• Title, Summary, Keyword: 기계학습

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Impact of Word Embedding Methods on Performance of Sentiment Analysis with Machine Learning Techniques

  • Park, Hoyeon;Kim, Kyoung-jae
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.8
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    • pp.181-188
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    • 2020
  • In this study, we propose a comparative study to confirm the impact of various word embedding techniques on the performance of sentiment analysis. Sentiment analysis is one of opinion mining techniques to identify and extract subjective information from text using natural language processing and can be used to classify the sentiment of product reviews or comments. Since sentiment can be classified as either positive or negative, it can be considered one of the general classification problems. For sentiment analysis, the text must be converted into a language that can be recognized by a computer. Therefore, text such as a word or document is transformed into a vector in natural language processing called word embedding. Various techniques, such as Bag of Words, TF-IDF, and Word2Vec are used as word embedding techniques. Until now, there have not been many studies on word embedding techniques suitable for emotional analysis. In this study, among various word embedding techniques, Bag of Words, TF-IDF, and Word2Vec are used to compare and analyze the performance of movie review sentiment analysis. The research data set for this study is the IMDB data set, which is widely used in text mining. As a result, it was found that the performance of TF-IDF and Bag of Words was superior to that of Word2Vec and TF-IDF performed better than Bag of Words, but the difference was not very significant.

Ripple Compensation of Air Bearing Stage upon Gantry Control of Yaw motion (요 모션 갠트리 제어 시 공기베어링 스테이지의 리플 보상)

  • Ahn, Dahoon;Lee, Hakjun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.11
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    • pp.554-560
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    • 2020
  • In the manufacturing process of flat panel displays, a high-precision planar motion stage is used to position a specimen. Stages of this type typically use frictionless linear motors and air bearings, and laser interferometers. Real-time dynamic correction of the yaw motion error is very important because the inevitable yaw motion error of the stage means a change in the specimen orientation. Gantry control is generally used to compensate for yaw motion errors. Flexure units that allow rotational motion are applied to the stage to apply this method to a stage using an air-bearing guide. This paper proposes a method to improve the constant speed motion performance of a H-type XY stage equipped with air bearing and flexure units. When applying the gantry control to the stage, including the flexure units, the cause of the mutual ripple generated from the linear motors is analyzed, and adaptive learning control is proposed to compensate for the mutual ripple. A simulation was performed to verify the proposed method. The speed ripple was reduced to approximately the 22 % level. The ripple reduction was verified by simulating the stage state where yaw motion error occurs.

Integrative Review on Nursing education Adopting Virtual Reality Convergence Simulation (간호교육에 적용한 가상현실 융합시뮬레이션 연구에 대한 통합적 고찰)

  • Kang, Sujeong;Kim, Chunmi;Lee, Hung Sa;Nam, Jae-Woo;Park, Myung Sook
    • Journal of Convergence for Information Technology
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    • v.10 no.1
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    • pp.60-74
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    • 2020
  • Nursing education using virtual reality simulation (VRS) has emerged as a new teaching method for improving nursing student's knowledge as well as of competency for clinical nursing skill. The purpose of this study was to analyze the effects of nursing education using VRS through an integrative analysis on quantitative and qualitative research. Through quality assessment on the total 382 studies, 17studies (12 quantitative and 5 qualitative) were finally selected. Contents of the 17 studies were reviewed and those with respect to four aspects were gathered: the condition, knowledge, and attitude for effective education using VRS, and the effects of nursing education using VRS on the practice. Readiness of the use of virtual reality device, mastsering of the platform, and interesting scenario were required condition for effective education. The effects of nursing education adopting virtual reality convergence simulation oin terms of knowledge, attitude, and practice included enhancement of the knowledge and extension of the knowledge, improvement in memorizing the process and sequence of the practice through repetitive education, and development of empathy ability and formation of rapport. Hence, adopting virtual reality to convergence simulation of nursing education can maximize the effect of the education.

Automated Construction Progress Management Using Computer Vision-based CNN Model and BIM (이미지 기반 기계 학습과 BIM을 활용한 자동화된 시공 진도 관리 - 합성곱 신경망 모델(CNN)과 실내측위기술, 4D BIM을 기반으로 -)

  • Rho, Juhee;Park, Moonseo;Lee, Hyun-Soo
    • Korean Journal of Construction Engineering and Management
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    • v.21 no.5
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    • pp.11-19
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    • 2020
  • A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.

A Study on the Awareness and Preparation of the Forth Industrial Revolution of Some Health Department College Students (일부 보건계열학과 대학생의 4차 산업혁명 인식 및 준비도 연구)

  • Cho, Hye-Eun
    • Journal of the Korea Convergence Society
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    • v.11 no.12
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    • pp.291-299
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    • 2020
  • The purpose of this study was to be used as basic data for the development of future-type curriculum in health. The awareness and preparation of the forth industrial revolution were surveyed on 280 college students in health departments preparing medical technicians. A self-written structured questionnaire was used for data collection, and the recognition of the forth industry revolution was 2.74, 3D printing (3.59) was high, and neural network machine learning(2.33) was the lowest. Students majoring in Physiotherapy (3.00) had the highest perception, and those majored in Dental engineering(2.37) had the lowest perception, and there was a difference in the degree of perception of IoT by major (p=0.024). For the forth industrial revolution, 54.5% of students are preparing, and lack of interest (42.9%) is the most difficult reason to prepare, and 50.6% of educational experience and 60.9% of VR&AR game experience have experience. In the era of the forth industrial revolution, job loss (38.7%) was high, and the required competency was creative capacity (50.6%). Therefore, it is necessary to develop a curriculum related to the fourth industrial revolution and apply teaching methods that can increase the awareness and preparation of health college students in the era of the fourth industrial revolution.

Development of Ship Valuation Model by Neural Network (신경망기법을 활용한 선박 가치평가 모델 개발)

  • Kim, Donggyun;Choi, Jung-Suk
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.1
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    • pp.13-21
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    • 2021
  • The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

Effects of Instructional Supervision Emphasizing Reflective Thinking on Teaching Science of Elementary Teacher (반성적 사고를 강조한 수업장학이 초등교사의 과학수업에 미치는 영향)

  • Kim, Young-Soon;Kim, Hyo-Nam;Sin, Ae-Kyoung
    • Journal of The Korean Association For Science Education
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    • v.31 no.8
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    • pp.1092-1109
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    • 2011
  • The purpose of this study was to analyze of the effects of instructional supervision emphasizing reflective thinking on science teaching of elementary teachers. The participants in this study were two teachers. This study was divided in former, middle, and later periods, and consisted of monitoring their own teaching, interviewing, journal writing, discussion with peer teachers and teacher training. Data included descriptions of nine science classes, nine interviews, seven journals and the journals of the researcher. Data analysis tools were the frameworks of the questions, feedback, teaching methods, elements of teaching behavior, and reflection levels. This study employed qualitative research, analysis of the frequency of data, and quoting of descriptions related to the result. The results of this study were as follows: First, teachers showed mainly technical reflection, but changed to show more practical reflection, and critical reflection in the later period of instructional supervision. Second, instructional supervision emphasizing reflective thinking on science teaching for elementary teachers meaningfully changed the question, feedback, teaching methods and teaching elements of teachers. From the results of this study, instructional supervision emphasizing reflective thinking on science teaching for elementary teachers can be considered an effective method in improving teaching elementary science, and instructional supervision used in this study made possible the higher level of reflection and appropriate teaching behavior.

Predictive Clustering-based Collaborative Filtering Technique for Performance-Stability of Recommendation System (추천 시스템의 성능 안정성을 위한 예측적 군집화 기반 협업 필터링 기법)

  • Lee, O-Joun;You, Eun-Soon
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.119-142
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    • 2015
  • With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

Response Modeling for the Marketing Promotion with Weighted Case Based Reasoning Under Imbalanced Data Distribution (불균형 데이터 환경에서 변수가중치를 적용한 사례기반추론 기반의 고객반응 예측)

  • Kim, Eunmi;Hong, Taeho
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.29-45
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    • 2015
  • Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

Analysis of Hibernating Habitat of Asiatic Black Bear(Ursus thibetanus ussuricus ) based on the Presence-Only Model using MaxEnt and Geographic Information System: A Comparative Study of Habitat for Non-Hibernating Period (MaxEnt와 GIS를 활용한 반달가슴곰 동면장소 분석: 비동면 기간 동안의 서식지 비교 연구)

  • JUNG, Dae-Ho;KAHNG, Byung-Seon;CHO, Chae-Un;KIM, Seok-Beom;KIM, Jeong-Jin
    • Journal of the Korean Association of Geographic Information Studies
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    • v.19 no.3
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    • pp.102-113
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    • 2016
  • This study analyzes the geographic information system (GIS) and machine learning models to understand the relationship between the appearance of hibernation sites and habitats in order to systematically manage the habitat of Asiatic Black Bear(Ursus thibetanus ussuricus) inhabiting Jirisan National Park, South Korea. The most important environmental factors influencing the hibernation sites was found to be the inclination(41.4%), followed by altitude(20.4%), distance from the trail(10.9%), and age group(7.7%) in the order of their contribution. A comparison between the hibernation habitat and the normal habitat of Asiatic Black Bear indicated that the average altitude of the hibernation sites was 63m, whereas the average altitude of the normal habitat was approximately 400m. The average inclination was found to be $7^{\circ}$, and a preference for the steeper inclination of $12-43^{\circ}$ was also observed. The average distance of the hibernation site from the road was approximately 300m; the range of separation distance was found to be 1,300-2,400m. This was thought to be the result of a safer selection of winter hibernation site by preventing human contact and outside invasion. This study analyzes the habitat environmental factors for the selection of hibernation sites that prevent severe cold and other threats during the hibernation period in order to provide fundamental data for hibernation ecology and habitat management of Asiatic Black Bear.