• Journal of Sensor Science and Technology
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• v.27 no.6
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• pp.397-402
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• 2018

Proper seat design is critical to the safety, comfort, and ergonomics of automotive driver's seats. To ensure effective seat design, quantitative methods should be used to evaluate the characteristics of automotive seats. This paper presents a system that is capable of simultaneously monitoring body pressure distribution and surface deformation in a textile material. In this study, a textile-based capacitive sensor was used to detect the body pressure distribution in an automotive seat. In addition, a strain gauge sensor was used to detect the degree of curvature deformation due to high-pressure points. The textile-based capacitive sensor was fabricated from the conductive fabric and a polyurethane insulator with a high signal-to-noise ratio. The strain gauge sensor was attached on the guiding film to maximize the effect of its deformation due to bending. Ten pressure sensors were placed symmetrically in the hip area and six strain gauge sensors were distributed on both sides of the seat cushion. A readout circuit monitored the absolute and relative values from the sensors in realtime, and the results were displayed as a color map. Moreover, we verified the proposed system for quantifying the body pressure and fabric deformation by studying 18 participants who performed three predefined postures. The proposed system showed desirable results and is expected to improve seat safety and comfort when applied to the design of various seat types. Moreover, the proposed system will provide analytical criteria in the design and durability testing of automotive seats.

• Investigative Magnetic Resonance Imaging
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• v.22 no.4
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• pp.218-228
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• 2018

Purpose: The objective of this study is to determine the effect of physical changes on MR temperature imaging at 7.0T and to examine proton-resonance-frequency related changes of MR phase images and T1 related changes of MR magnitude images, which are obtained for MR thermometry at various magnetic field strengths. Materials and Methods: An MR-compatible capacitive-coupled radio-frequency hyperthermia system was implemented for heating a phantom and swine muscle tissue, which can be used for both 7.0T and 3.0T MRI. To determine the effect of flip angle correction on T1-based MR thermometry, proton resonance frequency, apparent T1, actual flip angle, and T1 images were obtained. For this purpose, three types of imaging sequences are used, namely, T1-weighted fast field echo with variable flip angle method, dual repetition time method, and variable flip angle method with radio-frequency field nonuniformity correction. Results: Signal-to-noise ratio of the proton resonance frequency shift-based temperature images obtained at 7.0T was five-fold higher than that at 3.0T. The T1 value increases with increasing temperature at both 3.0T and 7.0T. However, temperature measurement using apparent T1-based MR thermometry results in bias and error because B1 varies with temperature. After correcting for the effect of B1 changes, our experimental results confirmed that the calculated T1 increases with increasing temperature both at 3.0T and 7.0T. Conclusion: This study suggests that the temperature-induced flip angle variations need to be considered for accurate temperature measurements in T1-based MR thermometry.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.3
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• pp.183-190
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• 2019

Recent changes in the construction method of piles to minimize noise, along with the development of high-strength reinforcement, have provided an economical high performance RC pile development to compensate for the disadvantages of existing PHC piles. In this study, a methodology for the development of cross - section details of high performance RC piles of various performances is presented by freely applying high strength steel and concrete. This study suggested a technique for calculating bending moments for a given axial force corresponding to the allowable crack widths and this can be used for serviceablity check. In calculating the design shear force, the existing design equation applicable to the rectangular or the I section was modified to be applicable to the hollow circular section. In particular, in the limit state design method, the shear force is calculated in proportion to the axial force, and the procedure for calculating PV diagram is established. Last, the section details are determined through PM diagrams that they have the similar flexural and axial-flexural performances of the PHC pile A, B and C types with a diameter of 500 mm. To facilitate the application of the selected standard sections to the practical tasks, the design PM diagram and design shear forces are proposed in accordance with the strength design method and limit state design method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1275-1281
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• 2019

The automatic modulation recognition of a radio signal is a major task of an intelligent receiver, with various civilian and military applications. In this paper, we propose a method to recognize the modulation of radio signals in wireless communication based on the deep neural network. We classify the modulation pattern of radio signal by using the LSTM model, which can catch the long-term pattern for the sequential data as the input data of the deep neural network. The amplitude and phase of the modulated signal, the in-phase carrier, and the quadrature-phase carrier are used as input data in the LSTM model. In order to verify the performance of the proposed learning method, we use a large dataset for training and test, including the ten types of modulation signal under various signal-to-noise ratios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1234-1239
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• 2019

This paper proposes a method for measuring fine dust concentration through digital processing of images captured by only existing CCTVs without additional equipment. This image processing algorithm consists of noise reduction, edge sharpening, ROI setting, edge strength calculation, and correction through HSV conversion. This algorithm is implemented using the C ++ OpenCV library. The algorithm was applied to CCTV images captured over a month. The edge strength values calculated for the ROI region are found to be closely related to the fine dust concentration data. To infer the correlation between the two types fo data, a trend line in the form of a power equation is established using MATLAB. The number of data points deviating from the trend line accounts for around 12.5%. Therefore, the overall accuracy is about 87.5%.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.53-60
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• 2021

High voltage CV cables have been widely installed underground due to their convenience and urban aesthetics. However, cable accidents have occurred frequently owing to poor construction and natural degradations. This paper proposes the method to measure the multi parameter measurement for optimum diagnostics of high voltage cable connection parts and verifies its technical usefulness. This measurement is intended to diagnose degradations of cable connection parts by using simultaneous vibration and thermography as well as partial discharge(PD). The experiment in a shielded laboratory was carried out to verify the usefulness of the multi parameter measurement. The experiment defined the degradation of the cable connection part as 12 types, and produced each degradation sample. As a result of experiment, it was possible to check the correlation of vibration signals with regard to progress in some defects. In the case of thermography, the coherence with regard to the progress of some defects was found. We figure that the proposed method would be useful also in the noise environment.

• Journal of Platform Technology
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• v.9 no.4
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• pp.79-89
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• 2021

In this paper, the fine dust (PM10) and ultrafine dust (PM2.5) measurement platforms are designed to be mobile and fixed using oneM2M, the international standard for IoT. The fine dust measurement platform is composed and designed with a fine dust measurement device, agent, oneM2M platform, oneM2M IPE, and monitoring system. The main difference between mobile and fixed is that the mobile uses the MQTT protocol for interconnection between devices and services without blind spots based on LTE connection, and the fixed uses the LoRaWAN protocol with low power and wide communication range. Not only fine dust, but also temperature, humidity, atmospheric pressure, volatile organic compounds (VOC), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and noise data related to daily life were collected. The collected sensor values were managed using the common API provided by oneM2M through the agent and oneM2M IPE, and it was designed into four resource types: AE and container. Six functions of operability, flexibility, convenience, safety, reusability, and scalability were analyzed through the fine dust measurement platform design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1222-1230
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• 2022

As the number of signal sources with low detectability by using various modulation techniques increases, research to classify signal modulation methods is steadily progressing. Recently, a Convolutional Neural Network (CNN) deep learning technique using FFT as a preprocessing process has been proposed to improve the performance of received signal classification in signal interference or noise environments. However, due to the characteristics of the FFT in which the window is fixed, it is not possible to accurately classify the change over time of the detection signal. Therefore, in this paper, we propose a CNN model that has high resolution in the time domain and frequency domain and uses wavelet transform as a preprocessing process that can express various types of signals simultaneously in time and frequency domains. It has been demonstrated that the proposed wavelet transform method through simulation shows superior performance regardless of the SNR change in terms of accuracy and learning speed compared to the FFT transform method, and shows a greater difference, especially when the SNR is low.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.338-343
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• 2022

The Air Radio Station plays a role in creating skyways for aircraft and helping them fly safely through aviation mobile communication facilities (U/VHF transceivers) and various navigation safety facilities. It is necessary to remove large trees and weeds around the aircraft because accurate and safe signals must be provided without interruption. During the mowing work for the efficient management of landscaping facilities, there have been cases in which noise is introduced by Lawn Mower, which hinders control work. Accordingly, in order to analyze how mower affects air mobile communication facilities, the interference effect on air mobile communication facilities was analyzed for four types of mower, two-stroke, and battery type. As a result of the analysis, it was found that the two-stroke mower greatly affects air mobile communication facilities.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.