• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.38-44
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• 2019

This paper proposes fingerprint-based positioning methods which can be used in a magnetic resonant wireless power transfer(WPT) system and verifies their performance. A new receiver coil with small orthogonal auxiliary coils is proposed to measure magnetic field signals in three axial directions. The magnitude and phase characteristics of the three-axis electromotive force can be obtained by using the proposed coil. To predict a position with the measured values, we propose a lookup table-based method and linear discriminant analysis-based method. For verification, the proposed methods are applied to predict 75 positions of the 6.78 MHz WPT system, and the performances such as accuracy and computation time are compared.

• Journal of IKEEE
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• v.22 no.4
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• pp.901-906
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• 2018

Due to the development of display technologies for images, the resolution and quality of images are increasing day by day. In accordance with the development of the display technology, researches have been actively conducted on technologies for converting and displaying existing images to higher resolution and quality. Since the results of theses studies are included in the image signal processor, hardware implementation is indispensable. In this paper, we propose a real-time HDR(High Dynamic Range) conversion hardware implementation of LDR(Low Dynamic Range) image using luminance distribution. The proposed method extracts the features of the image using the histogram of the luminance distribution, and extends the luminance and color based on the extracted features. In addition, when the proposed method is designed by hardware IP(Intellectual Property) and its performance is verified, 4K DCI(Digital Cinema Image) can be handled at a rate of 30fps at 265.46MHz.

• Korean Journal of Pharmacognosy
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• v.49 no.4
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• pp.291-297
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• 2018

Coxsackievirus B3 (CVB3) is a very well-known causative agent for viral myocarditis and meningitis in human. However, the effective vaccine and therapeutic drug are not developed yet. CVB3 infection activates host cell AKT signaling. Inhibition of AKT signaling pathway may attenuate CVB3 replication and prevent CVB3-mediate viral myocarditis. In this study, we determined antiviral effect of the selected natural plant extract to develop a therapeutic drug for CVB3 treatment. We screened several chemically extracted natural compounds by using HeLa cell-based cell survival assay. Among them, Linum usitatissimum L. extract was selected for antiviral drug candidate. L. usitatissimum extract significantly decreased CVB3 replication and cell death in CVB3 infected HeLa cells with no cytotoxicity. CVB3 protease 2A induced eIF4G1 cleavage and viral capsid protein VP1 production were dramatically decreased by L. usitatissimum extract treatment. In addition, virus positive and negative strand genome amplification were significantly decreased by 1 mg/ml L. usitatissimum extract treatment. Especially, L. usitatissimum extract was associated with inhibition of AKT signal and maintain mTOR activity. In contrast, Atg12 and LC3 expression were not changed by L. usitatissimum extract treatment. In this study, the potential AKT signal inhibitor, L. usitatissimum extract, was significantly inhibited viral genome replication and protein production by inhibition of AKT signal. These results suggested that L. usitatissimum extract is a novel therapeutic agent for treatment of CVB3-mediated diseases.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.101-108
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• 2019

The aim of this study was to verify differences in autonomic responses that are affected by the configurations of one-room type units using an electrocardiogram (ECG). Accordingly, 43 one-room units that were collected randomly were classified into ten different types of floor plan configurations mainly according to the location of the bathroom and kitchen. An ECG was subsequently measured for each plan type and the average ratio of the LF/HF (Power in low frequency range/Power in high frequency range) was calculated to measure the comfort level of each space. The results revealed a significant statistical difference between the average LF/HF ratio between the plan types (p < 0.05) and provided compelling evidence suggesting that the configuration of the plan may affect the quality of one-room space. This approach appears to be effective in counteracting stress that may exacerbate psychological disorders in single person households.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.39-46
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• 2019

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.612-623
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• 2022

Estimation of the presence of people in real time is extremely useful for businesses in providing better services. Many companies and researchers have attempted various researches in order to count the number of floating population in a specific space. Recently, as part of smart cities and digital twins, commercialization of measuring floating populations using Wi-Fi signals has become active in the public and private sectors. In this paper we present a method of estimating the floating population based on MAC-address values collected from smartphones. By distinguishing Real MAC-address and Random MAC-address values, we compare the estimated number of smartphone devices and the actual number of people caught on CCTV screens to evaluate the accuracy of the proposed method. And it appeared to have a similar correlation between the two datas. As a result, we present a method of estimating the floating population based on analyzing Wi-Fi Probe Requests.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.69-75
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• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.312-318
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• 2022

This paper proposes a model which classifies the type of guns and information about sound source location using deep neural network. The proposed classification model is composed of convolutional neural networks (CNN) and long short-term memory (LSTM). For training and test the model, we use the Gunshot Audio Forensic Dataset generated by the project supported by the National Institute of Justice (NIJ). The acoustic signals are transformed to Mel-Spectrogram and they are provided as learning and test data for the proposed model. The model is compared with the control model consisting of convolutional neural networks only. The proposed model shows high accuracy more than 90 %.