• Title/Summary/Keyword: 인터넷 기반 학습

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창의성과 비판적 사고

  • 김영정
    • Korean Journal of Cognitive Science
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    • v.13 no.4
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    • pp.81-90
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    • 2002
  • The main thesis of this article is that the decisive point of creativity education is the cultivation of critical thinking capability. Although the narrow conception of creativity as divergent thinking is not subsumed under that of critical thinking, the role of divergent thinking is not so crucial in the science context of creative problem-solving. On the contrary, the broad conception of creativity as focusing on the reference to utility and the third conception of creativity as a process based on the variation and combination of existing pieces of information are crucial in creative problem-solving context, which are yet subsumed under that of critical thinking. The emphasis on critical thinking education is connected with the characteristics of contemporary knowledge-based society. This rapidly changing society requires situation-adaptive or situation-sensitive cognitive ability, whose core is critical thinking capability. Hence, the education of critical thinking is to be centered on the learning of blowing-how and procedural knowledge but not of knowing-that and declarative knowledge. Accordingly, the learning of critical thinking is to be headed towards the cultivation of competence but not just of performance. In conclusion, when a rational problem-solving through critical and logical thinking turns out consequently to be novel, we call it creative thinking. So, creativity is an emergent property based on critical and logical thinking.

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A Comparative Study on the Effective Deep Learning for Fingerprint Recognition with Scar and Wrinkle (상처와 주름이 있는 지문 판별에 효율적인 심층 학습 비교연구)

  • Kim, JunSeob;Rim, BeanBonyka;Sung, Nak-Jun;Hong, Min
    • Journal of Internet Computing and Services
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    • v.21 no.4
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    • pp.17-23
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    • 2020
  • Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

A Study on Analyzing Sentiments on Movie Reviews by Multi-Level Sentiment Classifier (영화 리뷰 감성분석을 위한 텍스트 마이닝 기반 감성 분류기 구축)

  • Kim, Yuyoung;Song, Min
    • Journal of Intelligence and Information Systems
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    • v.22 no.3
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    • pp.71-89
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    • 2016
  • Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

A Study on the Development Strategy of Smart Learning for Public Education (스마트러닝의 공교육 정착을 위한 성공전략 연구)

  • Kim, Taisiya;Cho, Ji Yeon;Lee, Bong Gyou
    • Journal of Internet Computing and Services
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    • v.16 no.6
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    • pp.123-131
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    • 2015
  • Recently the development of ICT has a big impact on education field, and diffusion of smart devices has brought new education paradigm. Since people has an opportunity to use various contents anytime and communicate in an interactive way, the method of learning has changing. In 2011, Korean government has established the smart education promotion plan to be a first mover in the paradigm shift from e-learning to smart learning. Especially, government aimed to improve the quality of learning materials and method in public schools, and also to decrease the high expenditure on private education. However, the achievement of smart education policy has not emerged yet, and the refinement of smart learning policy and strategy is essential at this moment. Therefore, the purpose of this study is to propose the successful strategies for smart learning in public education. First, this study explores the status of public education and smart learning environment in Korea. Then, it derives the key success factors through SWOT(Strength, Weakness, Opportunity, Threat) analysis, and suggests strategic priorities through AHP(Analytic Hierarchy Priority) method. The interview and survey were conducted with total 20 teachers, who works in public schools. As a results, focusing on weakness-threat(WT) strategy is the most prior goal for public education, to activate the smart learning. As sub-factors, promoting the education programs for teachers($W_2$), which is still a weakness, appeared as the most important factor to be improved. The second sub-factor with high priority was an efficient optimizing the capability of new learning method($S_4$), which is a strength of systematic public education environment. The third sub-factor with high priority was the extension of limited government support($T_4$), which could be a threat to other public schools with no financial support. In other words, the results implicate that government institution factors should be considered with high priority to make invisible achievement in smart learning. This study is significant as an initial approach with strategic perspective for public education. While the limitation of this study is that survey and interview were conducted with only teachers. Accordingly, the future study needs to be analyzed in effectiveness and feasibility, by considering perspectives from field experts and policy makers.

Techniques for Acquisition of Moving Object Location in LBS (위치기반 서비스(LBS)를 위한 이동체 위치획득 기법)

  • Min, Gyeong-Uk;Jo, Dae-Su
    • The KIPS Transactions:PartD
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    • v.10D no.6
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    • pp.885-896
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    • 2003
  • The typws of service using location Information are being various and extending their domain as wireless internet tochnology is developing and its application par is widespread, so it is prospected that LBS(Location-Based Services) will be killer application in wireless internet services. This location information is basic and high value-added information, and this information services make prior GIS(Geographic Information System) to be useful to anybody. The acquisition of this location information from moving object is very important part in LBS. Also the interfacing of acquisition of moving object between MODB and telecommunication network is being very important function in LBS. After this, when LBS are familiar to everybody, we can predict that LBS system load is so heavy for the acquisition of so many subscribers and vehicles. That is to say, LBS platform performance is fallen off because of overhead increment of acquiring moving object between MODB and wireless telecommunication network. So, to make stable of LBS platform, in this MODB system, acquisition of moving object location par as reducing the number of acquisition of unneccessary moving object location. We study problems in acquiring a huge number of moving objects location and design some acquisition model using past moving patternof each object to reduce telecommunication overhead. And after implementation these models, we estimate performance of each model.

Research on the Curriculum for Integration of ICT+Design (ICT+디자인 융합 교육과정 개발연구)

  • Jeong, Sang-Hoon
    • Science of Emotion and Sensibility
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    • v.20 no.1
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    • pp.105-114
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    • 2017
  • Nowadays, novel and innovative technology including 3D printers, internet of things (IoT), and wearable devices are rapidly emerging. As we must constantly keep up with the most recent trends, words like convergence, multidisciplinarity, and design revolution indeed define society today. Due to the expansion of such diverse technological, industrial, and academic convergence trends, the role of design is becoming evermore essential in development of products as well as creative services. Even the government is pushing towards a 'creative economy' by encouraging ICT convergence to create novel industries as well as advanced jobs. In order to adapt flexibly to such changes in global trends, a solid academic curriculum centered around 'ICT+Design' must be developed. In the current research, we analyzed various literature and benchmarked the major universities both domestic and foreign. Also we utilized a survey-based approach against subjects who are experts or design specialists working in environments related to industry and research. In our proposed integrated ICT+Design educational curriculum, students familiarize themselves with design perspectives and methodology to creatively carry out the course. Moreover, experts from design and ICT came together in an act of 'Radical Collaboration' in which they shared their unique 'Design Thinking' in order to promote understanding and cooperation. Furthermore, industry experts have also taken part as mentors in order to create a workplace-oriented course with various integrated projects. Most importantly, the course was designed so that in addition to research, students can really get hands-on with their ideas in the creativity-integrated workplace.

Sensitivity Identification Method for New Words of Social Media based on Naive Bayes Classification (나이브 베이즈 기반 소셜 미디어 상의 신조어 감성 판별 기법)

  • Kim, Jeong In;Park, Sang Jin;Kim, Hyoung Ju;Choi, Jun Ho;Kim, Han Il;Kim, Pan Koo
    • Smart Media Journal
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    • v.9 no.1
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    • pp.51-59
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    • 2020
  • From PC communication to the development of the internet, a new term has been coined on the social media, and the social media culture has been formed due to the spread of smart phones, and the newly coined word is becoming a culture. With the advent of social networking sites and smart phones serving as a bridge, the number of data has increased in real time. The use of new words can have many advantages, including the use of short sentences to solve the problems of various letter-limited messengers and reduce data. However, new words do not have a dictionary meaning and there are limitations and degradation of algorithms such as data mining. Therefore, in this paper, the opinion of the document is confirmed by collecting data through web crawling and extracting new words contained within the text data and establishing an emotional classification. The progress of the experiment is divided into three categories. First, a word collected by collecting a new word on the social media is subjected to learned of affirmative and negative. Next, to derive and verify emotional values using standard documents, TF-IDF is used to score noun sensibilities to enter the emotional values of the data. As with the new words, the classified emotional values are applied to verify that the emotions are classified in standard language documents. Finally, a combination of the newly coined words and standard emotional values is used to perform a comparative analysis of the technology of the instrument.

멀티미디어 정보시스템을 이용한 기업체 교육의 효과요인 도출을 위한 실증적 연구

  • 김병곤;이동만;박순창
    • Proceedings of the CALSEC Conference
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    • 1999.11a
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    • pp.280-293
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    • 1999
  • 본 연구는 경영학 관련 분야에서 멀티미디어 기술의 경영학적 측면의 응용에 관한 연구의 중요성이나 필요성을 많은 학자들이 인식하고 있음에도 불구하고 아직 멀티미디어에 관한 연구가 전무한 실정에서 시도한 초기연구라는데 연구의 의의가 있다. 이러한 시점에서 교육공학과 경영정보학을 접목시킨 멀티미디어에 관한 연구는 상당히 중요할 것으로 판단된다. 이와 같이 본 연구는 경영정보학 분야에서 멀미미디어에 관한 연구로서는 초기의 연구로서, 본 연구가 가지는 연구의 필요성이나 중요성에 대해서는 우리들이 충분히 인식할 수 있을 것이다. 지금까지 국내외적으로 멀티미디어 정보시스템을 이용한 교육의 효과에 관한 연구는 몇 편의 탐색적 논문이 발견되고 있으나, 멀티미디어를 이용한 교육의 효과를 구성하는 요인이 무엇인지를 밝히기 위한 연구는 거의 전무한 실정이다 이러한 상황에서 멀티미디어를 이용한 교육의 효과를 구성하는 요인이 무엇이며, 구성요인 중 어떤 요인이 기업이나 학습자에게 가장 큰 효과를 가져다주는지를 밝히기 위한 연구는 현실적으로 상당히 중요하며 의미 있는 연구로 받아들여진다. 본 연구는 멀티미디어 정보시스템을 이용한 기업체 교육훈련의 효과요인을 도출하기 위하여 문헌연구와 실증적 연구를 병행 수행하였다. 우선 멀티미디어 정보시스템에 관한 문헌연구를 통하여 멀티미디어를 이용한 교육의 22가지 효과항목을 도출하였다. 다음으로 멀티미디어 정보시스템을 갖추고 있는 국내 5대 재벌 그룹연수원의 멀티미디어 교육실에서 교육을 받은 517명의 기업체 사원들을 대상으로 약 2개월간 설문조사를 실시하여 자료를 수집하고, 통계분석 패키지를 이용하여 자료를 분석하였다. 방식을 결합한 하이브리드 형태이다.인터넷으로 주문처리하고, 신속 안전한 배달을 기대한다. 더불어 고객은 현재 자신의 물건이 배달되는 경로를 알고싶어 한다. 웹을 통해 물건을 주문한 고객이 자신이 물건의 배달 상황을 웹에서 모니터링 한다면 기업은 고객으로 공간적인 제약으로 인한 불신을 불식시키는 신뢰감을 주게 된다. 이러한 고객서비스 향상과 물류비용 절감은 사이버 쇼핑몰이 전국 어디서나 우리의 안방에서 자연스럽게 점할 수 있는 상황을 만들 것이다.SP가 도입되어, 설계업무를 지원하기위한 기본적인 시스템 구조를 구상하게 된다. 이와 함께 IT Model을 구성하게 되는데, 객체지향적 접근 방법으로 Model을 생성하고 UML(Unified Modeling Language)을 Tool로 사용한다. 단계 4)는 Software Engineering 관점으로 접근한다. 이는 최종산물이라고 볼 수 있는 설계업무 지원 시스템을 Design하는 과정으로, 시스템에 사용될 데이터를 Design하는 과정과, 데이터를 기반으로 한 기능을 Design하는 과정으로 나눈다. 이를 통해 생성된 Model에 따라 최종적으로 Coding을 통하여 실제 시스템을 구축하게 된다.the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation model of the HAN projec

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Development of deep learning network based low-quality image enhancement techniques for improving foreign object detection performance (이물 객체 탐지 성능 개선을 위한 딥러닝 네트워크 기반 저품질 영상 개선 기법 개발)

  • Ki-Yeol Eom;Byeong-Seok Min
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.99-107
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    • 2024
  • Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

Sentiment Analysis of Movie Review Using Integrated CNN-LSTM Mode (CNN-LSTM 조합모델을 이용한 영화리뷰 감성분석)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.25 no.4
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    • pp.141-154
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    • 2019
  • Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.