• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Women's Health Nursing
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• v.28 no.2
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• pp.112-122
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• 2022

Purpose: The prevalence of osteoporosis in postmenopausal women is increasing, and diabetes patients have decreased bone density. Their health-related quality of life (HRQoL) is diminished by the resultant physical dysfunction and depression. The purpose of this study was to identify factors influencing HRQoL in postmenopausal women with diabetes and osteoporosis. Methods: This was a secondary data analysis of the Seventh Korea Health and Nutrition Examination Survey (2016-2018), which utilized a complex, multistage probability sample design. The participants in the study were 237 women with diabetes and osteoporosis. To evaluate the factors that influenced HRQoL, a complex-samples general linear model was constructed, and the Bonferroni correction was performed. Results: In this sample of women aged 45 to 80 years (mean±standard deviation, 71.12±7.21 years), the average HRQoL score was 0.83±0.18 out of 1.0. Factors influencing HRQoL were age (70s: t=-3.74, p<.001; 80s: t=-3.42, p=.001), walking for exercise more than 5 days a week (t=-2.83, p=.005), cerebrovascular disease (t=-8.33, p<.001), osteoarthritis (t=-2.04, p=.014), hypertension (t=2.03, p=.044), higher perceived stress (t=-2.17, p=.032), poor glycemic control (t=3.40, p=.001), waist circumference (t=-2.76, p=.007), sitting time per day (t=-2.10, p=.038), and a longer postmenopausal period (t=3.09, p=.002). Conclusion: In order to improve the HRQoL of postmenopausal women with osteoporosis and diabetes, it is necessary to implement intervention strategies that enable the effective management of chronic diseases, while preventing the complications of diabetes and minimizing stress through physical activity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Journal of Veterinary Clinics
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• v.40 no.3
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• pp.238-241
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• 2023

This study determined the effect of ovarian status at the beginning of the modified Double-Ovsynch program on reproductive performance in dairy cows. In the study, 1,302 cows were treated with a modified Double-Ovsynch program at 56 days after calving. This program comprises administering gonadotropin-releasing hormones (GnRH), prostaglandin F_2α (PGF_2α) 10 days later, GnRH 3 days later, GnRH 7 days later, and GnRH 56 h later, followed by timed artificial insemination (TAI) 16 h later. At the beginning of the program, cows were categorized according to the size of the largest follicle and the presence of a corpus luteum (CL) in the ovaries as follows: 1) small follicle (<5 mm, SF group, n = 100), 2) medium follicle (8-20 mm, MF group, n = 538), and 3) large follicle (≥25 mm, LF group, n = 354) without a CL, or 4) the presence of a CL (CL group, n = 310). The pregnancies per AI after the first TAI were analyzed by logistic regression using the LOGISTIC procedure, and the logistic model included the fixed effects of the herd size, parity, body condition score (BCS) at the first TAI, TAI period, and ovarian status. A larger herd size, higher BCS at the first TAI, and TAI period with no heat stress increased (p < 0.05) the probability of pregnancy per AI after the first TAI. However, ovarian status at the beginning of the program did not affect (p > 0.05) the pregnancies per AI (ranges of 37.9% to 42.9%). These results show that the modified Double-Ovsynch program can be used effectively while maintaining good fertility regardless of the ovarian status in dairy herds.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.530-537
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• 2016

This study investigates frontal crashes, analyzes the driver's action related to the change of the collision direction and determines the severity of (bodily injury). This study was conducted from August, 2013, to January, 2014, and the data for the car damage and human body damage were collected by emergency medical teams. In terms of data collection, we collected the accident vehicle, crash direction, body damage, etc., based on the Korea In-depth Accident Study (KIDAS) and Injury Severity Score (ISS). We used Minitab 17 and SPSS 22.0 to do the frequency analysis and ANOVA. In the analysis results, the prevalence of frontal collisions was 55.8% and mostly occurred in the 12 o'clock direction. In the analysis of the frontal crash direction according to age, the average ages for the 11, 12 and 1 o'clock directions were $46.46{\pm}13.47$, $44.43{\pm}13.40$ and $52.46{\pm}12.04$, respectively, so the older age drivers had a high probability of the accident occurring in the 1 o'clock direction. In the analysis of men's frontal collision direction according to age, the average ages in the 11, 12 and 1 o'clock directions were $47.10{\pm}13.88$, $45.24{\pm}13.78$ and $55.73{\pm}13.38$, respectively, so older aged men had a high probability of having collisions in the 1 o'clock direction. However, the statistical analysis of the frontal crash direction according to age in women didn't show any meaningful trend. When comparing the ISS according to age of the men and women in the collision direction, the men were less likely to have a 12 o'clock collision when $ISS{\geq}9$ and more likely to have a 1 o'clock collision when ISS<9. As a result, frontal crashes are more likely to occur in the 12 o'clock direction and the ISS decreases because the likelihood of frontal crashes in the 1 o'clock direction increases with increasing age. Therefore, when men recognize that they are heading for a 12 o'clock direction collision, they try to steer to the left to reduce the body damage.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.675-681
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• 2021

Recently, the Ministry of Education stipulates in the distance class operation regulations that student lecture evaluations for distance learning subjects should be conducted at least twice per semester and the results should be disclosed to students. Therefore, the lecture evaluation of D college was compared with the first semester of 2020 and the first semester of 2021. As for the multiple-choice evaluation result of the distance learning mid-course evaluation, the overall average of the mid-course evaluation of the distance class in the first semester of 2020 increased from 4.1819 to 4.4000 in the mid-course evaluation in the first semester of 2021.In the case of the first semester of 2020, due to Corona 19, all non-face-to-face classes were held, but in the first semester of 2021, face-to-face classes increased. The overall satisfaction level rose from 4.18 points in the first semester of 2020 to 4.39 points in the first semester of 2021. The screen composition, sound and picture quality, playback time, face appearance, lecture material provision, and frequency of use of the top 3% and bottom 3% also increased. Despite the changes caused by the LMS replacement, which was a concern, student attendance, assignments, and test submission rates also increased compared to the previous year. The null hypothesis that 'the difference between the two scores is the same' is the null hypothesis because the probability of significance is 0.000 and less than 0.05 in the case of the best 3% of the test result of the test result of the mid-course evaluation of distance classes in the first semester of 2020 and the evaluation of the intermediate lectures in the first semester of 2021. As this was rejected, it can be seen that the best score for the 2021 school year has significantly increased compared to the first semester of 2020. Also, in the case of Worst 3% or less, the significance probability is 0.000, which is less than 0.05, so the null hypothesis that 'the difference between the two scores is the same' was rejected, indicating that the Worst score for the 2021 school year was significantly higher than that for the first semester of 2020.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.