• Journal of Power System Engineering
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• v.13 no.2
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• pp.63-70
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• 2009

This paper discusses the influence on long-tenn predictions of the ship response in ocean by using the Global Wave Statistics data, GWS, and wave information from the remote sensing satellites. GWS's standard scatter diagrams of significant wave height and zero-crossing wave period are suggested to be corrected to a round number of 0.01/1000 fitted with a statistical analytic model of the conditional lognormal distribution for zero-crossing wave period. The GEOSAT satellite data are utilized which presented by I. R. Young and G. J. Holland (1996, named as GEOSAT data). At first, qualities of this data are investigated, and statistical characteristic trends are studied by means of applying known probability distribution functions. The wave height data of GEOSAT are compared to the data observed onboard merchant ships, the data observed by measure instrument installed on the ocean-going container ship and so on. To execute a long-tenn prediction of ship response, joint probability functions between wave height and wave period are introduced, therefore long-term statistical predictions are executed by using the functions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.1
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• pp.1-7
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• 2016

In this paper, the tunable filter based on high power cavity using mechanical switch for 1.5 GHz band is presented. The LPF is inserted to eliminate the spurious wave, coupler is embeded to extract the output power, and then the tunable filter system is configured using mechanical switch. The LPF obtains attenuation over 40 dB between 4 GHz and 12.75 GHz, Coupler is satisfied with coupling value 40 dB and coupling isolation over 55 dB. The tunable filter system using mechanical switch obtains insertion loss 0.88 dB at bypass mode between 1,495.9 MHz and 1,510. 9 MHz, 3.29 dB at fil mode between 1,495.9 MHz and 1,500.9 MHz. It is also satisfied with output power of 132 W at the center frequency 1,498.4 MHz, and switching time below 10 ms.

• Special Issue of the Society of Naval Architects of Korea
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• 2005.06a
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• pp.31-37
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• 2005

An attempt to improve the speed performance through the minimizing in wave resistance has been done by an application of gooseneck and no bulb type to bulbous bow for the DSME 78,500 Class LPG Carrier on the basis of the CFD calculation and comparatives model tests. The hydrodynamic characteristics according to the variation of the shape of Cp-curve, design load water line, frame line and bulbous bow that have an important effect on the wave resistance has been evaluated/calculated by ship-flow code. A wide variety in hull variation have been tried to have a good hull form with three types of fore-body hull forms mainly classified by the shape of bulbous bow. The speed performances for the three final hull forms with different bulbous bows have been evaluated through the model tests.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.479-482
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• 2001

This paper proposes a modified $\alpha$ conduction mode for controlling the output voltage magnitude of three-phase square-wave VSIs with an R-L load. From the viewpoint of both power capacity and switching losses, three-phase square-wave inverters are now used in most high power systems. When the square-wave VSI is driven with $\alpha$ conduction mode to control the magnitude of output voltages, interval over than half period is operated with $180^{\circ}$ conduction mode and the other interval with $120^{\circ}$ conduction mode. In $120^{\circ}$ conduction mode operation, two output terminals are connected to DC supply and the third one remains open. The potential of this open terminal will depend on the load characteristics and is unpredictable except the case of pure resistive loads. To cope this problem, we propose the modified α conduction mode.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.1
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• pp.19-28
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• 2018

Numerous research papers have been accumulated due to the development and computerization of bibliometrics. This made it difficult to review all of the related papers published worldwide to conduct the study. However, due to the development of Natural language processing techniques, the tendency analysis of published research papers has become easier. In this study, text mining analysis using the statistical computing language R was carried out based on the bibliographic information of SCOPUS DB (Data Base) in the field of coastal and ocean engineering. As expected, the term 'wave' predominates, and it was confirmed that numerical analysis and hydraulic experiments were still dominant from the terms 'numerical model', 'numerical simulation', and 'experimental study'. In addition, recent use of the term 'wave energy' related to marine energy has been recognized. On the other hand, it was quantitatively confirmed that the frequency of connection between 'wave', and 'height' or 'energy' prevailed, and suggested the possibility of high resolution analysis by detailed field and period in the future.

• Geomechanics and Engineering
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• v.23 no.2
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• pp.115-125
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• 2020

This study aims to explore practical and useful equations for rapid evaluation of uniaxial compressive strength of concrete (UCS-C) during the preliminary design stage of aggregate selection. For this purpose, aggregates which were produced from eight different intact rocks were used in the production of concretes. Laboratory experiments involved the tests for uniaxial compressive strength (UCS-R), point load index (PLI-R), P wave velocity (UPV-R), apparent porosity (n-R), unit weight (UW-R) and aggregate impact value (AIV-R) of the rock samples. UCS-C, point load index (PLI-C) and P wave velocity (UPV-C) of concrete samples were also determined. Relationships between UCS-R-rock parameters and UCS-C-concrete parameters were developed by regression analyses. In the simple regression analyses, PLI-C, UPV-C, UCS-R, PLI-R, and UPV-R were found to be statistically significant independent variables to estimate the UCS-C. However, higher coefficients of determination (R²=0.97-1.0) were obtained by multiple regression analyses. The results of simple regression analysis were also compared to the limited number of previous studies. The strength conversion factor (k) values were found to be 14.3 and 14.7 for concrete and rock samples, respectively. It is concluded that the UCS-C can roughly be estimated from derived equations only for the specified rock types.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2391-2398
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Most of the algorithms detecting PVC reported in literature is not always feasible due to the presence of noise and P wave making the detection difficult, and the process being time consuming and ineffective for real time analysis. To solve this problem, a new approach for the detection of PVC is presented based rhythm analysis and beat matching in this paper. For this purpose, the ECG signals are first processed by the usual preprocessing method and R wave was detected. The algorithm that decides beat type using the rhythm analysis of RR interval and beat matching of QRS width is developed. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate sensitivity of 99.74%, positive predictivity of 99.81% and sensitivity of 93.91%, positive predictivity of 96.48% accuracy respectively for R wave and PVC detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.244-253
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• 2016

The QRS complex of ECG signal represents the depolarization and repolarization activities in the cells of ventricle. Accurate informations of $QRS_{onset}$ and $QRS_{offset}$ are needed for automatic analysis of ECG waves. In this study, using the amount of change in the QRS complex voltage values and the distance from the $R_{peak}$, we determined the junction point from Q-wave to R-wave and the junction point from R-wave to S-wave. In the next step, using the integral calculation based on the connection point, we detected $QRS_{onset}$ and $QRS_{offset}$. We use the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and standard deviation of the differences between onsets or offsets manually marked by cardiologists and those detected by the proposed algorithm. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.