• 대한간호학회지
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• 제32권1호
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• pp.28-39
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• 2002

The purpose of this study was to measure the prevalence of urinary incontinence (UI), urologic symptoms, chronic health problems they have, and to explore whether the differences in incidence of UI ware by age, sex, voiding pattern, and chronic health problems. Method: 298 subject were selected, age range from 60 to 94 years residing in one city, in republic of Korea. Data was collected presence of UI, urologic symptoms, chronic health problems, knowledge, and the discomfort with incontinent. Collected data was analyzed with frequency, percentage, t-test, and $\chi$2-test. Result: The results of this study are as follows: 1. Mean age was 71.4 years. Prevalent rate of UI was 17.0%, woman showed more than man. 2. UI incidence was significant in age (t=7.84, p=.000), sex ($\chi$2 =9.47, p=.002), and voiding frequency ($\chi$2=18.34, p=.000). Also, UI incidence was significant relationship with chronic health problem of heart disease ($\chi$2 =10.65, p=0.001), hypertension ($\chi$2=4.04, p= 0.046) and respiratory problem ($\chi$2=9.67, p=0.002). 3. The UI was grouped into urgent incontinence (45%), stress incontinence (33%), and combined (22%). UI occurred during the daytime 48% and 17% at night. 4. Only 9.8 % of the UI seek advice and/or treatment for their symptoms, almost 90 % remained untreated due to lack of knowledge or improper information. 5. The discomforts due to their UI was no significant difference in their condition, the urgent use of the rest room, leaking urine, and nocturia. Conclusion: This study suggests that 1 year and 3 year follow-up study is needed to compare health status of UI. Also suggests intervention study for urologic discomfort of incontinent and behavioral education for the elderly are needed.

• Journal of Ecology and Environment
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• 제47권4호
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• pp.264-271
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• 2023

Environmental crises caused by climate change and human-induced disturbances have become urgent challenges to the sustainability of human beings. These issues can be addressed based on a data-driven understanding and forecasting of ecosystem responses to environmental changes. In this study, we introduce a long-term ecological monitoring system in Korean Long-Term Ecological Research (KLTER), and a plan for the Korean Ecological Observatory Network (KEON). KLTER has been conducted since 2004 and has yielded valuable scientific results. However, the KLTER approach has limitations in data integration and coordinated observations. To overcome these limitations, we developed a KEON plan focused on multidisciplinary monitoring of the physiochemical, meteorological, and biological components of ecosystems to deepen process-based understanding of ecosystem functions and detect changes. KEON aims to answer nationwide and long-term ecological questions by using a standardized monitoring approach. We are preparing three types of observatories: two supersites depending on the climate-vegetation zones, three local sites depending on the ecosystem types, and two mobile deployment platforms to act on urgent ecological issues. The main observation topics were species diversity, population dynamics, biogeochemistry (carbon, methane, and water cycles), phenology, and remote sensing. We believe that KEON can address environmental challenges and play an important role in ecological observations through partnerships with international observatories.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Journal of Radiation Protection and Research
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• 제49권1호
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• pp.1-18
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• 2024

Exponential growth has been observed in nuclear medicine procedures worldwide in the past decades. The considerable increase is attributed to the advance of positron emission tomography and single photon emission computed tomography, as well as the introduction of new radiopharmaceuticals. Although nuclear medicine procedures provide undisputable diagnostic and therapeutic benefits to patients, the substantial increase in radiation exposure to nuclear medicine patients raises concerns about potential adverse health effects and calls for the urgent need to monitor exposure levels. In the current article, model-based internal dosimetry methods were reviewed, focusing on Medical Internal Radiation Dose (MIRD) formalism, biokinetic data, human anatomy models (stylized, voxel, and hybrid computational human phantoms), and energy spectrum data of radionuclides. Key results from many articles on nuclear medicine dosimetry and comparisons of dosimetry quantities based on different types of human anatomy models were summarized. Key characteristics of seven model-based dose calculation tools were tabulated and discussed, including dose quantities, computational human phantoms used for dose calculations, decay data for radionuclides, biokinetic data, and user interface. Lastly, future research needs in nuclear medicine dosimetry were discussed. Model-based internal dosimetry methods were reviewed focusing on MIRD formalism, biokinetic data, human anatomy models, and energy spectrum data of radionuclides. Future research should focus on updating biokinetic data, revising energy transfer quantities for alimentary and gastrointestinal tracts, accounting for body size in nuclear medicine dosimetry, and recalculating dose coefficients based on the latest biokinetic and energy transfer data.

• 제어로봇시스템학회논문지
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• 제14권7호
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• pp.720-725
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• 2008

This paper proposes a system architecture for USN with a service robot to provide more active assisted living services for elderly persons by monitoring their mental and physical well-being with USN environments at home, hospital, or silver town. Sensors embedded in USN are used to detect preventive measures for chronic disease. Logged data are transferred to main controller of a service robot via wireless channel in which the analysis of data is performed. For the purpose of handling emergency situations, it needs real-time processing on gathering variety sensor data, routing algorithms for sensor nodes to a moving sink node and processing of logged data. This paper realized multi-hop sensor network to detect user movements with biometric data transmission and performed algorithms on Xenomai, a real-time embedded Linux. To leverage active sensing, a mobile robot is used of which task was implemented with a priority to process urgent data came from the sink-node. This software architecture is anticipated to integrate sensing, communication and computing with real-time manner. In order to verify the usefulness of a proposed system, the performance of data transferring and processing on a real-time OS with non real-time OS is also evaluated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5244-5259
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• 2019

With the continuous development of LBS (Location Based Service) applications, privacy protection has become an urgent problem to be solved. Differential privacy technology is based on strict mathematical theory that provides strong privacy guarantees where it supposes that the attacker has the worst-case background knowledge and that knowledge has been applied to different research directions such as data query, release, and mining. The difficulty of this research is how to ensure data availability while protecting privacy. Spatial multidimensional data are usually released by partitioning the domain into disjointed subsets, then generating a hierarchical index. The traditional data-dependent partition methods need to allocate a part of the privacy budgets for the partitioning process and split the budget among all the steps, which is inefficient. To address such issues, a novel two-step partition algorithm is proposed. First, we partition the original dataset into fixed grids, inject noise and synthesize a dataset according to the noisy count. Second, we perform IH-Tree (Improved H-Tree) partition on the synthetic dataset and use the resulting partition keys to split the original dataset. The algorithm can save the privacy budget allocated to the partitioning process and obtain a more accurate release. The algorithm has been tested on three real-world datasets and compares the accuracy with the state-of-the-art algorithms. The experimental results show that the relative errors of the range query are considerably reduced, especially on the large scale dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1634-1652
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• 2022

Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.872-885
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• 2015

The rapid increase of smart mobile devices and mobile applications has led to explosive growth of data traffic in cellular network. Offloading data traffic becomes one of the most urgent technical problems. Recent work has proposed to exploit opportunistic communications to offload cellular traffic for mobile data dissemination services, especially for accepting large delayed data. The basic idea is to deliver the data to only part of subscribers (called target-nodes) via the cellular network, and allow target-nodes to disseminate the data through opportunistic communications. Human mobility shows temporal and spatial characteristics and predictability, which can be used as effective guidance efficient opportunistic communication. Therefore, based on the regularity of human mobility we propose NodeRank algorithm which uses the encounter characteristics between nodes to choose target nodes. Different from the existing work which only using encounter frequency, NodeRank algorithm combined the contact time and inter-contact time meanwhile to ensure integrity and availability of message delivery. The simulation results based on real-world mobility traces show the performance advantages of NodeRank in offloading efficiency and network redundant copies.

• 산경연구논집
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• 제11권12호
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• pp.7-15
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• 2020

Purpose: This study aims to explore the feasibility of expanding complex wards to provide efficient hospital management and high-quality medical services to local residents of Gangneung Medical Center (GMC). Research Design, Data and Methodology: There are four research designs to achieve the research objectives. We analyzed Big Data for 3 months on Social Network Services (SNS). A questionnaire survey conducted on 219 patients visiting the GMC. Surveys of 20 employees of the GMC applied. The feasibility to expand the GMC ward measured through Focus Group Interview by 12 internal and external experts. Data analysis methods derived from various surveys applied with data mining technique, frequency analysis, and Importance-Performance Analysis methods, and IBM SPSS statistical package program applied for data processing. Results: In the result of the big data analysis, the GMC's recognition on SNS is high. 95.9% of the residents and 100.0% of the employees required the need for the complex ward extension. In the analysis of expert opinion, in the future functions of GMC, specialized care (△3.3) and public medicine (△1.4) increased significantly. Conclusion: GMC's complex ward extension is an urgent and indispensable project to provide efficient hospital management and service quality.

• 대한가정학회지
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• 제38권7호
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• pp.27-37
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• 2000

According to recent statistics, the number of family-owned small business have increased. And these growing numbers have created an urgent need for researchers and government to analyse and plan for this population. The purposes of this study were to identify the determining factors of the location of family owned small business and to analyze the factors related to their job satisfaction, and life satisfaction. The data used for this study, were 713 self-employed men and women which were elected from the panel data of 1998 MIPS of Daewoo Economic Research Institute. Statistics performed for the analysis were frequencies, percentiles, t-test, $\varkappa$$^2$, OLS and Logistic analysis. It was found that the person who work at home-base small office were the residents of smaller city, and are older than the office-going attendants. The variable which effects the job satisfaction of the home-based workers was the educational background. And sex was the only factor which affects the job satisfaction of the office-going attendants. It was also found that job satisfaction affects the life satisfaction significantly in both group.