• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.1-6
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• 2003

Since the scale in pipes reduces the flow rate, a quantitative evaluation of the scale is essential for the proper maintenance of pipes. Guided waves were employed to estimate the amount of scale in water supplying pipes. Using variable angle wedge, several modes of guided waves wire generated and their propagation charcteristics along the pipes with stale were analyzed. It was experimentally observed that the amplitude of F(M,2) modes at $f{\times}d=1.5MHz\;mm$ decreased significantly with increasing amount of scale. The present study showed that F(M,2) modes were optima) to evaluate the scale in water supplying pipes.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.357-360
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• 2019

We propose an efficient single convolutional neural network (CNN) for pedestrian detection on embedded GPUs. We first determine the optimal number of the convolutional layers and hyper-parameters for a lightweight CNN. Then, we employ a multi-scale approach to make the network robust to the sizes of the pedestrians in images. Experimental results demonstrate that the proposed algorithm is capable of real-time operation, while providing higher detection performance than conventional algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1351-1367
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• 2018

Image registration is an essential process for image fusion, change detection and time series analysis using multi-sensor images. For this purpose, we need to detect accurately the difference of scale and rotation between the multi-sensor images with difference spatial resolution. In this paper, we propose a new feature matching method using variable circular template for image registration between multi-resolution images. The proposed method creates a circular template at the center of a feature point in a coarse scale image and also a variable circular template in a fine scale image, respectively. After changing the scale of the variable circular template, we rotate the variable circular template by each predefined angle and compute the mutual information between the two circular templates and then find the scale, the angle of rotation and the center location of the variable circular template, respectively, in fine scale image when the mutual information between the two circular templates is maximum. The proposed method was tested using Kompsat-2, Kompsat-3 and Kompsat-3A images with different spatial resolution. The experimental results showed that the error of scale factor, the error of rotation angle and the localization error of the control point were less than 0.004, $0.3^{\circ}$ and one pixel, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.131-137
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• 2016

In this paper, a feasibility on a ground-penetrating radar for detecting subsurface cavities near buried pipes has been investigated. The experimental setup was implemented by employing an impulse ground-penetrating radar system, a xy Cartesian coordinate robot, an underground material filled tank, a metal pipe and a simulated cavity model. In particular, the simulated cavity model was constructed by packing Styrofoam chips and balls, which have both similar electrical properties to an air-filled cavity and a solid shape. Through typical three experiments, B-scan data of the radar have been acquired and displayed as 2-D gray-scale images. According to the comparison of B-scan images, we show that the subsurface cavities near the buried pipes can be detected by using the radar survey.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.183-194
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• 2009

In this paper, we propose a new method for detecting objectionable images with an active shape model. Our method first learns the shape of breast lines through principle component analysis and alignment as well as the distribution of intensity values of corresponding landmarks, and then extracts breast lines with the learned shape and intensity distribution. To accurately select the initial position of active shape model, we obtain parameters on scale, rotation, and translation. After positioning the initial location of active shape model using scale and rotation information, iterative searches are performed. We can identify adult images by calculating the average of the distance between each landmark and a candidate breast line. The experiment results show that the proposed method can detect adult images effectively by comparing various results.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.103-114
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• 2019

Moving-target detection using frequency-modulated continuous-wave (FMCW) radar systems has recently attracted attention. Detection tasks are more challenging with noise resulting from signals reflected from strong static objects or small moving objects(clutter) within radar range. Robust Principal Component Analysis (RPCA) approach for FMCW radar to detect moving objects in noisy environments is employed in this paper. In detail, compensation and calibration are first applied to raw input signals. Then, RPCA via Gradient Descents (RPCA-GD) is adopted to model the low-rank noisy background. A novel update algorithm for RPCA is proposed to reduce the computation cost. Finally, moving-targets are localized using an Automatic Multiscale-based Peak Detection (AMPD) method. All processing steps are based on a sliding window approach. The proposed scheme shows impressive results in both processing time and accuracy in comparison to other RPCA-based approaches on various experimental scenarios.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.400-400
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• 2022

관로 내 빈번히 발생하는 수격압의 발생은 관망 구조물에 피로가 누적되고 관벽에 손상을 발생시켜, 관로 내 누수가 다양한 형태로 생성된다. 관 내 누수가 발생되는 경우 관 내부의 수격압의 발생 시 생성되는 부압으로 인하여 외부 물질이 관으로 흡수되거나 혼합되어 스케일과 미생물의 생성되는 등 관 내의 수질에 악영향을 끼치며 마찰을 증가시켜 통수능이 감소하고 관리에 추가적인 비용을 발생시킨다. 이러한 영향을 방지하기 위해 관 내에서 생성되는 누수를 탐지하기 위하여 수격압을 발생시켜 압력파를 분석하거나 추적을 수행하는 여러 가지 연구들이 수행되었다. 본 연구에서는 현장 관망과 연결된 100A 대구경 관로에 관로 수압 발생장치를 연결하여 기존의 수격압을 발생시켜 분석하는 방법 대신 안전하고 용이한 방법인 압력파를 주입하여 실험을 수행하였다. 실험을 통해 획득한 데이터를 시간상에서 분석하고 Fourier 변환을 통한 빈도상 분석과 Wavelet 분석으로 신호주기에서 누수가 미치는 영향을 파악하였다. 실험 결과에서는 누수에 의한 영향으로 반사파가 직접적으로 변형되는 형태보다 시스템 전체에서 반영되어 수두가 감쇠되는 형태로 나타났다. Fourier 변환을 통해 무누수 조건과 누수조건의 비교에서 누수의 유무에 따른 신호의 형태가 차이를 보였다. 앞선 연구들에서의 누수의 특정한 위치를 찾아내는 형태 대신 신호처리 후 분석을 통해 시스템 전체에서 일어나는 감쇠를 통해 누수 존재 유무를 판별하고자 한다.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• KNOM Review
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• v.21 no.2
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• pp.18-25
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• 2018

Since the development of block-chain technology by Satoshi Nakamoto and Bitcoin pioneered a new cryptocurrency market, a number of scale of cryptocurrency have emerged. There are crimes taking place using the anonymity and vulnerabilities of block-chain technology, and many studies are underway to improve vulnerability and prevent crime. However, they are not enough to detect users who commit crimes. Therefore, it is very important to detect abnormal behavior such as money laundering and stealing cryptocurrency from the network. In this paper, the characteristics of the transactions and user graphs in the Bitcoin network are collected and statistical information is extracted from them and presented as plots on the log scale. Finally, we analyze visualized plots according to the Densification Power Law and Power Law Degree, as a result, present features appropriate for detection of anomalies involving abnormal transactions and abnormal users in the Bitcoin network.