• Journal of the Korean Geographical Society
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• v.41 no.1 s.112
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• pp.94-105
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• 2006

This paper aims to analyze changes of unusual temperature events on summer and winter and their controlling factors. There has been obviously an increased frequencies of summer unusual high temperature occurrence and decreased frequencies of winter unusual low temperature at most of stations. WMI, winter SHI and AOI might be essential for prediction of unusual temperature during winter and summer OHI and spring NPI for summer unusual low temperature. These factors are crucial because they reflect the recent global warming trend as well as have apparent associations with unusual temperature occurrence frequency in Korea.

• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.75-82
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• 2008

A vibration-based damage-monitoring scheme is proposed that would generate an alarm showing the occurrence and location of damage under temperature-induced uncertainty conditions. Experiments on a model plate-girder bridge are described, for which a set of modal parameters was measured under uncertain temperature conditions. A damage-alarming model is formulated to statistically identify the occurrence of damage by recognizing the patterns of damage-driven changes in the natural frequencies of the test structure and by distinguishing temperature-induced off-limits. A damage index method based on the concept of modal strain energy is implemented in the test structure to predict the location of damage. In order to adjust for the temperature-induced changes in the natural frequencies that are used for damage detection, a set of empirical frequency correction formulas is analyzed from the relationship between the temperature and frequency ratio.

• Steel and Composite Structures
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• v.14 no.4
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.5 s.248
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• pp.576-584
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• 2006

The actuating performance test of plate-type piezoelectric composite actuators having different lay-up sequences was experimentally carried out at simply supported and fixed-free boundary conditions. The actuating displacement of manufactured plate-type piezoelectric composite actuator (PCA) was measured using a non-contact laser displacement measurement system. Our results revealed that the actuating displacement with increasing applied electric field at a drive frequency of 1Hz increased non-linearly at the simply supported boundary condition whereas it almost linearly increased at the fixed-free boundary condition. On the other hand, the actuating displacement of piezoelectric composite actuator depended on the applied electric field in a drive frequency range from 1Hz to 10Hz, but its behavior was different in higher drive frequencies beyond 15Hz due to the occurrence of resonance. On the basis of the above experimental results, the bending characteristics of PCAs revealed different behavior depending on applied electric fields, drive frequencies as well as boundary conditions. Therefore, by investigating drive frequencies together with applied electric fields, actuating performance can be easily controlled and PCAs which were fabricated for this study will be sufficiently applied to pumping devices.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.3-9
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• 2015

Since changes in land use in urban space cause traffic volume and it is closely related to traffic accidents. Therefore, an analysis on the causes of traffic accidents is judged to be an essential factor to establish the measure to reduce traffic accidents. In this regard, the analysis was conducted on the clustering by using the nearest neighbor indexes with regard to the occurrence frequencies of commercial and residential zone based on traffic accident data of the past five years (2009-2013) with the target of local small-medium sized city, Jinju-si. The analysis results, obtained in this study, are as follows: the occurrence frequency of traffic accidents was the highest in spring and the lowest in winter respectively. The clustering of traffic accident occurrence at nighttime was stronger than at daytime. In addition, terms of the analysis on the clustering of traffic accident according to land use, changes according to the seasons was not significant in commercial areas, while clustering density in winter tended to become significantly lower in residential areas. The analysis results of traffic accident types showed that the side-right angle collision of cars was the highest in frequency occurrence, and widespread in both commercial areas and residential areas. These results can provide us with important information to identify the occurrence pattern of traffic accidents in the structure of urban space, and it is expected that they will be appropriately utilized to establish measures to reduce traffic accidents.

• Journal of the Korean Society of Food Science and Nutrition
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• v.27 no.1
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• pp.182-190
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• 1998

This study was conducted to compare food intakes between newly diagnosed diabetics and non-diabetics by food frequency questionnarie in adults in rural area. Food frequency questionnaire containing 65 food items was executed to 2,406 subjects over 30 years of age living inYeonchon-gun, Kyungki province. Frequency of consumption of each food items were divided into 9 categories ranging from 'over 3 tiemes a day' to 'never'. Fasting blood glucose levels were measured and 2-hour glucose tolerance test was administered for each subject. Newly diagnosed diabetics were identified as those with fasting blood glucose level ≥ 140mg/dl or 2-hour postprandial glucose level ≥200mg/dl and without previous diagnosis of diabetes. Intake frequencies of food items were compared between newly diagnosed diabetics and nondiabetics. For each food item examined, odds ratios for developing diabetes were calculated for people consuming more frequently, after adjusting for age, BMI and sex. Food intake frequencies were compared according to the occurrence of diabetes. Twenty four items were more frequently consumed by nonidabetics and 12 items were more frequently consumed by newly diagnosed diabetics. Odds ratios for diabetes were significantly lower for breads, biscuit, beef loin, beef tender loin, pork belly, fish paste, coffee, cola/cider, candy, beer, chongak kimchi, carrot, mushrooms and other white vegetables, banana, melon and juice. On the other hand, odds ratios for diabetes were higher for zucchini and garlic. Although this was a cross over prevalence study, the results indicate that consumption frequencies of several food items were related with the occurrence of diabetes in the study subjects.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1113-1121
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• 2011

Recently, a variety of data mining techniques has been applied in various fields like healthcare, insurance, and internet shopping mall. Association rule mining is a popular and well researched method for discovering interesting relations among large set of data items. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are three primary quality measures for association rules; support and confidence and lift. In this paper we consider some similarity measures with negative co-occurrence frequencies which is widely used in cluster analysis or multi-dimensional analysis as association thresholds. The comparative studies with support, confidence and some similarity measures are shown by numerical example.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Journal of the Korean Geographical Society
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• v.40 no.3 s.108
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• pp.285-295
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• 2005

This paper was examined distribution and changing trends on occurrence days of Spring Cold(cold surge in the flowering season) from the end of February to early in April during 1973-2004. We defined an occurrence day of Spring Colds as above $10\%$ of the anomalies of mean daily temperature or above $10\%$ of the difference of the daily mean temperature between the day before two days. In the west coastal region, the occurrence days of Spring Colds is higher in the end of February and less frequent in the early in April. In the east coastal region, the occurrence days of Spring Colds is higher in the early in April. Spring Colds is closely related with frequencies and a intensities of Siberian High. The occurrence days of Spring Colds has decreased in the most area except Ulleung island and the east coastal region.