• International Journal of Highway Engineering
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• v.8 no.4 s.30
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• pp.75-85
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• 2006

Plate Bearing Test(PBT) and California Bearing Ratio Test(CBR) usually have been used to evaluate the bearing capacity of sub-layer in pavement system. However, these tests have shortcomings for which man powers and time are spent greatly. Many researchers proposed a simple Dynamic Cone Penetrometer Test(DCP) to evaluate the bearing capacity of sub-layers in pavement system. This study performed several field bearing capacity tests(DCP, PBT, CBR, FWD) to evaluate field performance of DCP on sub-base and subgrade at four test sections simultaneously. The results showed that DCPI, $M_{FWD}$, and $PBT_K_{30}$ are highly correlated, but CBR and other test are not. This study proposed the following regression models between FWD, DCP, and PBT: $$M_{FWD}=993.10\Big(\frac{1}{DCPI}\Big)+33.95\;R^2=0.77$$ $$M_{FWD}=3.7533K_{30}+23.085\;R^2=0.69$$

• Journal of Energy Engineering
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• v.28 no.4
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• pp.76-81
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• 2019

This paper is a review of the certification system required for various energy prosumer business models, including P2P energy trading and participation in small demand response programs, which are based on reliable production and consumption certification. One of the most important parameter in energy trading is ensuring the reliability of trading account balancing. Therefore, we studied to use big data pattern analysis based blockchain smart contract between trading partners to make its tradings are more reliable. For this purpose big data analysis system collected from the IoT AMI and a production authentication system using a private blockchain network linked with the AMI is discussed, using the blockchain smart contract are also suggested. Futhermore, energy trading system concept and business models are introduced.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.4
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• pp.641-649
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• 2016

Highly precision body measurements represent basic data required by industry and researches who wish to utilize information about the human body. The proficiency and expertise of the measurers have a significant influence on the error and accuracy of data when various parts from multiple subjects' bodies are measured. Therefore, in order to measure accurate body measurements (when measuring bodies directly), it is necessary to conduct objective analyses on errors. This study calculated the Relative Technical Error of Measurement (%TEM) using data that measured each of 24 subjects and discussed errors and methods to reduce errors by conducting comparison analysis based on measured items and objects. The result of analysis indicated that the errors based on age and gender of the objects of measurement were minor; however, there were comparatively distinct differences in measured errors based on measured items. 'Right and left Shoulder Angle' for all measured subjects displayed the greatest errors and standard deviations. 'Height' dimension, Lateral Malleolus Height and Head Height had big errors; in addition, 'Circumference', Neck Base Circumference and Armscye Circumference also had big errors. More careful measurements of such items with big errors require additional educational plan such as a proposal for more objective and detailed measurement methods. Items with small errors but big standard deviations such as Waist Circumference, Calf Circumference, Minimum Leg Circumference, Chest Circumference, Hip Circumference and Waist Circumference confirmed that errors for them greatly decreased with repeated experiments and resultant measurers increased proficiency; consequently, repeated measuring experiments for these items greatly enhance accuracy.

• Archives of Plastic Surgery
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• v.47 no.2
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• pp.118-125
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• 2020

Since April 2015, post-mastectomy breast reconstruction has been covered by the Korean National Health Insurance Service (NHIS). The frequency of these procedures has increased very rapidly. We analyzed data obtained from the Big Data Hub of the Health Insurance Review and Assessment Service (HIRA) and determined annual changes in the number of breast reconstruction procedures and related trends in Korea. We evaluated the numbers of mastectomy and breast reconstruction procedures performed between April 2015 and December 2018 using data from the HIRA Big Data Hub. We determined annual changes in the numbers of total, autologous, and implant breast reconstructions after NHIS coverage commenced. Data were analyzed using Microsoft Excel. The post-mastectomy breast reconstruction rate increased from 19.4% in 2015 to 53.4% in 2018. In 2015, implant reconstruction was performed in 1,366 cases and autologous reconstruction in 905 (60.1% and 39.8%, respectively); these figures increased to 3,703 and 1,570 (70.2% and 29.7%, respectively) in 2018. Free tissue transfer and deep inferior epigastric perforator flap creation were the most common autologous reconstruction procedures. For implant-based reconstructions, the rates of directto-implant and tissue-expander breast reconstructions (first stage) were similar in 2018. This study summarizes breast reconstruction trends in Korea after NHIS coverage was expanded in 2015. A significant increase over time in the post-mastectomy breast reconstruction rate was evident, with a trend toward implant-based reconstruction. Analysis of data from the HIRA Big Data Hub can be used to predict breast reconstruction trends and convey precise information to patients and physicians.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.195-205
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• 2020

Recently, after the 4th industrial revolution era has arrived, technological advances started to develop and these changes have led to widespread use of data. Big data is often used for the safety of citizens, including the administration, safety and security of the country. In order to enhance the efficiency of maintaining such security, it is necessary to understand the installation status of CCTVs. By comparing the installation rate of CCTVs and crime rate in the area, we should analyze and improve the status of CCTV installation status, and crime rate in each area in order to increase the efficiency of security. Therefore, in this paper, big data analytic system based on public data is developed to collect data related to crime rate such as CCTV, female population, entertainment center, etc. and to reduce crime rate through efficient management and installation of CCTV.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.911-923
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• 2016

Machine learning becomes increasingly important in the big data era. Logistic regression is a type of classification in machine leaning, and has been widely used in various fields, including medicine, economics, marketing, and social sciences. Rhipe that integrates R and Hadoop environment, has not been discussed by many researchers owing to the difficulty of its installation and MapReduce implementation. In this paper, we present the MapReduce implementation of Gradient Descent algorithm and Newton-Raphson algorithm for logistic regression using Rhipe. The Newton-Raphson algorithm does not require a learning rate, while Gradient Descent algorithm needs to manually pick a learning rate. We choose the learning rate by performing the mixed procedure of grid search and binary search for processing big data efficiently. In the performance study, our Newton-Raphson algorithm outpeforms Gradient Descent algorithm in all the tested data.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.695-702
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• 2020

Recently, as research on smart farms has been actively conducted, indoor environment control, such as a green house, has reached a high level. However, In the field of forestry where cultivation is carried out in outdoor, the use of ICT is still insufficient. In this paper, we propose LPWA-based forest growth environment collection and big data analysis system using ICT technology. The proposed system collects and transmits the field cultivation environment data to the server using small solar power generation and LPWA technology based on the oneM2M architecture. The transmitted data is constructed as big data on the server and utilizes it to predict the production and quality of forest products. The proposed system is expected to contribute to the production of low-cost, high-quality crops through the fusion of renewable energy and smart farms. In addition, it can be applied to other industrial fields that utilize the oneM2M architecture and monitoring the growth environment of agricultural crops in the field.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.445-455
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• 2016

A goal of the Real-time railway safety supervision system is to improve the safety oversight efficiency and to prevent accidents by integrating existing distributed monitoring systems, train, signal, power and facilities. So, the system require better performance regarding real-time processing based on big data. The disk-based database that is used in existing railway control systems has a problem with real-time processing; memory-based databases haves a limitation in terms of big-data processing; and time series databases haves a limitation in terms of real-time processing. So, we need a new database architecture for simultaneous real-time processing based on big data. In this study, we review the existing railway monitoring systems and propose a new database architecture for a real-time railway safety supervision system.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.175-183
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• 2015

In this paper, we propose a analyses the big data of students who are willing to find employment and thus presents strategy for their higher success rate of employment. The experiment covered in this paper is based on female two-year community college students who are yet unsure about their future employment. The primary flaw of pervious employment strategy was job opportunity was only based on simple factors such as student's grade, appearance, and personality due to employers and firms's demand. Therefore, students were less satisfied and often resign. In order to prevent these failures, this paper plans a strategy by analyzing the big data. Furthermore, this is proven by the comparison between 2014 employment statistics and those of previous years, and employment request has been 21.3 percent increased along with 81.4 percent increase in match rate between firms and graduating students. Most importantly, the final success rate of employment presented 63.1 percent increase compared to the previous year.

• The Research Journal of the Costume Culture
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• v.27 no.4
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• pp.398-413
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• 2019

This study empirically analyzed the influence of fashion brands' marketing issues on actual sales and consumer preference-focusing on evaluation trends of brands over time by using the theoretical background and big data provided through literature. This study examined the influence of three fashion brands (Balenciaga, Vetements, and Off-White) that have recently seen a drastic increase in the number of searched volumes through social networks. To identify the consumer-brand evaluations and trends and the marketing issues, the time period was divided into Groups A and B, which are from 2014 to 2015 and from 2016 to 2017, respectively. This study analyzed the frequency of overlapping keywords by using the R program to graphically visualize the changes over the timeline. Specifically, this analysis extracted data mainly related to bags, wallets and accessories for 2014-2015, but in 2016-2017, all four brands saw a vast increase in the frequency of searching product keywords related to clothing and footwear, and newly extracted ones were the top keywords. When analyzing the big data with these keywords as indicators, I confirmed that the products related to bags, wallets, and accessories were shifted to those related to apparel and footwear. Consumers previously recognized luxury brands such as Balenciaga as accessories-oriented brands that were focused on handbags and sunglasses, but now they are gaining popularity and recognition among consumers as a fashion brand.