• Archives of Pharmacal Research
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• v.24 no.3
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• pp.234-239
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• 2001

Three types of learning and memory tests (Morris water maze, active and passive avoidance) were performed in rats following intracerebroventricular infusion of ethylcholine aziridium (AF64A). In Morris water maze, AF64A-treated rats showed the delayed latencies to find the platform iron 6th day after the infusion. In pretrained rats, AF64A caused the significant delay of latency at 7th days but not 8th day. In the active avoidance for the pretrained rats, the escape latency was significantly delayed in AF64A-treatment. The percentages of avoidance in AF64A-treated rats were less increased than those in the control. Especially, the percentage of no response in the AF64A-treated rats was markedly increased in the first half trials. In the passive avoidance, AF64A-treated rats shortened the latency 1.5 h after the electronic shock, but not 24 h. AF64A also caused the pretrained rats to shorten the latency 7th day after the infusion, but not 8th day. These results indicate that AF64A might impair the learning and memory. However, these results indicate that the disturbed memory by AF64A might rapidly recover after the first retrain. Furthermore, these results suggest that AF64A may be a useful agent for the animal model of learning for Spatial cognition .

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.719-723
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• 2007

It is very active that existing companies of providing e-Learning contents try to differentiate themselves through a business model based on Web 2.0. For instance, Etoos, online education website (www.etoos.com) run by SK Communications has made more space where students can participate in the Web 2.0 era and overhauled its website completely, turning into an open-ended one, which strengthens learning and fun in 2007. This study is to analyze the present state of e-Learning contents with representative e-learning sites for middle and high school students, to find that the development direction for next generation e-Learning lies in developing contents focusing on learners that can get effective feedback and drawing collective intelligence grounded on the essence of Web 2.0, and to suggest 'the project to form virtual private tutor community in e-Learning contents.'

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.103-106
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• 2005

In this paper, we propose a language modeling approach to improve the performance of a large vocabulary continuous speech recognition system. The proposed approach is based on the active learning framework that helps to select a text corpus from a plenty amount of text data required for language modeling. The perplexity is used as a measure for the corpus selection in the active learning. From the recognition experiments on the task of continuous Korean speech, the speech recognition system employing the language model by the proposed language modeling approach reduces the word error rate by about 6.6 % with less computational complexity than that using a language model constructed with randomly selected texts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Journal of the Korean earth science society
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• v.21 no.3
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• pp.219-229
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• 2000

Constructive learning is an active process of meaning construction and students decide their individual learning objectives according to their own interest concern and ability. The purpose of this study is to develop a teaching-learning model and classroom materials from constructivist perspectives and to apply them to an elementary school classroom in Seoul for one month. In this study, it was reorganized the contents of unit of 'beds and fossils' based on the discussion between students and teacher during the second semester of 4th grade class. The teaching-learning model consists of five steps: 1) introduction; 2) exercise; 3) presentation; 4) consensus; and 5) development. The implementation results were summarized.

• Culinary science and hospitality research
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• v.14 no.4
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• pp.14-26
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• 2008

The purpose of this study is to develop a communication model of teaching-learning at culinary practical learning class in school. Statistically, the organizational culture of culinary schools was influenced by the nature of hierarchical culture, task outcomes, and the conservative culture of organizations in companies. First, in basic skill class, teaching and learning methods are based on a teacher who leads students according to his plans and decisions. Second, in a higher skill course, teaching and learning methods are based on students who take an active part by injecting some fresh ideas into their class. Third, the model of three courses for culinary skill development has an effect on processing into a modeling-scaffolding-fading method by teaching and learning in school. It was ascertained that organizational culture directly or indirectly influenced organizational effectiveness and organizational culture in culinary schools. Moreover, it was found that organizational culture was the biggest influencing concept for communication effectiveness between teachers and students.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.509-514
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• 2000

A structural control algorithm using CMAC(Cerebellar Model Articulation Controller) neural network is proposed Learning rule for CMAC is derived based on cost function. Learning convergence of CMAC is compared with MLNN(Multilayer Neural Network). Numerical examples are shown to verify the proposed control algorithm. Examples show that CMAC can be applicable to structural control with fast learning speed.