• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.6
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• pp.25-38
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• 2010

This study focused on the analysis of satisfaction and preference of residents' for water space as a environmental friendly facility in residential complex. The purpose of this study is to serve design data of water space in residential complex, in order to make water space that residents' satisfaction are high. In this study, residents' satisfaction and preferences of water space in 5 residential complex were analyze through a questionnaire survey of residents. The major findings of the study are as follows. First, in terns of the need of the water space, 60.4% of the respondents answered that water space are need to improve the amenity. Secondly, in terms of satisfaction, 57.3% of the respondents have been satisfied with location of water space in the residential complex, and the maintenance satisfaction degree is above average level. Overall satisfaction degree is above average level too, but it is are not high as compared construction cost. Thirdly, in terms of preference, 26.5% of respondents have been prefer to the combined type of water space, and 25.9% of respondents prefer to dropping water type, and 25.9% of respondents prefer to flowing stream type. It appeared that the preference levels of 3 type is high similarly, so we can assume that residents prefer to moving water type because of having a feeling refreshed through the sound of water. The results of this study can be used as the design data of water space in residentialcomplex and expected to contributed in making the water space that residents' satisfaction are high.

• Tribology and Lubricants
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• v.38 no.5
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• pp.179-184
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• 2022

The power train of transmission for 21-ton grade wheel excavator makes use of a complex gear train composed of a planetary and helical gear system to drive the wheel excavator by transmitting power to the axle. The complex gear train with a shift mode is an important part of the transmission because of strength problems in an extreme environment. To calculate the specifications of the complex gear train and analyze the gear bending and compressive stresses of the complex gear train, this study analyzes gear bending and compressive stresses accurately for the optimal design of the complex gear train with respect to cost and reliability. In this article, the gear bending and compressive stresses of the complex gear train are calculated using the Lewes and Hertz equation. Evaluating the results with the data of the allowable bending and compressive stress from the stress and number of cycles curves of the gears verified the calculated specifications of the complex gear train. A computer structure analysis is performed with the 3D model of the planetary and helical gears to analyze the structure strength of the complex gear train. The results demonstrate that the durability and strength of the complex gear train are safe, because the safety factors of the bending and compressive stresses are more than 1.0.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.353-358
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• 1998

GRASS-AGNPS model integrated with GIS was applied to rural watersheds having complex land use characteristics and evaluated for its applicability through calibration using observed data. The analyses of raster encoding accuracy and model behavior to runoff, sediment yields and nutrient loads for different cell-size showed that 150 m cell size indicated reasonable applicability of the model. Simulated runoff was in a good agreement with the observed data and simulated peak runoff rate was larger than the observed data. The sediment yield simulated by modified AGNPS model using irregular cell for forest area were less than that of the regular cell method. In predicting sediment yields, the result showed a different trend at each representative rural watershed. Nutrient loads simulated by the model were significantly different from the observed data.

• Genomics & Informatics
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• v.1 no.1
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• pp.1-6
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• 2003

In conclusion, the seemingly fuzzy and disorganized data of biology with thousands of different layers ranging from molecule to the Internet have refused so far to be mapped precisely and predicted successfully by mathematicians, physicists or computer scientists. Genomics and bioinformatics are the fields that process such complex data. The insights on the nature of biological entities as complex interaction networks are opening a door toward a generalization of the representation of biological entities. The main challenge of genomics and bioinformatics now lies in 1) how to data mine the networks of the domains of bioinformatics, namely, the literature, metabolic pathways, and proteome and structures, in terms of interaction; and 2) how to generalize the networks in order to integrate the information into computable genomic data for computers regardless of the levels of layer. Once bioinformatists succeed to find a general principle on the way components interact each other to form any organic interaction network at genomic scale, true simulation and prediction of life in silico will be possible.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1133-1140
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• 2008

The goal of this paper is to analysis of the trend for stream quality in Gumi industrial complex with Winters' exponential smoothing method. It used the five different monthly time series data such as BOD, COD, TN, TP and EC from January 1998 to December 2006. The data of BOD, COD, TN, TP and EC are analyzed by time series method and forecasted the trends until December 2007. The stream qualities change for the better about BOD, COD, TN and TP, but the stream qualities resulted by EC is still serious.

• Survey Research
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• v.1 no.1
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• pp.109-123
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• 2000

In order to make statistically valid inferences for survey data based on complex probability sample designs, survey researchers must incorporate the sample design in the data analysis If this in not the case the variance estimates of survey statistics derived under the usual simple random sampling assumptions from an infinite population generally underestimate the true variance, which results in high Type l error level. In this article we introduce new software packages dedicated to analyze complex survey data In particular, we summarize analysis capabilities on SUDAAN Version 7.5 and SAS Version 8.

• Journal of the Korea Safety Management & Science
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• v.3 no.2
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• pp.113-121
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• 2001

Today, facilities are composed of many complex components or parts. Because of this characteristics, the frequency of failures is decreasing, but the strength of failures is increasing; therefore, the failure analysis about many complex components or parts was needed. In the former research about Fault Tree Analysis, failure data of similar facilities have been used for forecasting about target system or components, but in case that the system or components for forecasting failure is new or qualitative and quantitative data are given simultaneously, there are many difficulty in using Fault Tree Analysis with this incorrect failure data. Therefore, this paper deal with the Fault Tree Analysis method which be applied with Fuzzy theory in above case. In case that , therefore, if there is no the correct failure data, it is represented a system or components as qualitative variable. subsequently, it converted to the quantitative value using fuzzy theory, and the values used as the value for failure forecast.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.3
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• pp.253-258
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• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.