• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.65-73
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• 1997

This paper propose a new triangular mesh approximation method using wavelet coefficients for large terrain data. Using spatio-freguency localization characteristics of wavelet coefficients, we determine the complexity of terrain data and approximate the data according to the complexity. This proposed algorithm is simple and requires low computational cost due to its top-down approach. Because of the similarity between the mesh approximation and data compression procedures based on wavelet transform, we combine the mesh approximation scheme with the Embedded Zerotree Wavelet (EZW) coding scheme for the effective management of large terrain data. Computer simulation results demonstrate that the proposed algorithm is very prospective for the 3-D visualization of terrain data.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.666-679
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• 2018

The discrete wavelet transform (DWT) has good multi-resolution decomposition characteristic and its low frequency component contains the basic information of an image. Based on this, a fragile watermarking using the local binary pattern (LBP) and DWT is proposed for image authentication. In this method, the LBP pattern of low frequency wavelet coefficients is adopted as a feature watermark, and it is inserted into the least significant bit (LSB) of the maximum pixel value in each block of host image. To guarantee the safety of the proposed algorithm, the logistic map is applied to encrypt the watermark. In addition, the locations of the maximum pixel values are stored in advance, which will be used to extract watermark on the receiving side. Due to the use of DWT, the watermarked image generated by the proposed scheme has high visual quality. Compared with other state-of-the-art watermarking methods, experimental results manifest that the proposed algorithm not only has lower watermark payloads, but also achieves good performance in tamper identification and localization for various attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5112-5128
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• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.4
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• pp.89-95
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• 2003

In this paper, an algorithm to locate and size damage in a complex truss structure using the time domain response is presented. Sampled response data for specific time interval is spatially expanded over the structure to obtain the mean train energy for each element of the structure. The mean strain energy for each element is, in turn, used to build a damage index that represents the ratio of the stiffness parameter of the pre-damaged to the post-damaged structure. The validity of the methodology is demonstrated using data from a numerical example of a space truss structure with simulated damage. Also in the example, the effects of noisy data on the proposed algorithm are examined by adding random noised to the response data.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.698-703
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• 2006

This paper proposes an approach to search for the odor source using an autonomous mobile robot equipped with vision and odor sensors. The robot is initially navigating around the specific area with vision system until it looks for an object in the camera image. The robot approaches the object found in the field of view and checks it with the odor sensors if it is releasing odor. If so, the odor is classified and localized with the classification algorithm based on neural network The AMOR(Autonomous Mobile Olfactory Robot) was built up and used for the experiments. Experimental results on the classification and localization of odor sources show the validity of the proposed algorithm.

• Smart Structures and Systems
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• v.5 no.1
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• pp.43-54
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• 2009

This study introduces a novel approach for locating damage in a structure using wireless sensor system with local level computational capability to alleviate data traffic load on the centralized computation. Smart wireless sensor systems, capable of iterative damage-searching, mimic an optimization process in a decentralized way. The proposed algorithm tries to detect damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides a reasonably effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Since all of the damage searching process occurs within a small group of wireless sensors, no global control or data traffic to a central system is required. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

• Computers and Concrete
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• v.27 no.1
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• pp.29-39
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• 2021

This study aimed to develop a method for the determination of the damaged story in reinforced concrete (RC) structure with ambient vibrations, based on modified jerk energy methodology. The damage was taken as a localized reduction in the stiffness of the structural member. For loading, random white noise excitation was used, and dynamic responses from the finite element model (FEM) of 4 story RC shear frame were extracted at nodal points. The data thus obtained from the structure was used in the damage detection and localization algorithm. In the structure, two damage configurations have been introduced. In the first configuration, damage to the structure was artificially caused by a local reduction in the modulus of elasticity. In the second configuration, the damage was caused, using the Elcentro1940 and Kashmir2005 earthquakes in real-time history. The damage was successfully detected if the frequency drop was greater than 5% and the mode shape correlation remained less than 0.8. The results of the damage were also compared to the performance criteria developed in the Seismostruct software. It is demonstrated that the proposed algorithm has effectively detected the existence of the damage and can locate the damaged story for multiple damage scenarios in the RC structure.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.416-422
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• 2021

Due to the large differences in human activity within classes, the large similarity between classes, and the problems of visual angle and occlusion, it is difficult to extract features manually, and the detection rate of human behavior is low. In order to better solve these problems, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multi-object recognition and localization through a second-order detection network, and replaces the original feature extraction module with Dense-Net, which can fuse multi-level feature information, increase network depth and avoid disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects, and enhancing the network detection accuracy under multiple objects. During the experiment, the improved Faster R-CNN method in this article has 84.7% target detection result, which is improved compared to other methods, which proves that the target recognition method has significant advantages and potential.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.806-816
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• 2009

Character recognition system consists of four step; text localization, text segmentation, character segmentation, and recognition. The character segmentation is very important and difficult because of noise, illumination, and so on. For high recognition rates of the system, it is necessary to take good performance of character segmentation algorithm. Many algorithms for character segmentation have been developed up to now, and many people have been recently making researches in segmentation of touching or overlapping character. Most of algorithms cannot apply to the text regions of management number marked on the slab in steel image, because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in various cases. This paper describes a new algorithm of character segmentation to recognize slab management number marked on the slab in the steel image. It is very important that pre-processing step is to convert gray image to binary image without loss of character and touching character. In this binary image, non-touching characters are simply separated by using vertical projection profile. For separating touching characters, after we use combined profile to find candidate points of boundary, decide real character boundary by using method based on recognition. In recognition step, we remove noise of character images, then recognize respective character images. In this paper, the proposed algorithm is effective for character segmentation and recognition of various text regions on the slab in steel image.

• Ocean and Polar Research
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• v.33 no.1
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• pp.69-76
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• 2011

A test miner named MineRo was constructed for the purpose of shallow water test of mining performance. In June of 2009, the performance test was conducted in depth of 100 m, 5 km away from Hupo-port (Korean East Sea), to assess if the developed system is able to collect and lift manganese nodules from seafloor. In August of 2010, in-situ test of automatic path tracking control of MineRo was performed in depth of 120 m at the same site. For path tracking control, a localization algorithm determining MineRo's position on seabed is prerequisite. This study proposes an improved underwater navigation algorithm through estimation of MineRo's kinematic parameters. In general, the kinematic parameters such as track slips and slip angle are indirectly calculated using the position data from USBL (Ultra-Short Base Line) system and heading data from gyro sensors. However, the obtained data values are likely to be different from the real values, primarily due to the random noise of position data. The aim of this study is to enhance the reliability of the algorithm by measuring kinematic parameters, track slips and slip angle.