• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.5
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• pp.810-818
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• 1987

A new method is proposed to identify two-phase flow regimes in horizontal gas-liquid flow, based upon a statistical analysis of instantaneous pressure drop curves at an orifice. The probability density functions of the curves indicate distinct patterns depending upon the two-phase flow regime. The transition region also could be identified by the distribution shape of the probability density function. The statistical properties of the pressure drop are analyzed for various flow regimes and transitions. Finally, the data of flow patterns determined by the proposed method are compared with the flow pattern maps suggested by other investigators.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.5
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• pp.463-468
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• 2002

Influence of increased temperature on the standard metabolism in three species of marine bivalves, Crassostrea gigas, Ruditapes philippinarum and Mytilus edulis, acclimated to seasonal water temperatures and collected from the south coast of Korea, were examined in the laboratory. The standard oxygen consumption and filtration rates in the 3 species were measured respectively at the experimental temperature, 4, 7 and 10$^{\circ}C$ or 3, 6 and 9$^{\circ}C$ higher than the mean seasonal water temperature. When the experimental temperatures were higher than the seasonal water temperature, the rates of C. gigas decreased in autumn and spring, and increased In winter, while there was thermal stress in summer. The rates of R. philippinarum increased in spring when the experimental temperatures were 3$^{\circ}C$ and 6$^{\circ}C$ higher than the seasonal water temperature, but the rates increased in autumn and winter when the experimental temperature was even 9$^{\circ}C$ higher than the seasonal water temperature. In summer. metabolic activities of R. philippinarum decreased significantly at temperature higher than acclimation temperature. The rates of M. edulis increased in spring when the experimental temperatures were 3$^{\circ}C$ higher than the seasonal water temperature but the rates were stressed by the increased temperature above 3$^{\circ}C$. In winter, increased temperature did not affect the metabolic activities of M. edulis. These results suggested that the standard metabolism of the three marine bivalves in summer was stressed by the increased temperature, whereas the metabolism was activated in winter.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.41-59
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• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.1-8
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• 2016

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring(SHM) technique is ever-increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and are influenced by various external loads. Generally, the visual inspection and non-destructive test for an accessible point of structures are performed by experts. But nowadays, the system is required which is online measurement and detect risk elements automatically without blind spots on structures. In this study, in order to consider the response of non-linear structures, proposed a signal feature extraction and the adaptive threshold setting algorithm utilized to determine the abnormal behavior by using statistical methods such as control chart, root mean square deviation, generalized extremely distribution. And the performance of that was validated by using the acceleration response of structures during earthquakes measuring system of forced vibration tests and actual operation.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.495-501
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• 2011

The characteristics of a discriminator estimating a tracking error in a signal tracking loop of a GPS receiver can be affected by the noise power, and the slope of the discriminator function is actually lowered as the noise power increases. In this paper, an algorithm to compensate the lowered slope of the function using the estimated C/N0 is studied, and a new design method of a code tracking which provides more accurate tracking error than a conventional one by adopting the compensation algorithm is proposed. Through the experimental results, finally, it has been check that the accuracy of the proposed DLL is enhanced about 50% when the dynamics of the vehicle is 20g/s.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.265-268
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• 2006

인버터의 개발에 앞서 살펴야 할 것 중 하나로 부하의 종류이다 여기에서는 유체부하를 가지는 경우이다 경제적인 측면과 성능을 고려하여 마이크로컨트롤러(PIC18F4431)를 결정하였다. 또한 여기에서는 전반적인 인버터의 제어를 다루기보다는 유체부하에서 발생하기 쉬운 과부하상태시의 간단한 제어방안을 다루었다 유도기제어에 있어서 유체부하를 가지는 경우 갑작스러운 유체부하의 증가가 발생할 수 있다. 유도기에서 이와 같은 과부하가 발생할 경우 전압과 전류의 위상차는 줄어들게 되고 전류는 증가하게 되며 유도기의 실제 속도와 인버터의 지령치는 벌어지게 된다. 지속적으로 위상차를 감시하여 과부하 상태를 판별할 수 있으며 과부하 상태 시 속도를 변화시켜 실제 속도를 정상상태와 비슷하게 유지시켜준다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.575-580
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• 2024

Detecting the status of websites, normal or phishing, is necessary to defend against intelligent phishing attacks. We propose a machine learning-based classification to predict the status of websites. First, we collect information about 'URL', convert it into numerical data, and remove outliers. Second, we apply VIF(Variance Inflation Factors) to understand the correlation and independence between variables. Finally, we develop a phishing website detection model with machine learning-based classifications, which predicts website status. In the test datasets, Random Forest showed the best performance, with precision of 93.74%, recall of 92.26%, and accuracy of 93.14%. In the future, we expect to apply our model to detect various phishing crimes.

• Economic and Environmental Geology
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• v.35 no.2
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• pp.137-147
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• 2002

Natural geological and environmental processes reflect to element abundances in geological materials on the surface. This study aims to elucidate a possibility of geostatistical application to differentiate geochemical anomalies affected by anthropogenic and geogenic factors. A regional geochemical map was produced using 'inverse distance weight interpolation' method for analytical results of stream sediments «150 11m) which were collected from 2,290 first- to second-order streams over the whole Gyeonggi Province. The Jurassic granitic batholith in the southeastern province was selected as a target for the geostatistical examination. Factor analysis was conducted using 22 elements for stream sediments from 445 drainage basins over the granitic body. Co, Cr, Sc, MgO, Fe$_{2}$O$_{3}$, V, and Ni were grouped with high correlation coefficients and the depletion of the components may reflect the whole-rock chemistry of the granite. Regression analysis was done using Co, Cr, and Sc as dependent variables and other six components as independent variables, and the results were drawn as maps. The maps acquired generally show quite similar distribution patterns with those of concentrations of each variable. The similarity in the spatial patterns between the two maps indicates that the application of regression statistics can be valid for the interpretation of regional geochemical data. However, some components show local discrepancies which may be influenced by secondary factors regardless of the basement lithology. The regression analysis may be effective in extracting local geochemical anomalies which may reflect rather anthropogenic pollutions than geogenic influences.

• Economic and Environmental Geology
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• v.46 no.2
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• pp.105-121
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• 2013

This study introduces integrated approach on detection of a leakage in a sea wall based on remote sensing, electric resistivity survey, electromagnetic survey, and borehole survey for the Seosan A-Region sea wall. The satellite temperature distribution from Landsat ETM+ data identifies water leakage distribution and period by analyzing temperature mixing patterns between sea water and fresh water. Electric resistivity survey provides both horizontal and vertical anomaly distributions over the sea wall showing below average electric resistivity. Electromagnetic survey(electrical conductivity survey) reveals the potential possible leakage areas with minimal background impact by comparing electrical conductivity values between high and low tides. Borehole image processing system confirmed the locations of anomalies identified from the other survey methods and distributions of vertical fracture zones. The integrated approach identified 41.7% of the sea wall being the most probable area vulnerable to water leakage and effectively approximated both horizontal and vertical distribution of water leakage. The integrated analysis of remote sensing, electric resistivity survey, electromagnetic survey and borehole survey is considered to be an optimal method in identifying water leakage distribution, period, and extent of fractures knowledged from the boreholes.

• 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.