• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.294-298
• /
• 2015

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

• Proceedings of the KIPE Conference
• /
• 2014.11a
• /
• pp.11-12
• /
• 2014

This paper introduces a scaled hardware model for the 10kVA, 1kV, 11-level MMC (Modular Multilevel Converter), which was manufactured in the lab based on computer simulations with PSCAD/EMTDC. Various experiments were conducted to verify the major operation algorithms of MMC. The hardware scaled-model developed in the lab can be utilized for analyzing the operation analysis and performance evaluation of MMC according to the modulation pattern and redundancy operation scheme.

• Journal of Industrial Convergence
• /
• v.22 no.1
• /
• pp.1-20
• /
• 2024

Regarding the discourse on the correlation between governmental financial support and firm performance, much emphasis has been placed on the role of individual corporate characteristics as well as spatial features. However, there is a notable scarcity of empirical research examining the integrated impact of corporate and cluster characteristics on managerial performance. This study addresses this gap by empirically analyzing the financial and non-financial outcomes resulting from specific allocations of governmental financial support. Additionally, it explores corporate and cluster characteristics predicted to moderate the influence between governmental financial support and firm performance. The analysis employs a two-level hierarchical linear model (HLM) at individual and group levels. The data, reorganized based on business registration numbers at the firm and cluster levels, ultimately utilized panel data from 83,395 firms and 641 clusters. The research findings indicate that governmental financial support demonstrates a positive effect (+) on both sales and patents for firms, suggesting its effectiveness in complementing market failures. Results from the hierarchical linear model analysis show that when combined with human capital capacity, absorptive capacity, and cluster network density, governmental financial support exhibits significant positive effects on sales. This study contributes theoretical and practical insights by analyzing the relationship between governmental financial support and firm performance using a two-level hierarchical linear model. It highlights the role of corporate characteristics such as human capital and absorptive capacity, along with cluster characteristics like cluster network density, in moderating the effects of governmental financial support on firm performance.

• Health Policy and Management
• /
• v.32 no.1
• /
• pp.63-72
• /
• 2022

Background: Depression is a common disease around the world. Many studies are showing that mental health can be improved through physical activity, and daily regular exercise can reduce the negative effects of depression or depressive symptoms. In order to promote individual physical activity, a physical activity-friendly environment must precede. Therefore, this study attempted to confirm whether the number of sports facilities for all affects individual depression. Methods: Among the respondents to the 2018 Community Health Survey, data from 181,086 people excluding missing value were used. Descriptive and chi-square tests were performed to understanding the general characteristics of individual level variables. A multilevel logistic regression was conducted to confirm the effect of individual and regional level variables on depressive symptoms. Results: As a result of confirming the effect of individual characteristics on depressive symptoms, it was confirmed that both socioeconomic and health behavior factors had an effect. Similar results were shown in a model that considered regional level variables, and in the case of the number of sports facilities per population, people who belongs to smaller areas were more likely to have depressive symptoms (odds ratio, 0.98; 95% confidence interval, 0.97-0.99). Conclusion: As a result of the analysis, it was confirmed that both individual level and regional level variables had a significant effect on depressive symptoms. This suggests that not only individual level approaches but also regional level approaches are needed to improve individual depressive symptoms In particular, it may be possible to consider to increase the number of sports facilities in areas where the prevalence of depressive symptoms is high and the number of sports facilities is insufficient.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.1
• /
• pp.19-26
• /
• 2023

Because of the development of computational resources, an entire structure in which many components are combined can be analyzed. To do so, the calculation time and number of data points are increased. In many practical industry structures, there are many parts with repeated patterns. To analyze the repetitive structures effectively, a homogenization method is usually employed. In a homogenization module, including commercial programs, it is generally assumed that a unit cell is repeated in all directions. However, the practical industry structures usually have complicated, repeated patterns or structures. Complicated patterns are difficult to address using the conventional homogenization method. Therefore, in this study, a multilevel homogenization method was devised to consider complex regularities. The proposed homogenization method divides the structure into several areas and performs multiple homogenizations, resulting in a more accurate analysis than that provided by the previous method.

• Communications for Statistical Applications and Methods
• /
• v.25 no.2
• /
• pp.173-184
• /
• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• Korea journal of population studies
• /
• v.11 no.1
• /
• pp.97-116
• /
• 1988

This study examines the socioeconomic determinants of fertility behavior in Korea by developing a model which simultaneously takes into account both individual and community-level differences. It especially focuses on the micro-macro nexus of fertility behavior depending on social contexts. This study utilizes micro data obtained from the 1974 Korean National Fertility Survey(KNFS), and macro data obtained from Korean government statistics. The framework of the model is formalized as a set of structural equations modelling the fertility process. The model is formed on a cohort-specific processual basis and is restricted to five-year birth cohorts. Three cohorts of women are studied : those aged 30-34, 35-39, and 40-44. The model includes three fertility-process components : age at first birth, early fertility, and later fertility, which are defined by reference to the age of the mother. The results of this study indicate that socioeconomic development in Korea results in increased age at first birth and reduced numbers of children per couple. In addition to the developmental change, Korea's fertility decline is found to be facilitated by family planning programs. As expected, the effect of family planning on fertility is greater among better-educated women than among poorly educated women. The inconsistent but suggestive result, however, is that the effect of socioeconomic development on fertility is greater among less-privileged women than among more-previleged women.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.83-93
• /
• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• Health Policy and Management
• /
• v.32 no.4
• /
• pp.347-355
• /
• 2022

Background: Since November 2019, long-term care hospitals have been able to provide patients with discharging programs to support the elderly in the community. This study aimed to identify both patient- and hospital-level factors that affect successful community discharge from long-term care hospitals. Methods: A multilevel logistic regression model was performed using hospitals as a clustering unit. The dependent variable was whether a patient stayed in the community for at least 30 days after discharge from a long-term care hospital. As for the patient-level independent variables, an agreement between a patient and the family about discharge, length of hospital stay, patient category, and residence at discharge were included. The number of beds and the ratio of long-stay patients were selected for the hospital-level factors. The sample size was 1,428 patients enrolled in the discharging program from November 2019 to December 2020. Results: The number of patients who were discharged to the community and stayed at least for 30 days was 532 (37.3%). The intraclass correlation coefficient was 22.9%, indicating that hospital-level factors had a significant impact on successful community discharge. The odds ratio (OR) of successful community discharge increased by 1.842 times when the patients and their families agreed on discharge. The ORs also increased by 3.020 or 2.681 times, respectively when the patients planned to discharge to their own house or their child's house compared to those who didn't have a plan for residence at discharge. The ORs increased by 1.922 or 2.250 times when the hospitals were owned by corporate or private property compared to publicly owned hospitals. The ORs decreased by 0.602 or 0.520 times when the hospital was sized over 400 beds or located in small and medium-sized cities compared to less than 200 bedded hospitals or located in metropolitan cities. Conclusion: The results of the study showed that the patients' and their family's willingness for discharge had a great impact on successful community discharge and the hospital-level factors played a significant role in it. Therefore, it is important to acknowledge and support long-term care hospitals to involve active in the patient discharge planning process.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.7
• /
• pp.299-306
• /
• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.