• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1203-1211
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• 2022

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

• Korean Journal of Comparative Education
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• v.27 no.1
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• pp.199-222
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• 2017

This study explores and compares how three guidebooks on intercultural education in Europe (Education Pack, Intercultural Learning, and Intercultural Education in Primary School) define their objectives, contents, methods, and evaluation in order to promote intercultural education to young people and adults. All these three guidebooks start with the underlying fact that difference is the reality of our societies and propose similar objectives. These guidebooks include furthering an understanding of the reality of an interdependent world, going beyond negative prejudice and stereotypes, and generating positive attitudes and habits of behaviors towards people from other societies and cultures. They also suggest similar contents for intercultural education, which all relate to the discovery of mutual relationships and the dismantling of barriers between people from other cultural backgrounds. However, with regard to methods, they show significant contrast: Education Pack and Intercultural Learning propose several stages that consist of imagining ourself from the outside, understanding the world we live in, being acquainted with other realities, seeing difference positively, and favouring positive attitudes, values and behavior, while Intercultural Education in the Primary School insists on positive learning, discussion and group work. Evaluation remains the least developed area in intercultural education; fortunately, the last guidebook treats this problem more seriously than the first two by dedicating a whole chapter to it. What is required of us now is determining how to adapt the principles and approaches of European intercultural education to Korean societies and schools.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.185-195
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• 2003

A performance evaluation, which requires to observe students in the course of learning and studying and to evaluate their reports and materials, is emerging as an alternative evaluation method to overcome the shortcoming of simple written tests. However, there are many difficulties in real teaching setting to apply the performance evaluation, because it requires many burdens of efforts and time. In order to reduce these burdens of teachers, there have been several approaches which utilize the Internet for the evaluation. But these previous approaches have several limitations that they don't allow students' participation in evaluation activities, fail to provide a variety of evaluation methods. and/or support teachers' feedbacks very limitedly. In order to overcome these limitations. therefore. this paper designed and implemented a web- based performance evaluation system supporting the participation of students in doing evaluation themselves and various evaluation methods. which can be effectively managed by teachers. This web-based performance evaluation system developed in this paper can enhance not only students' high level thinking abilities but also their emotional and intellectual abilities. It can also help teachers to reduce the burden of working and the time in plenty used for evaluating students.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.55.3-55.3
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• 2019

We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. As an example, we use the IllustrisTNG hydrodynamic simulation of a (75 h^-1 Mpc)³ volume to train our machine to predict e.g., stellar mass and star formation rate in a galaxy-sized halo based purely on its DM content. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements we introduce here such as a refined error function in machine training and two-stage learning. Aided by these improvements, our model demonstrates a significantly increased accuracy in predicting baryonic properties compared to prior attempts --- in other words, the machine better mimics IllustrisTNG's galaxy-halo correlation. By applying our machine to the MultiDark-Planck DM-only simulation of a large (1 h^-1 Gpc)³ volume, we then validate the pipeline that rapidly generates a galaxy catalogue from a DM halo catalogue using the correlations the machine found in IllustrisTNG. We also compare our galaxy catalogue with the ones produced by popular semi-analytic models (SAMs). Our so-called machine-assisted semi-simulation model (MSSM) is shown to be largely compatible with SAMs, and may become a promising method to transplant the baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-only run. We discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.166-173
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• 2014

Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.707-721
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• 2022

In this study, a state-of-the-art review on urban inundation simulation technology was presented summarizing major achievements and limitations, and future research recommendations and challenges. More than 160 papers published in major domestic academic journals since the 2000s were analyzed. After analyzing the core themes and contents of the papers, the status of technological development was reviewed according to simulation methodologies such as physically-based and data-driven approaches. In addition, research trends for application purposes and advances in overseas and related fields were analyzed. Since more than 60% of urban inundation research used Storm Water Management Model (SWMM), developing new modeling techniques for detailed physical processes of dual drainage was encouraged. Data-based approaches have become a new status quo in urban inundation modeling. However, given that hydrological extreme data is rare, balanced research development of data and physically-based approaches was recommended. Urban inundation analysis technology, actively combined with new technologies in other fields such as artificial intelligence, IoT, and metaverse, would require continuous support from society and holistic approaches to solve challenges from climate risk and reduce disaster damage.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.123-130
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• 2022

Modernization of the educational sector requires globalization, democratization, and the transition to an information technology society. The main goal of education at the present stage is to solve the problem of ensuring the priority of the development of education and science. In modern conditions, the quality of training of qualified specialists is becoming particularly relevant. The great role of teacher education is emphasized by its main goal, which is to train specialists who can ensure the versatile and innovative development of a person as a person and the highest value of society, its mental, physical and aesthetic abilities, high moral qualities, and, consequently, the enrichment on this basis of the intellectual, creative and cultural potential of the people. Among the strategic tasks of modernizing higher education is to ensure informatization of the educational process and access to International Information Systems. The essence of the concept of multimedia is clarified. In the context of media education, multimedia lists a number of functions: informational, interpretive, cultural, entertainment, and educational. The need to meet the needs outlined in the article in the conditions of informatization of the educational process requires the teacher to have knowledge and skills in the field of multimedia pedagogical technologies, knowledge of advanced methods and means of modern science. It is considered what relevant concepts of media education have been developed and are being developed in Ukraine and form an important basis for the modernization of education, which will contribute to the construction of an information society in the country and the formation of civil society. Distance learning is considered - the most democratic form of education that allows broad segments of society to get an education. Distance learning methods are used in higher education institutions, in school education, in the system of advanced training of teachers, in the system of training managerial personnel.