• Journal of agricultural medicine and community health
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• v.46 no.2
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• pp.78-88
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• 2021

Objectives: This study aimed to investigate health risk awareness pertaining to fine dust exposure and the use of face masks in farmers, as well as their attitude toward education regarding fine dust-related respiratory disease prevention. Methods: In total, 295 farmers were interviewed in a survey using a structured questionnaire to obtain data on general characteristics, farming-related characteristics, health risk awareness pertaining to fine dust exposure, attitude toward education on fine dust-related respiratory disease prevention and the use of face masks. This study was analyzed the correlation between the perceived susceptibility to fine dust exposure and willingness to participate in education on fine dust-related respiratory disease prevention. Results: The mean score for perceived susceptibility to fine dust exposure was 3.8 (out of 5), and the participants were highly willing to receive education on fine dust-related respiratory disease prevention. In Multiple response analysis of reactions to exposure to fine dust generated during work, 221 participants responded that they practiced at least one preventive action; participants gave a positive response to "wearing masks" (56.1%), "personal hygiene, such as hand washing." (52.9%). In terms of education methods, 94 (33.6%) participants preferred to learn online or via text messages. Conclusions: The significant correlation between the perceived susceptibility to fine dust exposure and willingness to participate in education on fine dust-related respiratory disease prevention shows the importance of promoting education on prevention. The results of this study can help understand as reference for education on fine dust-related respiratory disease prevention.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.103-119
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• 2023

Since the Korean government has decided to apply the policy of BIM (Building Information Modeling) to the entire construction industry, it has experienced a positive trend in adoption and utilization. BIM can reduce workloads by building model objects into libraries that conform to standards and enable consistent quality, data integrity, and compatibility. In the domestic architecture, civil engineering, and the overseas landscape architecture sectors, many BIM library standardization studies have been conducted, and guidelines have been established based on them. Currently, basic research and attempts to introduce BIM are being made in Korean landscape architecture field, but the diffusion has been delayed due to difficulties in application. This can be addressed by enhancing the efficiency of BIM work using standardized libraries. Therefore, this study aims to provide a starting point for discussions and present a classification system for objects and attribute information that can be referred to when creating landscape libraries in practice. The standardization of landscape BIM library was explored from two directions: object classification and attribute information items. First, the Korean construction information classification system, product inventory classification system, landscape design and construction standards, and BIM object classification of the NLA (Norwegian Association of Landscape Architects) were referred to classify landscape objects. As a result, the objects were divided into 12 subcategories, including 'trees', 'shrubs', 'ground cover and others', 'outdoor installation', 'outdoor lighting facility', 'stairs and ramp', 'outdoor wall', 'outdoor structure', 'pavement', 'curb', 'irrigation', and 'drainage' under five major categories: 'landscape plant', 'landscape facility', 'landscape structure', 'landscape pavement', and 'irrigation and drainage'. Next, the attribute information for the objects was extracted and structured. To do this, the common attribute information items of the KBIMS (Korean BIM Standard) were included, and the object attribute information items that vary according to the type of objects were included by referring to the PDT (Product Data Template) of the LI (UK Landscape Institute). As a result, the common attributes included information on 'identification', 'distribution', 'classification', and 'manufacture and supply' information, while the object attributes included information on 'naming', 'specifications', 'installation or construction', 'performance', 'sustainability', and 'operations and maintenance'. The significance of this study lies in establishing the foundation for the introduction of landscape BIM through the standardization of library objects, which will enhance the efficiency of modeling tasks and improve the data consistency of BIM models across various disciplines in the construction industry.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.441-447
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• 2023

Lead(Pb), which is currently mainly used for shielding purposes in the medical radiation, has excellent radiation shielding functions, but is continuously exposed to radiation directly or indirectly due to the harmfulness of lead itself to the human body and the inconvenience caused by its heavy weight. Research on shielding materials that are human-friendly, lightweight, and convenient to use that can block risks and replace lead is continuously being conducted. In this study, based on the commonly used polyethylene terephthalate (PET) film and the fabric material used in actual radiation protective clothing, a multi-layer thin film was realized through sputtering and vacuum deposition of bismuth, tungsten, and tin, which are metal materials that can shield radiation. Thus, a shielding film was produced and its applicability as a radiation shielding material was evaluated. The radiation shielding film was manufactured by establishing the optimized conditions for each shielding material while controlling the applied voltage, roll driving speed, and gas supply amount to manufacture the shielding film. The adhesion between the parent material and the shielding metal thin film was confirmed by Cross-cut 100/100, and the stability of the thin film was confirmed through a hot water test for 1 hour to measure the change of the thin film over time. The shielding performance of the finally realized shielding film was measured by the Korea association for radiation application (KARA), and the test conditions (inverse wide beam, tube voltage 50 kV, half layer 1.828 mmAl) were set to obtain an attenuation ratio of 16.4 (initial value 0.300 mGy/s, measured value 0.018 mGy/s) and damping ratio 4.31 (initial value 0.300 mGy/s, measured value 0.069 mGy/s) were obtained. by securing process efficiency for future commercialization, light and shielding films and fabrics were used to lay the foundation for the application of films to radiation protective clothing or construction materials with shielding functions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6C
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• pp.395-406
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• 2006

Drilled shafts are a common foundation solution for large concentrated loads. Such piles are generally constructed by drilling through softer soils into rock and the section of the shaft which is drilled through rock contributes most of the load bearing capacity. Drilled shafts derive their bearing capacity from both shaft and base resistance components. The length and diameter of the rock socket must be sufficient to carry the loads imposed on the pile safely without excessive settlements. The base resistance component can contribute significantly to the ultimate capacity of the pile. However, the shaft resistance is typically mobilized at considerably smaller pile movements than that of the base. In addition, the base response can be adversely affected by any debris that is left in the bottom of the socket. The reliability of base response therefore depends on the use of a construction and inspection technique which leaves the socket free of debris. This may be difficult and costly to achieve, particularly in deep sockets, which are often drilled under water or drilling slurry. As a consequence of these factors, shaft resistance generally dominates pile performance at working loads. The efforts to improve the prediction of drilled shaft performance are therefore primarily concerned with the complex mechanisms of shaft resistance development. The shaft resistance only is concerned in this study. The nature of the interface between the concrete pile shaft and the surrounding rock is critically important to the performance of the pile, and is heavily influenced by the construction practices. In this study, the influences of asperity characteristics such as the heights and angles, the strength characteristics and elastic constants of surrounding rock masses and the depth and length of rock socket, et. al. on the shaft resistance of drilled shafts are investigated from elasto-plastic analyses( FLAC). Through the parametric studies, among the parameters, the vertical stress on the top layer of socket, the height of asperity and cohesion and poison's ratio of rock masses are major influence factors on the unit peak shaft resistance.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Explosives and Blasting
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• v.9 no.1
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• pp.3-13
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• 1991

The cautious blasting works had been used with emulsion explosion electric M/S delay caps. Drill depth was from 3m to 6m with Crawler Drill ${\phi}70mm$ on the calcalious sand stone (soft -modelate -semi hard Rock). The total numbers of test blast were 88. Scale distance were induced 15.52-60.32. It was applied to propagation Law in blasting vibration as follows. Propagtion Law in Blasting Vibration $V=K(\frac{D}{W^b})^n$ were V : Peak partical velocity(cm/sec) D : Distance between explosion and recording sites(m) W : Maximum charge per delay-period of eight milliseconds or more (kg) K : Ground transmission constant, empirically determind on the Rocks, Explosive and drilling pattern ets. b : Charge exponents n : Reduced exponents where the quantity $\frac{D}{W^b}$ is known as the scale distance. Above equation is worked by the U.S Bureau of Mines to determine peak particle velocity. The propagation Law can be catagorized in three groups. Cubic root Scaling charge per delay Square root Scaling of charge per delay Site-specific Scaling of charge Per delay Plots of peak particle velocity versus distoance were made on log-log coordinates. The data are grouped by test and P.P.V. The linear grouping of the data permits their representation by an equation of the form ; $V=K(\frac{D}{W^{\frac{1}{3}})^{-n}$ The value of K(41 or 124) and n(1.41 or 1.66) were determined for each set of data by the method of least squores. Statistical tests showed that a common slope, n, could be used for all data of a given components. Charge and reduction exponents carried out by multiple regressional analysis. It's divided into under loom over loom distance because the frequency is verified by the distance from blast site. Empirical equation of cautious blasting vibration is as follows. Over 30m ------- under l00m ${\cdots\cdots\cdots}{\;}41(D/sqrt[2]{W})^{-1.41}{\;}{\cdots\cdots\cdots\cdots\cdots}{\;}A$ Over 100m ${\cdots\cdots\cdots\cdots\cdots}{\;}121(D/sqrt[3]{W})^{-1.66}{\;}{\cdots\cdots\cdots\cdots\cdots}{\;}B$ where ; V is peak particle velocity In cm / sec D is distance in m and W, maximLlm charge weight per day in kg K value on the above equation has to be more specified for further understaring about the effect of explosives, Rock strength. And Drilling pattern on the vibration levels, it is necessary to carry out more tests.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.