• Journal of Korea Game Society
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• v.18 no.6
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• pp.39-48
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• 2018

Recently, some games with physical and mental healing function are released. The healing games tend to make people relax. With the features of simple healing contents, the healing games provide users psychological stability more than strong stimulus. The games don't give users continuous excitement, so the popularity of the games goes down soon. In this paper, we investigate domestic and international healing games on the basis of the platform, such as mobile, console and PC. We analyze the feature of healing games with the four keys to fun(Hard Fun, Easy Fun, Serious Fun, People Fun) suggested by Nicole Lazzro. By this analysis, we find how much of the 4 keys to fun the games include and the correlation between the 4 keys to fun and the popularity of the healing games.

• Journal of Microbiology and Biotechnology
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• v.29 no.3
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• pp.367-372
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• 2019

Deactivation of aminoglycosides by their modifying enzymes, including a number of aminoglycoside O-phosphotransferases, is the most ubiquitous resistance mechanism in aminoglycoside-resistant pathogens. Nonetheless, in a couple of biosynthetic pathways for gentamicins, fortimicins, and istamycins, phosphorylation of aminoglycosides seems to be a unique and initial step for the creation of a natural defensive structural feature such as a 3',4'-dideoxy scaffold. Our aim was to elucidate the biochemical details on the beginning of these C3',4'-dideoxygenation biosynthetic steps for aminoglycosides. The biosynthesis of istamycins must surely involve these 3',4'-didehydroxylation steps, but much less has been reported in terms of characterization of istamycin biosynthetic genes, especially about the phosphotransferase-encoding gene. In the disruption and complementation experiments pointing to a putative gene, istP, in the genome of wild-type Streptomyces tenjimariensis, the function of the istP gene was proved here to be a phosphotransferase. Next, an in-frame deletion of a known phosphotransferase-encoding gene forP from the genome of wild-type Micromonospora olivasterospora resulted in the appearance of a hitherto unidentified fortimicin shunt product, namely 3-O-methyl-FOR-KK1, whereas complementation of forP restored the natural fortimicin metabolite profiles. The bilateral complementation of an istP gene (or forP) in the ${\Delta}forP$ mutant (or ${\Delta}istP$ mutant strain) successfully restored the biosynthesis of 3',4'-dideoxy fortimicins and istamycins, thus clearly indicating that they are interchangeable launchers of the biosynthesis of 3',4'-dideoxy types of 1,4-diaminocyclitol antibiotics.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.50-56
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• 2020

With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.898-910
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• 2020

Single image-based super-resolution has been studied for a long time in computer vision because of various applications. Various deep learning-based super-resolution algorithms are introduced recently to improve the performance by reducing side effects like blurring and staircase effects. Most deep learning-based approaches have focused on how to implement the network architecture, loss function, and training strategy to improve performance. Meanwhile, Several approaches using Attention Module, which emphasizes the extracted features, are introduced to enhance the performance of the network without any additional layer. Attention module emphasizes or scales the feature map for the purpose of the network from various perspectives. In this paper, we propose the various channel attention and spatial attention in single image-based super-resolution and analyze the results and performance according to the architecture of the attention module. Also, we explore that designing multi-attention module to emphasize features efficiently from various perspectives.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.588-594
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• 2021

SIMON, a lightweight block cipher developed by the US National Security Agency, is a family of block ciphers optimized for hardware implementation. It supports many kinds of standards to operate in various environments. The counter mode of operation is one of the operational modes. It provides to encrypt plaintext which is longer than the original size. The counter mode uses a constant(Nonce) and Counter value as an input value. Since Nonce is the identical for all blocks, so it always has same result when operates with other constant values. With this feature, it is possible to skip some instructions of round function by pre-computation. In general, the input value of SIMON is affected by the counter. However in an 8-bit environment, it is calculated in 8-bit units, so there is a part that can be pre-computed. In this paper, we focus the part that can be pre-calculated, and compare with previous works.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.5
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• pp.59-71
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• 2005

A RFID (Radio Frequency Identification) system receives attention as the technology which can realize the ubiquitous computing environment. However, the feature of the RFID tags may bring about new threats to the security and privacy of individuals. Recently, Juels proposed the minimalist cryptography for very low-cost RFID tags, which is secure. but only under the impractical assumption such that an adversary is allowed to eavesdrop only the pre-defined number of sessions. In this paper, we propose a scheme to protect privacy for very low-cost RFID systems. The proposed protocol uses only bit-wise operations without my costly cryptographic function such as hashing, encryption which is secure which is secure against an adversary who is allowed to eavesdrop transmitted message in every session any impractical assumption. The proposed scheme also is more efficient since our scheme requires less datas as well as few number of computations than Juels's scheme.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.2
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• pp.75-95
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• 2022

This study was carried out with the aim of analyzing the development and functional changes of Webdewey, which has become a basic tool of classification learning, analyzing it in terms of usability for learners, and suggesting specific ways to improve WebDewey's usability. In order to achieve this research objective, the concepts and principles of UI and usability were first laid out, and Webdewey's structure and key functions were analyzed. Since then, Webdewey's media changes and periodical feature changes have been analyzed. In addition, an opinion survey was conducted on the usability of WebDewey among learners who used WebDewey in the learning process, and proposed ways to improve WebDewey's usability based on the implications and direction of improvement derived from it. In terms of UI, proposals have been made to introduce display methods, visualization devices, the advantages of printed versions, and the development of Korean versions. In terms of the 'Create built number' function, suggestions have been made to improve usability in terms of basic number selection, composite route guidance and error message provision, new reference and route construction, screen and button design, and built-number component guidance.