• Title/Summary/Keyword: 연구데이터

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Analysis and Prediction of Trends for Future Education Reform Centering on the Keyword Extraction from the Research for the Last Two Decades (미래교육 혁신을 위한 트렌드 분석과 예측: 20년간의 문헌 연구 데이터를 기반으로 한 키워드 추출 분석을 중심으로)

  • Jho, Hunkoog
    • Journal of Science Education
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    • v.45 no.2
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    • pp.156-171
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    • 2021
  • This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

Development of Eco-friendly Combustion Process for Waste 2,4,6-trinitrotoluene (폐 2,4,6-trinitrotoluene의 환경 친화적 연소처리공정 개발)

  • Kim, Tae Ho;An, Il Ho;Kim, Jong Min
    • Clean Technology
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    • v.27 no.3
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    • pp.247-254
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    • 2021
  • In this study, an eco-friendly combustion process of waste 2,4,6-trinitrotoluene (TNT: 2,4,6-trinitrotoluene) was developed, and fundamental data for the quantity of the organic matter in the final combustion residues is presented. Because complete combustion of TNT is not possible theoretically, the combustion process was optimized to reduce organic matter content in the combustion residue by performing measures such as heating time changes, addition of propellant material, and after treatment using a high-temp electrical furnace. From the results, it was confirmed that the organic matter content in the residue could be decreased to 7 ~ 10% with each method. The quantity of the organic matter could be minimized by optimizing the combustion conditions of the process. With only a combustion time increase, the amount of organic matter in the combustion residues was measured at about 9 wt%. The environmental friendliness of the final exhaust gas was also confirmed by real time gas component analyses. In addition, the organic contents could be reduced by a further 2 wt% by applying an additional heat treatment using an external electric furnace after the first incineration treatment. In the combustion process of propellant added waste TNT, it was found that various TNT wastes could be treated using the same eco-friendly protocols because the organic content in the residue decreased in accordance with the amount of propellant. The amount of the organic matter content produced by all these methods fulfilled the requirements under the Waste Management Act.

Analysis of Public Library Statistics in Korea: Focusing on the Overview and Facility of Public Libraries (한국의 공공도서관 통계에 대한 분석 연구: 도서관 기본 정보 및 시설 현황을 중심으로)

  • Lee, Yong-Gu;Kim, Seon-A
    • Journal of the Korean Society for information Management
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    • v.38 no.3
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    • pp.335-356
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    • 2021
  • This study analyzed the trends and current status in public libraries in Korea for 14 years, focusing on the overview and facilities of public libraries using public library statistics from 2007 to 2020. The research data were collected from the National Library Statistics System and compared with the population and GDP per capita of Korea. As a result, the number of public libraries in Korea is 1,172 as of 2020, which has nearly doubled compared to 2007, and it was found that this increase has a very strong correlation with GDP per capita and population. There were 229 contracting out public libraries operating by the local government in 2020, and the contracting out rate increased from 18.8% in 2007 to 25.1%. Although the site area of public libraries varies greatly from library to library, it tends to get larger over time. The total floor area of public libraries operating by the Office of Education is larger than that of public libraries operating by the local government, and it gradually increases over time, but the total floor area of public libraries operating by the local government have decreased slightly. The total number of seats in the library is decreasing in all libraries, and the reading seats for children and the reading seats for the elderly and the disabled tend to slightly increase.

A Time Series Analysis of Urban Park Behavior Using Big Data (빅데이터를 활용한 도시공원 이용행태 특성의 시계열 분석)

  • Woo, Kyung-Sook;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.48 no.1
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    • pp.35-45
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    • 2020
  • This study focused on the park as a space to support the behavior of urban citizens in modern society. Modern city parks are not spaces that play a specific role but are used by many people, so their function and meaning may change depending on the user's behavior. In addition, current online data may determine the selection of parks to visit or the usage of parks. Therefore, this study analyzed the change of behavior in Yeouido Park, Yeouido Hangang Park, and Yangjae Citizen's Forest from 2000 to 2018 by utilizing a time series analysis. The analysis method used Big Data techniques such as text mining and social network analysis. The summary of the study is as follows. The usage behavior of Yeouido Park has changed over time to "Ride" (Dynamic Behavior) for the first period (I), "Take" (Information Communication Service Behavior) for the second period (II), "See" (Communicative Behavior) for the third period (III), and "Eat" (Energy Source Behavior) for the fourth period (IV). In the case of Yangjae Citizens' Forest, the usage behavior has changed over time to "Walk" (Dynamic Behavior) for the first, second, and third periods (I), (II), (III) and "Play" (Dynamic Behavior) for the fourth period (IV). Looking at the factors affecting behavior, Yeouido Park was had various factors related to sports, leisure, culture, art, and spare time compared to Yangjae Citizens' Forest. The differences in Yangjae Citizens' Forest that affected its main usage behavior were various elements of natural resources. Second, the behavior of the target areas was found to be focused on certain main behaviors over time and played a role in selecting or limiting future behaviors. These results indicate that the space and facilities of the target areas had not been utilized evenly, as various behaviors have not occurred, however, a certain main behavior has appeared in the target areas. This study has great significance in that it analyzes the usage of urban parks using Big Data techniques, and determined that urban parks are transformed into play spaces where consumption progressed beyond the role of rest and walking. The behavior occurring in modern urban parks is changing in quantity and content. Therefore, through various types of discussions based on the results of the behavior collected through Big Data, we can better understand how citizens are using city parks. This study found that the behavior associated with static behavior in both parks had a great impact on other behaviors.

Strength Evaluation of Sin91e-Radius Total Knee Replacement (TKR) (인공무릎관절의 단축법위 회전시 근력정가)

  • Wan, Jin-Young;Sub, Kwak-Yi
    • Journal of Life Science
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    • v.14 no.3
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    • pp.484-489
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    • 2004
  • Artificial joint replacement is one of the major surgical advances of the 21th century. The primary purpose of a TKA (Total Knee Arthroplasty) is to restore normal knee Auction. Therefore, ideally, a TKA should: (a) maintain the natural leverage of the knee joint muscles to ensure generating adequate knee muscle moments to accomplish daily tasks such as rising from a chair or climbing stairs;(b) allow the same range of motion as an complete knee; and (c) provide adequate knee joint stability. Four individuals (2 peoples after surgery one year and 2 peoples after surgery three years) participated in this study. All they were prescreened for health and functional status by the same surgeon who performed the operations. Two days of accommodation practice occurred prior to the actual strength testing. The isometric strength (KIN-COM III) of the quadriceps and hamstring were measured at 60$^\circ$ and 30$^\circ$ of knee flexion, respectively. During isokinetic concentric testing, the range of motion was between 10$^\circ$ to 80$^\circ$ of knee flexion (stand-to-sit) and extension (sit-to-stand). for a given test, the trial exhibiting maximum torque was analyzed. A 16-channel MYOPACTM EMG system (Run Technologies, Inc.) was used to collect the differential input surface electromyographic (EMG) signals of the vastus medialis (VM), vastus lateralis(VL), rectus femoris (RF) during sit-to-stand and stand-to-sit tests. Disposable electrodes (Blue SensorTM, Medicotest, Inc.) were used to collect the EMG signals. The results were as follows; 1. Less maximum concentric (16% and 21% less for 1 yew man and 3 years mm, respectively) and isometric (12% and 29%, respectively) quadriceps torque for both participants. 2.14% less maximum hamstrings concentric torque for 1 year man but 16% greater torque for 3 years mm. However, 1 year man had similar hamstring isometric peak torque for both knees. 3. Less quadriceps co-contraction by 1 year man except for the VM at 10$^\circ$-20$^\circ$ and 30$^\circ$-50$^\circ$ range of knee flexion.

Prediction of field failure rate using data mining in the Automotive semiconductor (데이터 마이닝 기법을 이용한 차량용 반도체의 불량률 예측 연구)

  • Yun, Gyungsik;Jung, Hee-Won;Park, Seungbum
    • Journal of Technology Innovation
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    • v.26 no.3
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    • pp.37-68
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    • 2018
  • Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.

The Factors Affecting the Population Outflow from Busan to the Seoul Metropolitan Area (지역별 수도권으로의 인구유출에 영향을 미치는 요인 연구: 부산시 사례를 중심으로)

  • LIM, Jaebin;Jeong, Kiseong
    • LHI Journal of Land, Housing, and Urban Affairs
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    • v.12 no.2
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    • pp.47-59
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    • 2021
  • This study aims to review the trends of the population outflows in the metropolitan area of Busan and to investigate the factors that affect population out-migration to the Seoul metropolitan area. The following variables are considered for analysis: traditional population movement variables and quality of life variables, such as population, society, employment, housing, culture, safety, medical care, greenery, education, and childcare. The 'domestic population movement data', provided by the MDIS of the National Statistical Office, was used for this research. Out of the total of 57 million population movement data in the period 2012 - 2017, population outmigration from Busan to the Seoul metropolitan area was extracted. Independent variables were drawn from public data sources in accordance with the temporal and spatial settings of the study. The multiple linear regression model was specified based on the dataset, and the fit of the model was measured by the p-value, and the values of Adjusted R2, Durbin-Watson analysis, and F-statistics. The results of the analysis showed that the variables that have a significant effect on population movement from Busan to the Seoul metropolitan area were as follows: 'single-person households', 'the elderly population', 'the total birth rate', 'the number of companies', 'the number of employees', 'the housing sales price index', 'cultural facilities', and 'the number of students per teacher'. More positive (+) influences of the population out-movement were observed in areas with higher numbers of single-person households, lowers proportions of the elderly, lower numbers of businesses, higher numbers of employees, higher numbers of housing sales, lower numbers of cultural facilities, and lower numbers of students. The findings suggest that policies should enhance the environments such as quality jobs, culture, and welfare that can retain young people within Busan. Improvements in the quality of life and job creation are critical factors that can mitigate the outflows of the Busan residents to the Seoul metropolitan area.

Research Trend and Futuristic Guideline of Platform-Based Business in Korea (플랫폼 기반 비즈니스에 대한 국내 연구동향 및 미래를 위한 가이드라인)

  • Namn, Su Hyeon
    • Management & Information Systems Review
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    • v.39 no.1
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    • pp.93-114
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    • 2020
  • Platform is considered as an alternative strategy to the traditional linear pipeline based business. Moreover, in the 4th industrial revolution period, efficiency driven pipeline business model needs to be changed to platform business. We have such success stories about platform as Apple, Google, Amazon, Uber, and so on. However, for those smaller corporations, it is not easy to find out the transformation strategy. The essence of platform business is to leverage network effect in management. Thus platform based management can be rephrased as network management across the business functions. Research on platform business is popular and related to diverse facets. But few scholars cover what the research trend of the domain is. The main purpose of this paper is to identify the research trend on platform business in Korea. To do that we first propose the analytical model for platform architecture whose components are consumers, suppliers, artifacts, and IT platform system. We conjecture that mapping of the research work on platform to the components of the model will make us understand the hidden domain of platform research. We propose three hypotheses regarding the characteristics of research and one proposition for the transitional path from pipeline to platform business model. The mapping is based on the research articles filtered from the Korea Citation Index, using keyword search. Research papers are searched through the keywords provided by authors using the word of "platform". The filtered articles are summarized in terms of the attributes such as major component of platform considered, platform type, main purpose of the research, and research method. Using the filtered data, we test the hypotheses in exploratory ways. The contribution of our research is as follows: First, based on the findings, scholars can find the areas of research on the domain: areas where research has been matured and territory where future research is actively sought. Second, the proposition provided can give business practitioners the guideline for changing their strategy from pipeline to platform oriented. This research needs to be considered as exploratory not inferential since subjective judgments are involved in data collection, classification, and interpretation of research articles.

A Case Study on Forecasting Inbound Calls of Motor Insurance Company Using Interactive Data Mining Technique (대화식 데이터 마이닝 기법을 활용한 자동차 보험사의 인입 콜량 예측 사례)

  • Baek, Woong;Kim, Nam-Gyu
    • Journal of Intelligence and Information Systems
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    • v.16 no.3
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    • pp.99-120
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    • 2010
  • Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

Sentiment analysis on movie review through building modified sentiment dictionary by movie genre (영역별 맞춤형 감성사전 구축을 통한 영화리뷰 감성분석)

  • Lee, Sang Hoon;Cui, Jing;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.22 no.2
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    • pp.97-113
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    • 2016
  • Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.