• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.983-989
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• 2006

Current emigration and immigration control inspects passports by the naked eye, registers them by manual input, and compares them with items of database. In this paper, we propose the method to recognize information codes of passports. The proposed passport recognition method extracts character-rows of information codes by applying sobel operator, horizontal smearing, and contour tracking algorithm. The extracted letter-row regions is binarized. After a CDM mask is applied to them in order to recover the individual codes, the individual codes are extracted by applying vertical smearing. The recognizing of individual codes is performed by the RBF network whose hidden layer is applied by ART 2 algorithm and whose learning between the hidden layer and the output layer is applied by a generalized delta learning method. After a photo region is extracted from the reference of the starting point of the extracted character-rows of information codes, that region is verified by the information of luminance, edge, and hue. The verified photo region is certified by the classified features by the ART 2 algorithm. The comparing experiment with real passport images confirmed the good performance of the proposed method.

• Journal of the Korean Society of Earth Science Education
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• v.1 no.1
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• pp.41-51
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• 2008

For teaching-learning to geological region of earth science part in science education, middle school, the practical uses of natural science museum are very effective. So, the appropriate uses of the natural science museum are necessary for the teaching and learning of Science Education. This study aims to consider the presentation characteristics of the natural science museum and to examine how is effective it to geological region in middle school science curricula on those uses. From the results, the natural science museum is low in multi-sided and open-endedness presentation characteristics but high in ones of accessible characteristic. And its presentations are good in multimodal characteristics using supported materials but relatively low in relevant and multimedia ones. In the museum, diorama and self-performing presentation types are not but internet ones are most. The presentations of the natural science museum are mainly assigned to knowledge region linked to basic science concepts but relatively insufficient in STS aspects, on the basis of connection the presentations with middle school science curricula. It is respected that these insufficiencies might be diminished by variable arrangements of and explanations to the presentations for understanding improvements. And, applies to the presentations in STS may be encountered, if multi-sided observations to them is available.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.39-48
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• 2021

This study aimed to analyze the level of professors' understanding and perception of adaptive learning and proposed how college can implement successful adaptive learning in college classes. For research purposes, online survey was conducted by 162 professors of A university in capital region. As a result, professors seemed to feel pressure to provide students personalized feedback and gave concerned that students don't study enough in advance before participating in class. It was also found that professors realized that they have low level of understanding about adaptive learning, while they revealed intention to make use of adaptive learning in their class. They also answered that adaptive learning system is the most helpful support for encouraging professors to apply adaptive learning in real class. We proposed what is required to encourage professor to implement adaptive learning in their class.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.462-469
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• 2002

While the conventional K-means algorithms use a fixed weight to design a vector codebook for all learning iterations, the proposed method employs a variable weight for learning iterations. The weight value of two or more beyond a convergent region is applied to obtain new codevectors at the initial learning iteration. The number of learning iteration applying a variable weight must be decreased for higher weight value at the initial learning iteration to design a better codebook. To enhance the splitting method that is used to generate an initial codebook, we propose a new method, which reduces the error between a representative vector and the member of training vectors. The method is that the representative vector with maximum squared error is rejected, but the vector with minimum error is splitting, and then we can obtain the better initial codevectors.

• Journal of Korean Home Economics Education Association
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• v.2 no.1
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• pp.101-110
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• 1990

The purpose of this study is to survey the learner’s need achievement, interest and practical level of the learning objectives according to the teacher’s teaching method in home economics of middle school(Dietary Life Unit), to examine the differences according to the local situation, and to get some information to improve the teaching method. This study surveyed the eight units of dietary life with 459 students in the rural community (224 students) and Seoul(235 Students). The collected data were analyzed by T-test, F-test. To sum up this study are as follows: 1. When the need, achievement, interest and practical level of unit “food”are compared the students of seoul with those of rural community, the student of Seoul show high in “The use of Processed Foodstuffs” and low in “Cooking the Processed Foodstuffs.”The student of rural community show in “A kind of Cooking Method”and low in “Environment and Food Life”. 2. The need, achievement, interest, practical level of the whole unit in rural community is higher than those is Seoul. 3. According to teaching method, comparison Seoul with rural community is revealed as follows. Seoul region is revealed significance to discovery learning in “Cooking Foodstuffs(The achievement and interest level) and ”Environment and Food Life”(interest level). Rural community is revealed significance to discussion learning in “The constituent and Food Life”(The need level, interest level). Rural community is revealed significance to explanation learning in “Environment and Food Life”(The achievement level).

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1380-1390
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• 2021

The video face recognition (FR) is one of the most popular researches in the field of computer vision due to a variety of applications. In particular, research using the attention mechanism is being actively conducted. In video face recognition, attention represents where to focus on by using the input value of the whole or a specific region, or which frame to focus on when there are many frames. In this paper, we propose a novel attention based deep learning method. Main novelties of our method are (1) the use of combining two loss functions, namely weighted Softmax loss function and a Triplet loss function and (2) the feasibility of end-to-end learning which includes the feature embedding network and attention weight computation. The feature embedding network has a positive effect on the attention weight computation by using combined loss function and end-to-end learning. To demonstrate the effectiveness of our proposed method, extensive and comparative experiments have been carried out to evaluate our method on IJB-A dataset with their standard evaluation protocols. Our proposed method represented better or comparable recognition rate compared to other state-of-the-art video FR methods.

• Journal of Hydrogen and New Energy
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• v.33 no.5
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.238-244
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• 2020

The development and distribution rate of smartphones have progressed so rapidly that it is safe for the entire nation to use them in the smart age, and the use of smartphones has become an essential medium for the use of domestic media content, and many people are using various contents regardless of gender, age, or region. Recently, various media outlets have been consuming video content for online learning, indicating that learners utilize video content online for learning. In the previous research, satisfaction studies were conducted according to the type of content, and the improvement plan was necessary because no research was conducted on how to evaluate the learning content itself and provide it to learners. In this paper, we would like to propose a system through evaluation and review of learning content itself as a way to improve the way of providing video content for learning and quality learning content.