• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.1-7
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• 2021

Since recently only an auto-correlated superposition coding (SC) scheme for non-orthogonal multiple access(NOMA) has been investigated, this paper proposes a cross-correlated SC scheme for NOMA. First, we derive the closed-form expression of the sum rate of the proposed cross-correlated SC scheme. Then, numerical analyses demonstrate that the sum rate of the proposed cross-correlated SC scheme is larger than that of the conventional auto-correlated SC scheme. We also show that for the stronger channel gain user, the signal-to-noise ratio (SNR) gain of the proposed cross-correlated SC scheme is about 15, compared with the conventional auto-correlated SC scheme. As a result, the proposed cross-correlated SC scheme could be a promising technology for 6G ultra-reliable low-latency communications (URLLC).

• 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 The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.185-198
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• 2022

Purpose : We conducted a study to propose comparing subjective sleeping states data that was collected through scientifically measuring by using smartwatch and surveys for seafarers. In addition, we conducted a study to provide sleep though analysis of the environmental factors that influence the sleep efficiency of seafarers. Methods : For scientific measurement of seafarers' sleeping states, we measured the sleep hours and sleep efficiency for at least 3 days by using the healthcare function of smartwatches which are wearable devices. As for subjective sleeping states, we collected data on sleep hours and satisfaction rates on the quality of sleep through a survey. Lastly, as for the environmental factors that affect the sleep efficiency which is measured by smartwatches such as physical environment, bedroom space, bedroom furniture, bedding, a survey by self-evaluation method was conducted. Results : There were significant differences in the scientific sleep hours measurement values for all seafarers and the average sleep hours in the subjective survey. There were significant differences in the scientific sleep efficiency measurements of all the seafarers and the sleep satisfaction of the subjective survey, and there was no correlation between the scientific sleep efficiency and the subjective sleep satisfaction of all the seafarers. Among the environmental factors affecting the sleep efficiency of the seafarers, humidity variable had the most influence, and vibration and illuminance variables were also shown to have a very significant effect. Conclusion : We propose the measuring method that can analyze the quantitative and qualitative sleep states of the seafarers by using small activity recorders. In addition, it is very important to control humidity, vibration, and illuminance among ship environmental factors, and comfort, noise and bedroom size should also be reflected.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.11-18
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• 2023

This study explores a new energy partitioning approach to determine the fracture toughness of 3-D printed pristine/recycled high density polyethylene (HDPE) blends employing the essential work of fracture (EWF) concept. The traditional EWF approach conducts a uniaxial tensile test with double-edge notched tensile (DENT) specimens and measures the total energy defined by the area under a load-displacement curve until failure. The approach assumes that the entire total energy contributes to the fracture process only. This assumption is generally true for extruded polymers that fracture occurs in a material body. In contrast to the traditional extrusion manufacturing process, the current 3-D printing technique employs fused deposition modeling (FDM) that produces layer-by-layer structured specimens. This type of specimen tends to include separation energy even after the complete failure of specimens when the fracture test is conducted. The separation is not relevant to the fracture process, and the raw experimental data are likely to possess random variation or noise during fracture testing. Therefore, the current EWF approach may not be suitable for the fracture characterization of 3-D printed specimens. This paper proposed a new energy partitioning approach to exclude the irrelevant energy of the specimens caused by their intrinsic structural issues. The approach determined the energy partitioning location based on experimental data and observations. Results prove that the new approach provided more consistent results with a higher coefficient of correlation.

• Journal of Korean Society on Water Environment
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• v.39 no.4
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• pp.316-328
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• 2023

In this study, the characteristics of nonpoint pollutant outflow and contribution rate of pollution in Songya-stream mainstream and tributaries were analyzed. Further, water pollution management and improvement measures for pollution-oriented rivers were proposed. An on-site investigation was conducted to determine the inflow of major pollutants into the basin, and it was found that pollutants generated from agricultural land and livestock facilities flowed into the river, resulting in a high concentration of turbid water. Based on the analysis results of the pollution load data calculated through actual measurement monitoring (flow and water quality) and the occurrence and emission load data calculated using the national pollution source survey data, the S3 and S6 were selected as the concerned pollution tributaries in the Songya-stream basin. Results of cluster analysis using Pearson correlation coefficient evaluation and Density based spatial clustering of applications with noise (DBSCAN) technique showed that the S3 and S6 were most consistent with the C2 cluster (a cluster of Songya-stream mainstream owned area) corresponding to the mainstream of Songya-stream. The analysis results of the major pollutants in the concerned pollution tributaries showed that livestock and land pollutants were the major pollutants. Consequently, optimal management techniques such as fertilizer management, water gate management in paddy, vegetated filter strip and livestock manure public treatment were proposed to reduce livestock and land pollutants.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.175-179
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• 2022

The physical properties of biomaterials are important for their isolation and separation from body fluids. In particular, the precise evaluation of the multi-physical properties of single biomolecules is essential in that the correlation between physical and biological properties of specific biomolecule. However, the majority of scientific equipment, can only determine specific-physical properties of single nanoparticles, making the evaluation of the multi-physical properties difficult. The improvement of analytical techniques for the evaluation of multi-physical properties is therefore required in various research fields. In this study, we developed a motion-tracking algorithm to evaluate the multi-physical properties of single-nanoparticles by analyzing their behavior. We observed the Brownian motion and electric-field-induced drift of fluorescent nanoparticles injected in a microfluidic chip with two electrodes using confocal microscopy. The proposed algorithm is able to determine the size of the nanoparticles by i) removing the background noise from images, ii) tracking the motion of nanoparticles using the circular-Hough transform, iii) extracting the mean squared displacement (MSD) of the tracked nanoparticles, and iv) applying the MSD to the Stokes-Einstein equation. We compared the evaluated size of the nanoparticles with the size measured by SEM. We also determined the zeta-potential and surface-charge density of the nanoparticles using the extracted electrophoretic velocity and the Helmholtz-Smoluchowski equation. The proposed motion-tracking algorithm could be employed in various fields related to biomaterial analysis, such as exosome analysis.

• Journal of Sasang Constitutional Medicine
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• v.13 no.3
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• pp.31-39
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• 2001

Bandwidth and Pitch Range are very important in the area of distinguish of phone which is one of many areas of phonetics and distinguish the individual way of phone. So if each constitution has a trait in its phone, they are important to judge the constitutions. In this report we try to understand the relativity between constitutions and Formant Bandwidth, Pitch Range and the number of syllables in a minute which are important to distinguish the phone. And we try to make judging the constitutions objective. 1. We analyzed Formant Bandwidth and there are some differences between constitutions but it doesn't have any importance in the statistics. 2. We analyzed Pitch Range and there are some differences between constitutions but it doesn't have any importance in the statistics. 3. We analyzed the number of syllables in a minute and there are some differences between constitutions but it doesn't have any importance in the statistics. As mentioned above there are differences between constitutions in Formant Bandwidth, Pitch Range and the numbers of syllables in a minute, but they don't have any importance in the statistics. However if we increase the number of samples and remove noise, there will be great possibility to find some important meanings.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3
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• pp.123-128
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• 2007

This Paper Presents new transient signal classification algorithms for underwater transient signals. In general. the ambient noise has small spectral deviation and energy variation. while a transient signal has large fluctuation. Hence to detect the transient signal, we use the spectral deviation and power variation. To classify the detected transient signal. the feature Parameters are obtained by using the Wigner-Ville distribution based eigenvalue decomposition. The correlation is then calculated between the feature vector of the detected signal and all the feature vectors of the reference templates frame-by-frame basis, and the detected transient signal is classified by the frame mapping rate among the class database.