• Journal of Korean Library and Information Science Society
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• v.34 no.4
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• pp.179-202
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• 2003

Reading is a typical representative knowledge and information activities. It is a put of the presents man and by itself Read-learning is the cognitive and social activities of the creating platonic world. However, in the education training of students become a visible problems of read-learning too many. To solve the problems have to pull off a systematical plan in read activities. This aims to purpose the model of the union reading contents management as the method for the read activity based on the problems analysis of read-learning activity. It is reading contents management as the model knowledge cresting processes.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.3
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• pp.209-228
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• 2018

The primary purpose of this study is to analyze the learning activities of "the Reading a Book per Semester" with the goal of finding ways to collaborate with the school library. "the Reading a Book per Semester" is a program that requires students to read during the timetable-designated "Korean" class instead of encouraging students to read outside of class, as has been the case thus far. The program was designed by 3 parts: reading, sharing, and formulating. However, an analysis of 11 Korean textbooks and 22 approaches to the program showed that the model has shifted to . Under this new model, schools could expand each step in the following ways: utilize the school libraries more as the book search space during the selection process; include writing a reading diary in the reading process; and further encourage students to read outside of the classrooms in the formulating step. If these amendments were to be applied, they would strengthen the educational purpose of the school libraries within schools.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• English Language & Literature Teaching
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• v.11 no.2
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• pp.19-34
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• 2005

Hypertext raises question to general assumptions about our conventional conceptions of education. In this essay, three kinds of learning-models are presented by the application of "writing" hypertext literature to the English education of the elementary school. These models, which I call the "scene-centered" system, give knowledge to learners in non-linear, non-sequential structure. The term "scene" is a single concept or idea composed of a single sub-text, which is to be made by the group of students. This system is focused on the collaborative composition of students. Students, by generating sub-texts and connecting texts, perform the educational activities to expand the source text. The "scene-centered" system is, to put it into a Barte's term, a "writerly text." But in order to "write," "reading" should be accompanied. So, this system is a learning model in which writing and reading are carried on simultaneously. In all the process, students play a role of multi-user, with three access rights: read, write, and annotate. So, students making use of hypertext systems will act as reader-authors. And teachers will take the new role in collaborative writing environment. No longer the central authoritarian evaluator, they will become consultants, co-writers, coaches of their students.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.100-107
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• 2013

As the eBook market grows fast recently, various eBook viewer solutions such as hardware viewers and software readers came out to the market. We can, however, hardly find mobile eBook interfaces for the reading disabled people who have difficulties in reading for their visual impairment or learning disabilities, or dyslexia. An eBook viewer interfaces for the reading disabled people should be carefully and distinctively designed because the reading disabled people cannot use normal versions of eBook viewer. In this paper, we suggest a eBook viewer interface model to make the reading disabled people read eBooks easily. Depending on the type of the reading disabled people: the full blind, the almost blind, the just learning disabled, our model provides an adaptive interface to make them read eBooks effectively. In addition, unlike the existing simple audio books, we also support annotation systems to make the reading disabled people interact with eBook viewer. To show the effectiveness of our model, we implemented an eBook viewer prototype on an android-based mobile device. We are sure that our model and implementation can make the reading disabled people, who is 10% of all the domestic people, read eBooks effectively.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• 중국학논총
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• no.61
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• pp.255-277
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• 2019

This paper presented a course plan based on the ideas I gained from conducting a lecture on Chinese language for students in the second semester of the Chinese language department at a four-year university. In the paper, we sought to deviate from the traditional grammar-translation teaching style and find ways for students to enjoy learning without difficulty in all areas by using the 'total language approach' such as writing, speaking, listening and reading through reading skills. Therefore, we discussed the educational significance and expression of the 'Chinese Languages' class, and introduced the class stages and methods of progress. In other words, they suggested introduction of text plots, explanation of vocabulary and grammar, presentation of original text, questions about text, arrangement of words, ordering sentences to fit the plot, and understanding the plot while looking at the picture.