system can be used to improve object recognition, map building and localization for robot area. A novel method of indoor navigation system for a mobile robot is proposed using RFID technology. The mobile robot With a RFID reader and antenna is able to find what obstacles are located where in circumstance and can build the map similar to indoor circumstance by combining RFID information and distance data obtained from sensors. Using the map obtained, the mobile robot can avoid obstacles and finally reach the desired goal by $A^*$ algorithm. 3D map which has the advantage of robot navigation and manipulation is able to be built using z dimension of products. The proposed robot navigation system is proved to apply for SLAM and path planning in unknown circumstance through numerous experiments.