• Journal of the Korean Geosynthetics Society
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• v.13 no.4
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• pp.11-20
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• 2014

The behaviors and stability of reinforced subgrade with short geogrid were examined and evaluated during construction. First of all, analytical approach for the minimum length of geogrid was performed to guarantee stability during construction loading state. Secondly, the economic aspects for reinforced subgrade were compared with between domestic standards applying with 0.7 H reinforcement length and new way to mix short and long reinforcement. Full scale railroad subgrade was constructed with the size of 5 m high, 6m wide, and 20m long to verify the stability of the subgrade with the length of 0.3 H, 0.35 H, 0.4 H reinforcement. Total 51 sensors were installed to measure settlement, bulging, and the change of stress of the subgrade. It is concluded that the reinforced subgrade with short(0.35H, 35% of height) geogrid had stability within allowable level of deformation and stress increment during construction.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.70-73
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• 2005

자기누설탐상시스템은 지하에 매설된 가스관에서 발생되는 부식이나 크랙 또는 기계적 변형을 탐지하기 위한 방법으로 비파괴검사 방법의 하나이다. 가스관은 Nd자석에 의해 착자가 되고, 가스관에 부식이 발생했을 경우 가스관의 단면적이 작아지게 되어 자기누설이 발생하며, 발생된 자기누설을 홀센서로 검출하여 부식의 유무, 크기, 모양 등을 판별하게 된다. 가스관에는 한 개의 독립적인 부식도 있지만, 다수의 부식이 밀집되어 나타나기도 한다. 다수의 부식이 밀집되었을 경우 부식간의 거리에 따라 하나의 부식으로 판정되기도 하며, 그에 따라 부식의 깊이를 판정하는데 있어 정확성이 저감된다. 따라서 본 논문에서는 다수의 부식이 밀집되어 발생할 경우 자기적 영향을 분석하고, 깊이 판정에 있어 정확성을 높이기 위한 연구를 수행하였다. 이를 위해서 실제 결함을 제작하여 실험하고, 해석하여 비교하였으며 밀집된 다수의 부식에 의한 자기적 영향에 대하여 고찰하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.481-489
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• 2000

A time-frequency analysis method was developed to analyze the dispersive waves caused by impact loads in structural members such as beams and plates. Stress waves generated by ball drop and pencil lead break were recorded by ultrasonic transducers and acoustic emission (AE) sensors. Wavelet transform (WT) using Gabor function was employed to analyze the dispersive waves in the time-frequency domain, and then to find the arrival time of the waves as a function of frequency. The measured group velocities in the beam and the plate were compared with the predictions based on the Timoshenko beam theory and Rayleigh-Lamb frequency equations, respectively. The agreements were found to be very good.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.705-711
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• 2013

In this paper, we present a low-complexity non-coherent demodulation algorithm and hardware architecture for LR-WPAN systems which can support the variable data rate for various applications. The need for LR-WPAN systems that can support the variable data rate is increasing due to the emergence of various sensor applications. Since the existing symbol based double correlation (SBDC) algorithm requires the increase of complexity to support the variable data rate, we propose the sample based double correlation (SPDC) algorithm which can be implemented without the increase of complexity. The proposed non-coherent demodulator was designed by verilog HDL and implemented with FPGA prototype board.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.3
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• pp.223-236
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• 1999

The need to monitor and undertake remidial works on large structures has greatly increased in recent years due to the appearance of widespread faults in large structures such as bridges and buildings, etc, of 20 or more years of age. The health condition of structures must be monitored continuously to maintenance the structures. In order to do in-situ monitoring, the sensor is necessary to be embedded in the structures. Fiber optic sensors can be embedded in the structures to get the health information in the structures. The fiber sensor was constructed with $3{\times}3$ fiber couplers to sense the multi-point strains and failure instants. The 4 RC (reinforced concrete) beams were made to 2 of A type, 2 of B type beams. These beams were reinforced by the reinforcing bars, and were tested under the flexural loading. The behavior of the beams was simultaneously measured by the fiber optic sensors, electrical strain gages, and LVDT. The states of the beams were interpreted by these all signals. By these experiments, There were verified that the fiber optic sensors could measure the structural strains and failure instants of the RC beams, The fiber sensors were well operated until the failure of the beams. It was shown that the strains of the reinforcing steel bar can be used to monitor the health condition of the beams through the flexural test of RC beams. On the other words, the results were arrived that the two strains in the reinforcing bar measured at the same point can give the information of the structural health status. Also, the failure instants of beams were well detected from the fiber optic filtered signals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1144-1151
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• 2021

The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.11-19
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• 2019

Against advanced image-related crimes, a high level of digital forensic methods is required. However, feature-based methods are difficult to respond to new device features by utilizing human-designed features, and deep learning-based methods should improve accuracy. This paper proposes a deep learning model to identify camera models based on DenseNet, the recent technology in the deep learning model field. To extract camera sensor features, a HPF feature extraction filter was applied. For camera model identification, we modified the number of hierarchical iterations and eliminated the Bottleneck layer and compression processing used to reduce computation. The proposed model was analyzed using the Dresden database and achieved an accuracy of 99.65% for 14 camera models. We achieved higher accuracy than previous studies and overcome their disadvantages with low accuracy for the same manufacturer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.981-988
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• 2022

The intelligent production plant called smart factories that apply information and communication technology (ICT) are collecting data in real time through various sensors. Recently, researches that effectively applying to these collected data have gained a lot of attention. This paper proposes a method for the tool condition monitoring based on the sound signal generated in machining process. First, it not only detects a fault tool, but also presents various tool states according to idle and active operation. The second, it's to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. Finally, the contrast-enhanced PSD image obtained is diagnosed by using CNN. The results of the experiments demonstrate the high discrimination potential afforded by the proposed sound PSD image + CNN and show high diagnostic results according to the tool status.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1890-1897
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• 2021

People counting is an important technology to provide application services such as smart home, smart building, smart car, etc. Due to the social distancing of COVID-19, the people counting technology attracted public attention. People counting system can be implemented in various ways such as camera, sensor, wireless, etc. according to service requirements. People counting system using WiFi AP uses WiFi CSI data that reflects multipath information. This technology is an effective solution implementing indoor with low cost. The conventional WiFi CSI-based people counting technologies have low accuracy that obstructs the high quality service. This paper proposes a deep learning people counting system based on WiFi CSI data. Data preprocessing using auto-encoder, data augmentation that transform WiFi CSI data, and a proposed deep learning model improve the accuracy of people counting. In the experimental result, the proposed approach shows 89.29% accuracy in 6 subjects.

• Composites Research
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• v.22 no.6
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• pp.1-6
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• 2009

This paper presents an experimental study on the piezoresistive behavior of nanocomposite strain sensors subjected to various loading modes and their capability to detect structural deformations and damages. The electrically conductive nanocomposites were fabricated in the form of a film using various types of thermoplastic polymers and multi-walled carbon nanotubes (MWNTs) at various loadings. In this study, the nanocomposite strain sensors were bonded to a substrate and subjected to tension, flexure, or compression. In tension and flexure, the resistivity change showed dependence on measurement direction, indicating that the sensors can be used for multi-directional strain sensing. In addition, the sensors exhibited a decreasing behavior in resistivity as the compressive load was applied, suggesting that they can be used for pressure sensing. This study demonstrates that the nanocomposite strain sensors can provide a pathway to affordable, effective, and versatile structural health monitoring.