• Proceedings of KOSOMES biannual meeting
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• 2004.11a
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• pp.129-135
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• 2004

Plate has cutout inner bottom and girder and floor etc in hull construction absence is used much, and this is strength in case must be situated, but establish in region that high stress interacts sometimes fatally in region that there is no big problem usually by purpose of weight reduction, a person and change of freight, piping etc. Because cutout's existence gnaws in this place, and, elastic budding strength by load rouses large effect in ultimate strength. Therefore, perforated plate elastic budding strength and ultimate strength is one of important design criteria which must examine when decide structural elements size at early structure design step if ship. Therefore, and, reasonable elastic budding strength about perforated plate need design ultimate strength. Calculated ultimate strength change several aspect ratioes and cutout's dimension, and thickness in this investigation. Used program applied ANSYS F.E.M code based on finite element method

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2016.11a
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• pp.152-152
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• 2016

Anodizing is a technology to generate thicker and high-quality films than natural oxide films by treating metals via electrochemical methods. It is a technique to develop metals for various uses, and extensive research on the commercial use has been performed for a long time. Aluminum anodic oxide (AAO) is generate oxide films, whose sizes and characteristics depending on the types of electrolytes, voltages, temperatures and time. Electrochemical manufacturing method of nano structure is an efficient technology in terms of cost reduction, high productivity and complicated shapes, which receives the spotlight in diverse areas. The sulfuric acid was used as an anodizing electrolyte, controlling its temperature to $10^{\circ}C$. The anode was 5083 Al alloy with dimension of $5(t){\times}20{\times}20mm$ while the cathode was the platinum. The distance between the anode and the cathode was maintained at 3 cm. Agitation was introduced by magnetic stirrer at 300 rpm to prevent localized temperature rise that hinders stable growth of oxide layer. In order to observe surface characteristics with applied current density, the electrolyte temperature, concentration was maintained at constant condition for $10^{\circ}C$, 10 vol.%, respectively. To prevent hindrance of stable growth of oxide layer due to local temperature increase during the experiment, stirring was maintained at constant rate. In addition, using galvanostatic method, it was maintained at current density of $10{\sim}30mA/cm^2$ for 40 minutes. The cavitation experiment was carried out with an ultrasonic vibratory apparatus using piezo-electric effect with modified ASTM-G32. The peak-to-peak amplitude was $30{\mu}m$ and the distance between the horn tip and specimen was 1 mm. The specimen after the experiment was cleaned in an ultrasonic, dried in a vacuum oven for more than 24 hours, and weighed with an electric balance. The surface damage morphology was observed with 3D analysis microscope. As a result of the investigation, differences were observed surface hardness and anti-cavitation characteristics depending on the development of oxide film with applied current density.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.968-980
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• 2023

The dynamic structural responses are sensitive to the time-frequency content of seismic waves, and seismic input motions in time-history analysis are usually required to be compatible with design response spectra according to nuclear codes. In order to generate spectra-compatible input motions while maintaining the intrinsic non-stationarity of seismic waves, an improved time-domain approach is proposed in this paper. To maintain the nonstationary characteristics of the given seismic waves, a new time-frequency envelope function is constructed using the Hilbert amplitude spectrum. Based on the intrinsic mode functions (IMFs) obtained from given seismic waves through variational mode decomposition, a new corrective time history is constructed to locally modify the given seismic waves. The proposed corrective time history and time-frequency envelope function are unique for each earthquake records as they are extracted from the given seismic waves. In addition, a dimension reduction iterative technique is presented herein to simultaneously superimpose corrective time histories of all the damping ratios at a specific frequency in the time domain according to optimal weights, which are found by the genetic algorithm (GA). Examples are presented to show the capability of the proposed approach in generating spectra-compatible time histories, especially in maintaining the nonstationary characteristics of seismic records. And numerical results reveal that the modified time histories generated by the proposed method can obtain similar dynamic behaviors of AP1000 nuclear power plant with the natural seismic records. Thus, the proposed method can be efficiently used in the design practices.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.252-258
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• 2022

In this study, we propose a method for analyzing students' understanding of probability and statistics in small lectures at universities. A computer-based test for probability and statistics was performed on 95 science and engineering college students. After dividing the students' responses into 7 clusters using the Robust PCA and the Gaussian mixture model, the achievement of each subject was analyzed for each cluster. High-ranking clusters generally showed high achievement on most topics except for statistical estimation, and low-achieving clusters showed strengths and weaknesses on different topics. Compared to the widely used PCA-based dimension reduction followed by clustering analysis, the proposed method showed each group's characteristics more clearly. The characteristics of each cluster can be used to develop an individualized learning strategy.

• The Journal of Korean Academy of Prosthodontics
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• v.34 no.2
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• pp.277-289
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• 1996

The purpose of this study was to compare the amount of the vertical pin opening of dentures processed by the conventional compression method & the injection-processing method. The articulating ribbon and T-Scan system were used to determine the changes in tooth contacts and the correlation between the changes in tooth contacts and the amount of the vertical pin opening after processing was analyzed statistically. The obtained results were as follows. 1. The amounts of the vertical pin opening were 0.75mm, 0.31mm for conventional compression method and injection-pressing method, respectively. The difference between the two methods was highly significant on the basis of t test statistics(p<0.05). 2. The total number of contact points recorded by articulating ribbon after processing was decreased to 48%, 72%, of that before processing for conventional compression method and injection-pressing method, by T-Scan system, 39%, 53%, respectively. 3. The mean values for the number of contacts points per tooth recorded by articulating ribbon after processing were 1.6 for the second molar, 2.2 for the first moalr, 0.4 for the second premolar, 0.6 for the first premolar in conventional compression method, and 2, 3.2, 1.2, 0.8 in injection-pressing method. 4. The correlation between the reduction of contact points and the amount of the pin opening after processing was not significant. 5. The mean values for the number of contact points per tooth recorded by T-Scan system after processing were 1.25, 1.4, 0.7, and 0.55 at the second molar, the first molar, the second premolar, and the first premolar in conventional compression method, and 1.45, 2.1, 1.0, 0.75 in injection method, respectively. 6. The correlation between the number of contact points by using articulating ribbon and T-Scan system after processing was not significant in both methods.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.733-741
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• 2009

The design rule of digital communication systems is the reliable data transmission with high spectral efficiency and minimum allowable power. This paper suggests the method that saves the average power by implementing a multi-dimensional constellation in case of multi-carrier communication system. By using multi-dimensional constellations we can relocate constellation points in the form of a sphere. If we simply convert the two-dimensional QAM modulation into multi-dimensional QAM, constellation points of 2 N dimensional cube form are made up. Relocating outermost constellation points of 2 N dimensional cube form into low energy constellation points, the constellation of the 2 N-dimensional sphere form is made up which decreases power consumption. In this paper, the multi-dimensional constellations of 2 N-dimensional sphere form are designed from 16-QAM to 2,048-QAM, and power reductions are obtained by comparing constellations of 2-dimensional QAMs and multi-dimensional constellations of 2 N-dimensional sphere form. The result shows that the average power consumption of higher dimensional constellations increases, because the more a dimension elevates, the more the relocatable constellation points increase. But, the increment of the average power savings decreases as the a dimension elevates. The transmission of the data by using multi-dimensional constellations of the sphere form is effective to save the average power consumption with little hardware complexity.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.1
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• pp.13-22
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• 2001

This paper presents three dimensional structure of RF packages and the improvement effect of its electrical characteristics when implementing RF transceivers. We divided RF modules into several subunits following each subunit function based on the partitioning algorithm which suggests a method of three dimension stacking interconnection, PAA(pad area array) interconnection and stacking of three dimensional RF package structures. 224MHz ITS(Intelligent Transportation System) RF module subdivided into subunits of functional blocks of a receiver(RX), a transmitter(TX), a phase locked loop(PLL) and power(PWR) unit, simultaneously meeting the requirements of impedance characteristic and system stability. Each sub­functional unit has its own frequency region of 224MHz, 21.4MHz, and 450KHz~DC. The signal gain of receiver and transmitter unit showed 18.9㏈, 23.9㏈. PLL and PWR modules also provided stable phase locking, constant voltages which agree with design specifications and maximize their characteristics. The RF module of three dimension stacking structure showed $48cm^3$, 76.9％ reduction in volume and 4.8cm, 28.4％ in net length, 41.8$^{\circ}C$, 37% in maximum operating temperature, respectively. We have found that three dimensional PAA package structure is able to produce high speed, high density, low power characteristics and to improve its functional characteristics by subdividing RF modules according to the subunit function and the operating frequency, and the features of physical volume, electrical characteristics, and thermal conditions compared to two dimensional RF circuit modules.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.521-528
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• 2002

We propose a new approach to the feature selection for the classification of handwritten characters using two-dimensional(2D) wavelet packet bases. To extract key features of an image data, for the dimension reduction Principal Component Analysis(PCA) has been most frequently used. However PCA relies on the eigenvalue system, it is not only sensitive to outliers and perturbations, but has a tendency to select only global features. Since the important features for the image data are often characterized by local information such as edges and spikes, PCA does not provide good solutions to such problems. Also solving an eigenvalue system usually requires high cost in its computation. In this paper, the original data is transformed with 2D wavelet packet bases and the best discriminant basis is searched, from which relevant features are selected. In contrast to PCA solutions, the fast selection of detailed features as well as global features is possible by virtue of the good properties of wavelets. Experiment results on the recognition rates of PCA and our approach are compared to show the performance of the proposed method.