• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.684-693
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• 2021

Purpose: The purpose is to establish a safe facility environment from abnormal voltages such as lightning by developing a smart land resistance measuring device that can acquire real-time land resistance data using Arduino. Method: This paper studied design models and application cases by developing a land resistance acquisition and analysis system with Arduino and a power line communication (PLC) system. Some sites in the wind power generation complex in Gyeongsangnam-do were selected as test beds, and real-time land resistance data applied with new technologies were obtained. The electrode arrangement adopted a smart electrode arrangement using a combination of a Wenner four electrode arrangement and a Schlumberger electrode arrangement. Result: First, the characteristic of this technology is that the depth of smart multi-electrodes is organized differently to reduce the error range of the acquired data even in the stratigraphic structure with specificity between floors. Second, IT convergence technology was applied to enable real-time transmission and reception of information on land resistance data acquired from smart ground electrodes through the Internet of Things. Finally, it is possible to establish a regular management system and analyze big data accumulated in the server to check the trend of changes in various elements, and to model the optimal ground algorithm and ground system design for the IT convergence environment. Conclusion: This technology will reduce surge damage caused by lightning on urban infrastructure underlying the 4th industrial era and design an optimized ground system model to protect the safety and life of users. It is also expected to secure intellectual property rights of pure domestic technology to create jobs and revitalize our industry, which has been stagnant as a pandemic in the post-COVID-19 era.

• Informatization Policy
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• v.28 no.3
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• pp.73-94
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• 2021

This study aimed to analyze the minutes of the Legislation and Judiciary Committee, one of the standing committees of the Korean National Assembly, by applying the WORDFISH algorithm of automated text analysis to estimate the pattern of changes in the ideological orientation of the members of Korea's political elite. The results of the analysis showed that the Legislation and Judiciary Committee generally undergoes changes in ideological orientation around the time of a major administrative change, especially during the period preceding a change up to the time of its implementation. Compared with the United States, where changes in the ideological orientation of the political elite occur simultaneously based on parties, changes in that of the political elite at the Korean National Assembly tend to occur in response to a certain transitional point in time or a change in the ruling government. What is especially noteworthy in terms of the ideological orientation reflected in the minutes of the Legislative Judiciary Committee is that the microscopic effect tends to disappear when the macroscopic effect occurs and, conversely, that the microscopic effect emerges once the macroscopic effect has disappeared. In other words, changes in the ideological orientation of the political elite appear to indicate the effect of a particular legislator's individual characteristics when no effect is observed during a given term or year of the National Assembly, whereas they revealed the effect of a given time itself when no effects related with the individual characteristics of a legislator are discerned.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of Korean Library and Information Science Society
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• v.52 no.3
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• pp.287-311
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• 2021

It is not easy for information users to find books that are suitable for them in a knowledge information society. There is a growing need for libraries to break away from traditional services and provide user-tailored recommendation services, but there are few qualitative studies on user satisfaction so far. In this study, a user-customized book recommendation was performed by applying Apriori, a correlation analysis algorithm, and satisfaction factors were analyzed in depth through interviews. The experimental data was the loan data of 100 people who used the most frequently used loan data for 10 years from 2009 to 2019 of the S library in Seoul. The interviewees of the experiment were those who could be interviewed in depth. After the correlation analysis, the concepts and categories derived by analyzing the interview data were 59 concepts, 6 sub-categories, and 2 upper categories, respectively. The upper categories were 'reading' and 'book recommendation service'. In the 'reading' category, there were 16 concepts of motivation for reading, 8 concepts of preferred books, and 12 concepts of expected effects. Also, in the category of 'reading recommendation service', there were 10 'reflection factors', 4 'reflection methods', and 9 'satisfaction factors'.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.93-99
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• 2021

Recently, the miniaturization and digitalization for the inspection devices of point-of-care testing (POCT) are rapidly evolving. In the urine test, a lot of researches on index paper technology are being conducted because people can be self-diagnosed through visual color comparison using a urine test paper, Dipsick. The purpose of this study is to analyze the RGB values from the color changes on Dipstick Pad, which isused for urine test, using a smartphone camera. To this end, the primary, analytes in urine wasdiabetes-related parameters such as glucose, ketone body and pH, which is the most frequently tested elements, and we pursuited to quantify the changes in dipstick color caused from artificial urine containing different ranges of sugar, ketone body, and pH. In this experiment, changes in RGB values under bright and dark illuminances were compared, and changes in RGB value were monitored as a function of concentration of analytes under the ambient illumination of laboratory. As a result, color separation at the bright luminance region was good, but it did not appearat the low luminance region, and the changed profiles in RGB value under different illuminances was suggested to correct the problem of the color separation algorithm.

• Ocean and Polar Research
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• v.43 no.4
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• pp.229-243
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• 2021

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.30-42
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• 2021

The amount of compensation and the number of cases owing to car damage from pot holes on highways across the country increased by about 4.2 times and 3.5 times, respectively, in 2019 compared to 2015. Due to the increase in damage caused by these road hazards, the Ministry of Land, Infrastructure and Transport is developing technologies and services that can collect road hazard information by using devices on commercial vehicles (DTGs, black boxes, ADASs). In preparation for the development of these technologies, this study conducted an integrated assessment of algorithms developed for interrupted-flow and uninterrupted-flow traffic under three scenarios in order to provide road hazard information to drivers and road managers. As a result, the overall accuracy of the integrated assessment was derived at 81.88%. Errors generated in this integrated assessment reflect only missing data in less than 1 minute, GPS coordinate location and algorithm related errors, taking into account the purpose and assumptions of the assessment. Among them, we derive an accuracy of 90.15%overall by calibrating GPS error data. The results of this study can be used as basic data for improving the accuracy of location-based information collected by commercial vehicles and for policy development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.23-41
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• 2019

This study understand the subject domain of book which has been read by men and woman elderly by analizying the PFNET using library big data and confirm the difference between adult at age 30-40. This study extract co-occurrence matrix of book lending on the popular book list from library big data, for 4 group, men/woman elderly, men/woman adult. With these matrix, this study performs FP network analysis. And Pearson Correlation Analysis based on the Triangle Betweenness Centrality calculated on the loan book was performed to understand the correlation among the 4 clusters which has been created by PNNC algorithm. As a result, reading trend which has been focused on modern korean novel has been revealed in elderly regardless gender, among them, men elderly show extreme tendency concentrated on modern korean long series novel. In the correlation analysis, the male elderly showed a weak negative correlation with the adult male of r = -0.222, and the negative direction of all the other groups showed that the tendency of male elderly's loan book was opposite.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.