• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.1-10
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• 2007

Programming is emphasized in information(computer science) education course domestically and in foreign countries, and novices are given ample opportunities to experience programming. Programming error is a critical factor which makes it difficult to learn programming for novices. However, if they are given appropriate feedback, it can have positive influence on programming learning. In this paper, we design programming learning supporting system for novice through error feedback and provide some implementations for EPL 'Dolittle'. This system has four features as highlighting, guiding messages, object tree, and step-execution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.451-457
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• 2018

We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with $256{\times}256$ pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of $720{\times}480$ pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.61-70
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• 2006

In the area of e-learning education, SCORM2004 that is suggested by ADL and is a defacto standard allows to design and apply various interrelations among learning objects which organize learning process through consolidating IMS Simple Sequencing into S&N. In this paper, we intend to realize a web_based adaptive learning management that enable to guide experientially the learning activity through the SCORM 2004 S&N and the Traffic-Signal-Lamp Metaphor. This adaptive system allows professor to design the learning courseware realizing various learning strategies to be able to reuse same learning contents and student to be leaded a adaptive learning through being supplied immediately the state and evaluation of learning.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.823-831
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• 2019

In recent years, there has been developed systems such as a surveillance system and access control using a face recognition function instead of a password or an RFID chip, thereby reducing the risk of falsification. Moreover, deep learning technology has been applied to real-time face recognition technology in video, so it makes possible the development of access control system that improves the accuracy of recognition and efficiency of management. In this paper, we propose a real-time access management system based on face recognition using ResNet. The system is based on web server, which make it possible to manage the access by recognizing the person of the image through the camera and access information stored in the database. It can be accessed by a user application to receive various information. The implemented system identifies a person in real time and allows access control by accurately distinguishing whether they are members or not, and the test results can recognize in 0.2 seconds. The accuracy of recognition rate is up to about 97% depending on the experiment environment. With this system, access can be managed quickly and effectively, even many people rush to it.

• Journal of Information Processing Systems
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• v.2 no.3 s.4
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• pp.178-182
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• 2006

Children exhibit different behaviors, skills, and motivations. The main aim of this research was to investigate children's sensibility factors for icons, and to look for the best typeface for application to Web-Based Instruction (WBI) for e-Learning. Three types of icons were used to assess children's sensibilities toward metaphors: text-image, representational, and spatial mapping. Through the factor analysis, we found that children exhibited more diverse reactions to the text-image and representational types of icons than to the spatial mapping type of icons. Children commonly showedn higher sensibilities to the aesthetic-factor than to the familiarity-factor or the brevity-factor. In addition, we propose a collaborative-typeface system, which recommends the best typeface for children regarding the readability and aesthetic factor in WBI. Based on these results, we venture some suggestions on icon design and typeface selection for e-Learning.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.345-354
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• 2021

Recently, deep learning-based image recognition systems have been adopted to various surveillance environments, but most of them are still picture-type object recognition methods, which are insufficient for the long term temporal analysis and high-dimensional situation management. Therefore, we propose a method recognizing the specific dangerous situation generated by human in real-time, and utilizing deep learning-based object analysis techniques. The proposed method uses deep learning-based object detection and tracking algorithms in order to recognize the situations such as 'trespassing', 'loitering', and so on. In addition, human's joint pose data are extracted and analyzed for the emergent awareness function such as 'falling down' to notify not only in the security but also in the emergency environmental utilizations.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.827-830
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• 2005

e-learning 교육은 오프라인 교육의 다양한 제한적 문제를 해결할 수 있는 대안으로 많은 발전을 이루어 오고 있다. e-learning 교육의 표준화 작업으로 앞으로 더 많은 발전을 가져올 것이고 ITS (Intelligent Tutoring System)의 구현을 앞당길 것이다. 그러나 모든 교육이 능동적으로 문제를 해결해 나갈 수 있는 능력을 키우는 것 이라는 교육학적 입장에서 본 논문은 학습자의 개별적 특성을 수용하는 개별화된 학습방향을 선택할 수 있는 Self-learning 서비스를 제안한다. 이 서비스는 교수설계자에 의해 지정된 시퀀싱을 학습자가 재정렬 할 수 있다. 이 시스템은 SCORM 기반의 LMS 에 추가되는 서비스이다.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.96-109
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• 2011

Recently, the geographic information system(GIS) is generally used in various fields with the development of information and communication technology, with expansion of its applications and utilization scope. Especially, utilizing GIS is expected to have positive effects on the geography learning and more helpful for the geographic information observation compared to the picture or 2D based media. The effective visualization of complex geographic data does not only take realization of its visual information but also increases the human ability in analysis and understanding to use the geographic information. In this paper, we examine a method to develop the geography learning contents based on the technology with augmented reality and GIS, and then we have a case study for various kinds of visualization techniques and examples to use in geography learning situation. Moreover, we introduce an example of the manufacturing process from the existing GIS data to augmented reality based geography learning system. From the above, we show that the usefulness of our method is applicable for effective visualization of the three-dimensional geographic information in the geography learning environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.