• Title/Summary/Keyword: 지속적학습

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A fundamental study on the automation of tunnel blasting design using a machine learning model (머신러닝을 이용한 터널발파설계 자동화를 위한 기초연구)

  • Kim, Yangkyun;Lee, Je-Kyum;Lee, Sean Seungwon
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.24 no.5
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    • pp.431-449
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    • 2022
  • As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

An Artificial Intelligence Approach to Waterbody Detection of the Agricultural Reservoirs in South Korea Using Sentinel-1 SAR Images (Sentinel-1 SAR 영상과 AI 기법을 이용한 국내 중소규모 농업저수지의 수표면적 산출)

  • Choi, Soyeon;Youn, Youjeong;Kang, Jonggu;Park, Ganghyun;Kim, Geunah;Lee, Seulchan;Choi, Minha;Jeong, Hagyu;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_3
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    • pp.925-938
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    • 2022
  • Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

Future Prospects of Forest Type Change Determined from National Forest Inventory Time-series Data (시계열 국가산림자원조사 자료를 이용한 전국 산림의 임상 변화 특성 분석과 미래 전망)

  • Eun-Sook, Kim;Byung-Heon, Jung;Jae-Soo, Bae;Jong-Hwan, Lim
    • Journal of Korean Society of Forest Science
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    • v.111 no.4
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    • pp.461-472
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    • 2022
  • Natural and anthropogenic factors cause forest types to continuously change. Since the ratio of forest area by forest type is important information for identifying the characteristics of national forest resources, an accurate understanding of the prospect of forest type change is required. The study aim was to use National Forest Inventory (NFI) time-series data to understand the characteristics of forest type change and to estimate future prospects of nationwide forest type change. We used forest type change information from the fifth and seventh NFI datasets, climate, topography, forest stand, and disturbance variables related to forest type change to analyze trends and characteristics of forest type change. The results showed that the forests in Korea are changing in the direction of decreasing coniferous forests and increasing mixed and broadleaf forests. The forest sites that were changing from coniferous to mixed forests or from mixed to broadleaf forests were mainly located in wet topographic environments and climatic conditions. The forest type changes occurred more frequently in sites with high disturbance potential (high temperature, young or sparse forest stands, and non-forest areas). We used a climate change scenario (RCP 8.5) to establish a forest type change model (SVM) to predict future changes. During the 40-year period from 2015 to 2055, the SVM predicted that coniferous forests will decrease from 38.1% to 28.5%, broadleaf forests will increase from 34.2% to 38.8%, and mixed forests will increase from 27.7% to 32.7%. These results can be used as basic data for establishing future forest management strategies.

An Analysis on the Curricula and Recognitions of the Home Economics Teachers who were the Participants of the First-Grade Home Economics Regular Teacher Qualification Program (중등 가정과 1급 정교사 자격 연수 프로그램 운영 실태 분석 및 연수 참여자의 인식)

  • Lim, Il-Young;Kweon, Li-Ra;Lee, Hye-Suk;Park, Mi-Jin;Ryu, Sang-Hee
    • Journal of Korean Home Economics Education Association
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    • v.19 no.4
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    • pp.37-56
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    • 2007
  • The purpose of this study is to provide basic resources to the first-grade Home Economics Regular Teacher Qualification Program (FGHERTQP) in order to improve its operation plans. For the study, the three methods were carried out: an analysis on the curricula of FGHERTQP over six years since 2000, a questionnaire asking their satisfaction degrees and needs on the programs which was answered by the home economics teachers who were the participants of FGHERTQP, and several statistical analyses such as a descriptive-test, a $X^2$-test, a t-test, and one way ANOVA by using SPSS Win ver 10.0. The results of the study were as follows; Firstly, FGHERTQP has been operated ten times by five training centers during resent six years. Subject matters ($1{\sim}7$), whole numbers of lectures ($11{\sim}29$), and their allotted working hours ($111{\sim}136$) vary with individual training centers and operation years. Secondly, when using 5 point likert scales, Contents and Methods of evaluation marked 3.08 which were the lowest scores, and Qualification Training in General marked 3.72 which was the highest score among five fields of Qualification Training in General, Contents, Organizations, Methods and Evaluation. The overall scores were low. Thirdly, in needs analysis on offering subject matters, the participants wanted to study the field of home economics education more than that of subject contents. Looking about the highest needs classified by domains, Food Principles & Meal Management showed the highest in Foods. And Consumer Issues in Clothing & Textiles in Textiles, Upcoming Housing Cultures in Housing, Family Relationship in Child Development & Family Relationship, Juveniles and their daily life as a consumer in Family & Consumer Resources Management. Fourthly, training centers' lectures available had a significant influence on the satisfaction degrees according to general characteristic variations of the participants. That is, as a training center offers more lectures in the field of subject education than those of subject contents, the participants showed higher satisfaction degrees (p<.05).

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The Effect of the Quality of Education Service on the Performance of Education Service through Relationship Commitment in Franchise Beauty Academy: Moderating Effect of Trust Level (프랜차이즈 뷰티 아카데미의 교육서비스 품질이 관계 몰입을 통한 교육 서비스 성과에 미치는 영향 연구: 신뢰 수준의 조절효과)

  • Kim, Chang-Bong;Kim, Hee-Su
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.16 no.3
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    • pp.193-211
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    • 2021
  • Recently, interest in Korean Wave craze and K-beauty, led by K-pop, is increasing. In addition, the popularity and influence of the domestic beauty service industry has increased, and the economic and cultural ripple effects have been continuously expanding. The need to professional manpower training in response to the demand for manpower due to the growing development of domestic beauty services is emphasized, and the number of trainees who are actual consumers of beauty academy is increasing. Therefore, the purpose of our study is to examine the importance of quality factors of educational services to achieve educational purposes in the educational services provided by the Beauty Academy and the relationship between relationship commitment and educational service performance. Furthermore, it is to draw the importance of administrative support services, educational programs as well as educational service provision activities. However, the research for professional manpower training according to the provision of beauty services is insufficient compared to the development speed of the beauty industry. Therefore, at the present time when beauty service education is emphasized, our study will examine the relationship between relationship commitment and educational service performance based on the quality of education service by the students of domestic beauty academy. The measurement variables set for our study are program, instructor quality, tuition, external service, service fairness, relationship commitment, trust level, and educational service performance. The variables were analyzed and derived through the survey, and the following contents were derived from the empirical analysis. First, the quality of education service provided by the beauty academy, such as program, external service, service fairness, relationship commitment and trust level, had a significant effect on relationship commitment. Educational services provided by the institute, such as the systematicity and diversity of educational programs, enabled students to have a uniform relationship commitment. The quality of education service itself is to learn the expertise necessary for providing beauty service from the standpoint of the students and play an organic role in the relationship with the institute. Second, the moderating effect of trust level between academies and students was significant in the quality of education service and the relationship commitment. This means that students will feel higher level of service quality through the practical trust relationship of the students about the educational services provided by the institute. Based on the results of the empirical analysis, the implications of our study are to find ways to improve the students' ability and satisfaction represented by the results of educational services. This is because the quality of education services provided by the institute called Beauty Academy will have a great impact on the career choice of educational facilities and students. The characteristics of consistency, convenience, and knowledge orientation of education itself should be considered comprehensively, and a strong market position should be established through image formation through external service factors, which are external environments of academies.Furthermore, in terms of presenting differentiated strategies with competitors, the educational service quality factors play a significant role in the commitment to the relationship with the students, so the role of relationship marketing will be important for the psychological stability experienced by the students by grasping the demand accompanying the behavior of the students in advance.

Research Trend Analysis of Publications in the Journal of Home Economics Education Association Using Network Text Analysis (네트워크 텍스트 분석을 이용한 한국가정과교육학회지 논문의 연구 동향 분석)

  • Lee, Yoon-Jung;Kim, Eun Jeung;Kim, Ji sun
    • Journal of Korean Home Economics Education Association
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    • v.31 no.4
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    • pp.1-18
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    • 2019
  • The purpose of this study was to analyze the research trend in home economics education using network text analysis method. The 586 research articles published in the Journal of Home Economics Education Association between July, 2003 and December 2018 were examined using Neckinger 4, a social network analysis software. The frequency and centrality measures(degree centrality, closeness centrality, and betweenness centrality) were calculated for the words appeared throughout the whole period, and the centrality analysis and LAD(Latent Dirichlet Allocation) were conducted for the four sub-periods. The results are as follows: first, the most frequently appeared words are parents, culture, unit, health, career, consumption, practicality, etc. The words such as parents and management scored high in degree centrality; parents and male students in closeness centrality; and male students and units in betweenness centrality. Second, when divided into four periods, the words such as education, family, purpose, class, middle school, and school appeared most frequently across the periods; but some words such as 'purpose' (in period 3 and 4), or 'process' (in period 4) were salient only in certain periods. Third, the words with high centrality were consistent regardless of the types of centrality within each period. Fourth, the topic analysis using LAD showed that curriculum, textbook, family healthiness, teaching-learning, evaluation, dietary life, appearance management, and consumption were the topics consistently appeared across all periods. The topics have become diversified and deepened. New topics such as teacher training and safety appeared in later periods, possibly due to the curriculum and national policy changes, and housing as a less represented topic is suggested as an area that needs further research attention. This study has implication in that it allows researchers to identify the major research interests and the trends in research by researchers in home economic education.

Estimation of Ground-level PM10 and PM2.5 Concentrations Using Boosting-based Machine Learning from Satellite and Numerical Weather Prediction Data (부스팅 기반 기계학습기법을 이용한 지상 미세먼지 농도 산출)

  • Park, Seohui;Kim, Miae;Im, Jungho
    • Korean Journal of Remote Sensing
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    • v.37 no.2
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    • pp.321-335
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    • 2021
  • Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

The development of resources for the application of 2020 Dietary Reference Intakes for Koreans (2020 한국인 영양소 섭취기준 활용 자료 개발)

  • Hwang, Ji-Yun;Kim, Yangha;Lee, Haeng Shin;Park, EunJu;Kim, Jeongseon;Shin, Sangah;Kim, Ki Nam;Bae, Yun Jung;Kim, Kirang;Woo, Taejung;Yoon, Mi Ock;Lee, Myoungsook
    • Journal of Nutrition and Health
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    • v.55 no.1
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    • pp.21-35
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    • 2022
  • The recommended meal composition allows the general people to organize meals using the number of intakes of foods from each of six food groups (grains, meat·fish·eggs·beans, vegetables, fruits, milk·dairy products and oils·sugars) to meet Dietary Reference Intakes for Koreans (KDRIs) without calculating complex nutritional values. Through an integrated analysis of data from the 6th to 7th Korean National Health and Nutrition Examination Surveys (2013-2018), representative foods for each food group were selected, and the amounts of representative foods per person were derived based on energy. Based on the EER by age and gender from the KDRIs, a total of 12 kinds of diets were suggested by differentiating meal compositions by age (aged 1-2, 3-5, 6-11, 12-18, 19-64, 65-74 and ≥ 75 years) and gender. The 2020 Food Balance Wheel included the 6th food group of oils and sugars to raise public awareness and avoid confusion in the practical utilization of the model by industries or individuals in reducing the consistent increasing intakes of oils and sugars. To promote the everyday use of the Food Balance Wheel and recommended meal compositions among the general public, the poster of the Food Balance Wheel was created in five languages (Korean, English, Japanese, Vietnamese and Chinese) along with card news. A survey was conducted to provide a basis for categorizing nutritional problems by life cycles and developing customized web-based messages to the public. Based on survey results two types of card news were produced for the general public and youth. Additionally, the educational program was developed through a series of processes, such as prioritization of educational topics, setting educational goals for each stage, creation of a detailed educational system chart and teaching-learning plans for the development of educational materials and media.

The Pattern Analysis of Financial Distress for Non-audited Firms using Data Mining (데이터마이닝 기법을 활용한 비외감기업의 부실화 유형 분석)

  • Lee, Su Hyun;Park, Jung Min;Lee, Hyoung Yong
    • Journal of Intelligence and Information Systems
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    • v.21 no.4
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    • pp.111-131
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    • 2015
  • There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

Basic Research on the Possibility of Developing a Landscape Perceptual Response Prediction Model Using Artificial Intelligence - Focusing on Machine Learning Techniques - (인공지능을 활용한 경관 지각반응 예측모델 개발 가능성 기초연구 - 머신러닝 기법을 중심으로 -)

  • Kim, Jin-Pyo;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.51 no.3
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    • pp.70-82
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    • 2023
  • The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.