• Journal of Digital Convergence
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• v.20 no.5
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• pp.833-846
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• 2022

This study was conducted to identify nutrition knowledge, eating behaviors, and the need for an integrated program to improve eating behaviors according to residential type of female college students. Data were collected from March 16 to April 16, 2021, using online self-report questionnaires and the final 217 data were used. To analyze data, frequencies and percentages, means and standard deviations, independent sample t-test, and ANOVA were conducted using SPSS WIN 26.0. The results showed that home residents had better eating behaviors than those living in rented rooms. Moreover, the demand for an integrated eating behavior improvement program for disease and health information topic was higher among home residents and participants in rented rooms than among those living in dormitories. When developing an integrated program for improving eating behaviors among female college students, it is essential to apply a differentiated intervention program that considers residential type.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1403-1412
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• 2023

The purpose of this study was to examine the effect of an ICT based integrated dementia prevention program on vitality, fear of dementia, preventive behavior of dementia in the elderly using senior citizen centers. The experimental treatment was given an ICT based integrated dementia prevention program for the preventive behavior of dementia for 12 sessions over 6 weeks by the one group pretest-posttest design. Data analysis was Paired t-test, Pearson's correlation coefficient. The level of vitality and preventive behavior of dementia improved, and fear of dementia level decreased for these elderly using senior citizen centers. Therefore, it is recommended that the integrated ICT based dementia prevention program be utilized in clinical settings to prevent dementia among senior citizens using senior centers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.30-39
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• 2024

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

• Atmosphere
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• v.24 no.3
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• pp.445-456
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• 2014

A real-time quality control system for meteorological data (air temperature, air pressure, relative humidity, wind speed, wind direction, and precipitation) measured by an integrated meteorological sensor has been developed based on comparison of quality control procedures for meteorological data that were developed by the World Meteorological Organization and the Korea Meteorological Administration (KMA), using time series and statistical analysis of a 12-year meteorological data set observed from 2000 to 2011 at the Incheon site in Korea. The quality control system includes missing value, physical limit, step, internal consistency, persistence, and climate range tests. Flags indicating good, doubtful, erroneous, not checked, or missing values were added to the raw data after the quality control procedure. The climate range test was applied to the monthly data for air temperature and pressure, and its threshold values were modified from ${\pm}2{\sigma}$ and ${\pm}3{\sigma}$ to ${\pm}3{\sigma}$ and ${\pm}6{\sigma}$, respectively, in order to consider extreme phenomena such as heat waves and typhoons. In addition, the threshold values of the step test for air temperature, air pressure, relative humidity, and wind speed were modified to $0.7^{\circ}C$, 0.4 hPa, 5.9%, and $4.6m\;s^{-1}$, respectively, through standard deviation analysis of step difference according to their averaging period. The modified quality control system was applied to the meteorological data observed by the Weather Information Service Engine in March 2014 and exhibited improved performance compared to the KMA procedures.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.22-31
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• 2013

For the case of test equipments requiring data accuracy, real-time is highly required in acceptance test for performance evaluation of developed weapons. For convenience' sake, test equipments are usually developed using Windows. However Windows does not support real-time in itself. Thus, in this paper, so as to reduce development time and expenses, we design and implement an integrated middleware for real-time device drivers using RTiK-MP. Using DLL, we also support user API's for the sake of development convenience without details of the complex RTiK-MP structure. We evaluate the performance of the proposed integrated middleware using the RDTSC command which returns the number of CPU clock ticks. The evaluation results show that it operates correctly within error ranges in the periods of 1ms and 4ms for the cases of TCP/IP and RS-232, respectively.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.361-368
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• 2013

The aim of this study was to identify influence that integrated PNF(IPNF) on sprinter and skater pattern exercise had an effect on static and dynamic balance in chronic low back pain elderly adult. The subjects of this study were 34 and they were divided into two groups: IPNF exercise group(n=17), swiss ball exercise group(n=17). They were measured static balance ability using Good Balance System (GBS) and dynamic balance ability using functional reaching test(FRT) and timed up and go (TUG) during pre and post exercise. The results of this study were significant differences in groups about pre and post static and dynamic balance tests the greater part(p<.05). There were significant differences in groups according to exercises(p<.05). The above results from this study indicated that integrated patterns of PNF have improved the static and dynamic balance ability. This study will be enough to provide the basic raw material using a integrated PNF.

• Journal of Korean Society of Steel Construction
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• v.24 no.3
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• pp.313-323
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• 2012

The purpose of this study is to evaluate $CO_2$ reduction and the flexural performance of steel wire-integrated void deck plate slabs that were inserted in omega-shaped steel plates to reduce concrete and welded H-section beams. The void deck plate slab can secure the structure, not only reducing the weight of the building but it is also eco-friendly. Therefore, this study evaluated the flexural performance of the composite beam by conducting a monotonic loading test with the use of actuators. It quantitatively evaluated the $CO_2$ emission based on earlier studies. The main test parameters are the concrete thickness of upper slabs, and the interrupted width of the omega-shaped steel plate. The result of the test showed that the welded H-section beam applied steel wire-integrated void deck plate slabs that were inserted into the omega-shaped steel plate declined in flexural performance on the composite beam after reducing concrete volume. Likewise, it is effective in reducing $CO_2$.

• Journal of Information Technology Services
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• v.12 no.4
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• pp.255-265
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• 2013

This paper proposes a DEA-AR model for the efficiency evaluation of the iron ore brands in an integrated steel mill. The input factor is defined as unit cost of each brand based on CIF and two output factors are chosen as Fe and Al which are the important ingredients of iron ore. The relative importance between two output factors is determined by several experts using AHP model. The efficiency of each brand is determined using DEA and DEA-AR models. The negative correlation between the DEA-AR efficiency and the unit cost (CIF) is shown as significant whereas no significant correlation exist between the efficiency and the output factors. Also, the Kruskal Wallis rank sum test shows that there exist efficiency differences among the iron ore types whereas no difference is shown among the countries. The result could be utilized in selecting good brands of iron ores based on the DEA-AR efficiency in an integrated steel mill.

• Journal of the Korean Institute of Gas
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• v.17 no.6
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• pp.46-51
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• 2013

Composite cylinder is used by hydrogen fuel cell vehicles and natural gas vehicles because of high specific modulus, specific strength and fatigue resistance. composite cylinder has a seamless integrated liner and it is fully overwrapped with structural fibers of high strength carbon fibers in an epoxy matrix. In this study, filament winding pattern and autofrettage pressure design technique are presented considering structural weakness of knuckle and compressive residual stress. Presented methodology is verified by pressure cycling test of composite cylinders.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.