• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.447-454
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• 2011

Power-line interference(PLI) can distort certain regions in analysing the ECG signal. In particular, the regions such as P and R wave that are important element in diagnosing with arrhythmia is expressed as different type of noise according to the case whether power-line frequency is multiples of sampling frequency and or not. Noise characteristics is also divided into linearity and non-linearity. In this paper, adaptive subtraction method for removing variable PLI of ECG signal is proposed. We classify the multiple relationship between power line and sampling frequency as Multiple and Non-multiple. PLI of Linear segment is extracted through moving average filter, PLI of non-linear segment is extracted through the interference component that is extracted in the linear segment and stored in the temporary buffer. The performance of P wave and R wave detection is evaluated by using 119 data record of MIT-BIH arrhythmia database. The achieved scores indicate P wave detection rate of 97.91%, R wave detection rate of 96.66% and P wave detection rate of 99.01%, R wave detection rate of 97.93% accuracy respectively for Notch filter and proposed subtraction method.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.174-180
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• 2020

With the removal of underwater obstacles in the Busan New Port, the water depth of the pier has been secured up to 17m, and the port authority is constantly responding to the trend of container vessels becoming larger. Also, in 2020, 24,000TEU class container ships are entering to the port, and it is planned to secure the depth in the port to 23 m later in line with this trend. Mega container ships must check in advance the factors to be considered depending on the situation at the time, and for this, it is judged that information sharing among stakeholders is necessary. In this paper, to understand the effect of the corresponding route because of the mega container ships, a transit safety evaluation was conducted based on statistical data on ship entry and departure and maneuver characteristics of corresponding ships. The result showed that the transit of the mega container ships has increased up to 8.4% comparing to the risk of 4,000TEU class container ships. Additionally, safety measures such as minimum safety depth and tug operation plans were presented by gathering opinions on operational characteristics from the perspective of pilotage for safe transit in the Busan New Port area. Through this, it is considered that it will be possible to contribute to the prevention of accidents when entering and leaving the Busan New Port.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.9
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• pp.1027-1032
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• 2012

Two types of very small multiplying gears and arrays have been developed for new dental hand-pieces, and the increased speed ratios, modules, number of teeth, gear diameters, and gear types were calculated based on the dynamics of the machinery. The contacting and bending strengths were evaluated for gear teeth with two design concepts using AGMA equations and finite element analyses, and the contacting stresses on teeth with and without DLC (diamond-like-carbon) coating layers were calculated. Fatigue and tension tests were performed to obtain an S-N curve, the Young's modulus, and the strength of the gear material, and these were utilized in the analyses. Slightly larger stresses were found for 2-axis-type gears than for other types of gears, and the S-N curves showed that a gear lifetime of 109 cycles was satisfied. The contacting stresses in gears coated with DLC were reduced by 30%. A new prototype model of a hand-piece with small gears was successfully fabricated and tested.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.613-619
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• 2017

Recently, the size of container ships has been progressively increasing, and much attention is required for safe navigation in shallow areas such as coastal waters and ports due to increases in draft. It is necessary to understand the characteristics of ship motion not only in still waters but also with waves. Especially in shallow regions, squat due to the vertical movement of the ship can be an important evaluation factor for the safe navigation, and wave drift force acting in the horizontal direction can have a great influence on the maneuverability of a ship. In this study, a numerical simulation using computational fluid dynamics has been performed for the wave exciting force acting in the vertical direction and the wave drift force acting in the horizontal direction for a very large container vessel sailing in shallow zone. As a result, it was found that total resistance in still waters greatly increased in shallow water. Wave drift force was shown to decrease given longer wavelengths regardless of water depth. It was observed that the wave exciting force in shallow water was considerably larger than at other water depths. As wave height against the central part of the ship lowered, the aft side rose.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.2
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• pp.84-91
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• 2019

Recently, there has been a tendency to improve seismic performance of building structure by installing a steel hysteretic damping device which is economically efficient and easy to install and maintain. However, for a reinforced concrete building, a set of complicated connecting hardware and braces to fix the steel hysteretic damping device yields deteriorated reliability in damping performance. Therefore, this study presents a method of directly embedding a Kagome damper, which was investigated in previous researches, into a concrete structure without additional connecting hardware. Moreover, in this study, a series of experiments conducted to provide a basis of the Kagome damper by confirming the seismic behavior for various embedded lengths. As a result, in a group of the embedded length of $1.0l_d$, the dampers were pulled out, while concrete breakout occurs. In a group of $2.0l_d$, neither pull-out nor concrete breakout occurred, while the dampers show stable behavior. Moreover, the buried length of $2.0l_d$ has 1.3 times better energy dissipation capacity. The system presented in this study can reduce the cost and period for installing, omitting making additional hardware.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.213-222
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• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.35-44
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• 2021

As the execution speed of Python is slower than those of other programming languages (e.g., C, C++, and FORTRAN), Python is not considered to be efficient for writing numerical geodynamic code that requires numerous iterations. Recently, many computational techniques, such as the Just-In-Time (JIT) compiler, have been developed to enhance the calculation speed of Python. Here, we developed two-dimensional (2D) numerical geodynamic code that was optimized for the JIT compiler, based on Python. Our code simulates mantle convection by combining the Particle-In-Cell (PIC) scheme and the finite element method (FEM), which are both commonly used in geodynamic modeling. We benchmarked well-known mantle convection problems to evaluate the reliability of our code, which confirmed that the root mean square velocity and Nusselt number obtained from our numerical modeling were consistent with those of the mantle convection problems. The matrix assembly and PIC processes in our code, when run with the JIT compiler, successfully achieved a speed-up 30× and 258× faster than without the JIT compiler, respectively. Our Python-based FEM-PIC code shows the high potential of Python for geodynamic modeling cases that require complex computations.