• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.522-524
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• 2019

In spite of a rapid development in the quality of built-in mobile cameras, their some physical restrictions hinder them to achieve the satisfactory results of digital single lens reflex (DSLR) cameras. In this work we propose an end-to-end deep learning method to translate ordinary images by mobile cameras into DSLR-quality photos. The method is based on the framework of generative adversarial networks (GANs) with several improvements. First, we combined the U-Net with DenseNet and connected dense block (DB) in terms of U-Net. The Dense U-Net acts as the generator in our GAN model. Then, we improved the perceptual loss by using the VGG features and pixel-wise content, which could provide stronger supervision for contrast enhancement and texture recovery.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.59-61
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• 2018

Highly compressed images typically not only have low resolution, but are also affected by compression artifacts. Performing image super-resolution (SR) directly on highly compressed image would simultaneously magnify the blocking artifacts. In this paper, a SR method based on deep learning is proposed. The method is an end-to-end trainable deep convolutional neural network which performs SR on compressed images so as to reduce compression artifacts and improve image resolution. The proposed network is divided into compression artifacts removal (CAR) part and SR reconstruction part, and the network is trained by three-step training method to optimize training procedure. Experiments on JPEG compressed images with quality factors of 10, 20, and 30 demonstrate the effectiveness of the proposed method on commonly used test images and image sets.

• ETRI Journal
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• v.45 no.4
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• pp.678-689
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• 2023

This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency (F₀), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.

• Educational Technology International
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• v.9 no.2
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• pp.97-121
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• 2008

The purposes of the present study are to describe the design of mobile learning scenarios based on learning sciences theories, and to discuss implications for the future research in this area. To move beyond mere speculations about the abundant possibilities of mobile learning and to make real impact in K-12 school settings, it is critical to conduct school-based research grounded on the learning sciences theories. Towards this end, this paper describes school-based mobile learning projects conducted by a research team at the Learning Sciences Lab in Singapore, and then discusses the possibilities and challenges of mobile learning to further inform future research. Specifically, this paper explores the affordances of mobile technology, such as portability, connectivity and context-sensitivity, to design seamless learning scenarios that bridge formal and informal learning experiences. The authors present a framework for re-conceptualizing different types of learning based on physical settings and intentionality, and then describe two seamless learning scenarios, namely 3Rs and Chinatown Trail, which were implemented in one primary school in Singapore. In conclusion, the authors discuss the affordances of seamless mobile learning for enhancing one's lived experiences to build a living ecological relationship between the person and the environment, and how mobile technology can play a critical role for enabling such lived experiences.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.19-24
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• 2018

This study investigated the academic stress and the immersion in learning in relation to AR and VR assisted instructions compared to traditional approaches. To that end, 78 $8^{th}$ graders in T and S city in Gangwondo were assigned to experimental and control groups. The experimental group received the VR and AR lessons. The academic stress was measured with the pre- and post-test scores, while the immersion in learning was measured with the post-test scores. In brief, AR and VR assisted lessons made statistically significant differences in the academic stress and immersion in learning in comparison to the traditional approaches.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.79-87
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• 2023

On-line learning has been adopted as a major educational method due to the COVID-19 pandemic. Students and faculties got accustomed to on-line educational environment as they experienced it during the COVID-19 pandemic. Development of various technologies and social requirement for educational renovation lay groundwork for on-line learning as well. Therefore, on-line learning or blended learning will be likely to go on after the end of COVID-19 pandemic and it is necessary to prepare the guidelines for effective utilizing on-line learning. The primary purpose of this study is to examine the learning behaviors and the learning effects by using LMS data. Learning behaviors were measured in terms of learning time and access frequency for pre-recorded video lectures targeting computer-practice classes. The results of empirical analysis reveal that frequency was the significant predictor of course achievements but learning time was not. The findings of empirical analysis will provide insights that the effective planning and designing on-line classes based on learning behaviors are key to enhancing learning effects and learner's satisfaction.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.88-93
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• 2021

The automotive industry is developing day by day. Among them, it is very important to prevent accidents while driving. However, despite the importance of developing automobile industry technology, accidents due to road defects increase every year, especially in the rainy season. To this end, we proposed a road defect detection system for road management by converging deep learning and raspberry pi, which show various possibilities. In this paper, we developed a system that visually displays through a map after analyzing the images captured by the Raspberry Pi and the route GPS. The deep learning model trained for this system achieved 96% accuracy. Through this system, it is expected to manage road defects efficiently at a low cost.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.190-195
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• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.