• Journal of Multimedia Information System
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• v.3 no.4
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.860-863
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• 2003

인터넷과 정보 통신 기술의 발달은 컴퓨터 응용 및 컴퓨팅 환경에 상당한 변화를 가져왔으며, 여러 분야에서 이들 기술이 응용되고 있다. 고도로 발전하고 있는 정보통신 기술이 교육분야에 적용 및 활용되어 기존의 교육 패러다임에 상당한 변화를 초래함으로써, 새로운 교육형태의 교육체계 구현을 통해 교육 현장에 커다란 기여를 하고 있다. 대표적인 것이 원격교육을 비롯한 e-learning, 가상교육시스템 등이 있다. 인터넷과 컴퓨터가 보편화된 현재, 많이 연구 제안되고 구현이 되어서, 실제로 온라인 상에서 실시간 혹은 비실시간으로 학습교육시스템들이 서비스되고 있다. 본 논문에서는 기존에 많이 제안된 e-Learning시스템에 협력학습의 개념을 도입하여 교수-학습자 뿐 아니라 학습자-학습자간에 상호작용을 극대화하고, 한발 더 나아가 웹을 통하여 교수의 강의 내용을 학습자가 언제, 어디서든지 멀티미디어 데이터를 제공받아서 학습 및 평가 받을 수 있는 LOD(Lecture on Demand :주문형 강의)을 도입하여 협력학습을 지원하는 e-Learning 시스템의 LOD 플랫폼의 설계를 제안한다.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1102-1112
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• 2017

The purpose of this study is to design and application of term project model for Game Math in flipped learning environment. In the term project self study model, students interacts with multi-instruction materials and multi-tutors on flipped learning. We develop a case for game update term project and implement it to a real Game Math classroom. As a result, we show the positive learning experiences focused on effects of technology and human relation through survey.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.606-610
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• 2009

As the technology of tele communication develops, learners want their study environment free from time and place limitation. The paper will reformulate the existing learning contents using the DMB broadcasting technology, enhancing the qualities of learning by transforming analog learning environments into digital ones. The paper will also present Multimedia-based learning patterns using one of the most notable technologies, DMB broadcasting technology.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.519-527
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• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

• Korean Journal of Cognitive Science
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• v.13 no.3
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• pp.47-60
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• 2002

The study investigated the effects of types of presentation and cognitive load on multimedia learning. In experiment 1, subject were 90 elementary school students. The subject were assigned in three conditions: Narration and Text (NT) condition, Animation and Narration(AN) condition, Animation and Text(AT) condition. The result showed that AN condition improved the learning performances in comparison with AT condition, NT condition. Experiment 2 was administrated to 87 undergraduate students. They were participated in three conditions, also. The conditions were Animation and Text (AT) condition, Animation and Narration (AN) condition, Animation, Narration and Text (ANT) condition. the results showed that AN condition was greater in AT, ANT condition. The results from a series of these experiments imply that varying the types of presentation of identical learning materials had influences on the performances. Multimedia presentation(animation and verbal conditions) improved the learning performances in comparison with monomedia presentation(verbal condition), and the advantage was raised when learners were provided the learning material in the multimodal and multimedia environment(AN condition). Also, it came out that redundant text identical to narration disrupted learning when learners were in the picture (either animation or illustration) and narration conditions. Likewise, also for adults, performances were improved in the multimodal conditions and redundant text identical to narration was not helpful for learning. These results are evidences for the dual-coding theory and the cognitive load theory.

• Korean Journal of Cognitive Science
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• v.17 no.3
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• pp.231-253
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• 2006

The interaction effects of the cognitive style and the presentation order of learning material was explored in the study. Visualizers performed better when the graphic information was presented prior to the verbal information, whereas verbalizers did better when the verbal information was presented prior to the graphic information. The results of the present research have practical implication of personalized multimedia design based on the learner's cognitive style. The results also have suggested that the cognitive load of a multimedia material can be varied depending on the compatibility of the cognitive style and the material.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.281-288
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• 2018

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging startup companies in recent years. Despite the huge success of crowdfunding, it has been reported that only around 40% of crowdfunding campaigns successfully raise the desired goal amount. The purpose of this study is to investigate key factors influencing successful fundraising on crowdfunding platforms. To this end, we mainly focus on contents of project campaigns, particularly their linguistic cues as well as multiple features extracted from project information and multimedia contents. We reveal which of these features are useful for predicting success of crowdfunding campaigns, and then build a predictive model based on those selected features. Our experimental results demonstrate that the built model predicts the success or failure of a crowdfunding campaign with 86.15% accuracy.