• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.17-22
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• 2021

Eco-friendly magnesium-air battery is a kind of metal-air battery known as a primary battery with a very high theoretical discharge capacity. This battery is also called a metal-fuel cell from the viewpoint of using oxygen in the atmosphere as a cathode active material and magnesium alloy as a fuel. Since battery performance is determined by the properties of the magnesium alloy used as a anode, more research and development of the magnesium alloy electrode as a anode material are required in order to commercialize it as a high-performance battery. In this study, the commercialized magnesium alloys(AZ31, AZ61) were selected and then electrochemical measurements and discharge test were conducted. Electrochemical properties of magnesium alloys were investigated by OCP changes, Tafel parameters and CV measurement, and the feasibilities of AZ61 alloy with excellent discharge capacity(1410mAhg^-1) as electrode materials were evaluated through CC discharge experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.83-88
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• 2022

Internet of Things (hereinafter referred to as IoT) related technologies are continuously developing in line with the recent development of information and communication technologies. IoT system sends and receives unique data through network based on various sensors. Data generated by IoT systems can be defined as big data in that they occur in real time, and that the amount is proportional to the amount of sensors installed. Until now, IoT systems have applied data storage, processing and computation through centralized processing methods. However, existing centralized processing servers can be under load due to bottlenecks if the deployment grows in size and a large amount of sensors are used. Therefore, in this paper, we propose a distributed processing system for applying a data importance-based algorithm aimed at the high availability of the system to efficiently handle real-time sensor data arising in IoT environments.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.521-526
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• 2023

Due to the recent pandemic, most educational systems are being conducted through online classes. Unlike face-to-face classes, it is even more difficult for learners to maintain concentration, and evaluating the learners' attitude toward the class is also challenging. In this paper, we proposed a real-time concentration-based review support system for learners in real-time video lectures that can be used in online classes. This system measured the learner's face, pupils, and user activity in real-time using the equipment used in the existing video system, and delivers real-time concentration measurement values to the instructor in various forms. At the same time, if the concentration measurement value falls below a certain level, the system alerted the learner and records the timestamp of the lecture. By using this system, instructors can evaluate the learners' participation in the class in real-time and help to improve their class abilities.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.

• Convergence Security Journal
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• v.22 no.1
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• pp.29-38
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• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.135-141
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• 2018

In this paper, we describe design and implementation of optimal smart home control system. Recent developments in technologies such as sensors and communication have enabled the Internet of Things to control a wide range of objects, such as light bulbs, socket-outlet, or clothing. Many businesses rely on the launch of collaborative services between them. However, traditional IoT systems often support a single protocol, although data is transmitted across multiple protocols for end-to-end devices. In addition, depending on the manufacturer of the Internet of things, there is a dedicated application and it has a high degree of complexity in registering and controlling different IoT devices for the internet of things. ARIoT system, special marking points and edge extraction techniques are used to detect objects, but there are relatively low deviations depending on the sampling data. The proposed system implements an IoT gateway of object based on OneM2M to compensate for existing problems. It supports diverse protocols of end to end devices and supported them with a single application. In addition, devices were learned by using deep learning in the artificial intelligence field and improved object recognition of existing systems by inference and detection, reducing the deviation of recognition rates.

• Journal of Ecology and Environment
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• v.46 no.3
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• pp.154-160
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• 2022

Background: The mango is one of the essential fruit trees for the economy of Thailand. Mango pollination relies primarily on insects. Other external forces, such as wind, are less efficient since pollen is sticky and aggregating. There is only one report from Thailand on the use of bees as mango pollinators. The study of the behavior and pollination efficiency of honey bees (Apis mellifera) and stingless bees (Tetragonula laeviceps species complex) was conducted in Nam Dokmai mango plantings in Phrao and Mae Taeng districts, Chiang Mai province, between February and March 2019. Results: Our results reveal that the honey bees commenced foraging earlier than the stingless bee. The number of flowers visited within 1 minute by honey bees was higher than that visited by stingless bees. The average numbers of honey bees and stingless bees that flew out of the hive per minute from 7 a.m. and 6 p.m. in the Phrao district were 4.21 ± 1.62 and 9.88 ± 7.63 bees/min, respectively, i.e., higher than those observed in Mae Taeng, which were 3.46 ± 1.13 and 1.23 ± 1.20 bees/min, respectively. The numbers of fruits per tree were significantly higher in the honey bee and stingless bee treatments (T1 and T2) than in the open pollination treatment (T3). The number of fruits between T1 and T2 treatments was not different. In the pollinator exclusion treatment (T4), no fruit was produced. Fruit size factors were not significantly different among T1, T2, and T3 treatments. Conclusions: Our results showed that insect pollination is crucial for mango production, especially with the Nam Dokmai variety in Northern Thailand. As pollinator exclusion treatment showed no fruit set, and pollinator treatment significantly increased the fruit sets compared to open access plots, a managed pollinator program would benefit the mango growers for better productivity. Both the honey bee and the stingless bee were shown to be effective as pollinators.

• Food Science and Preservation
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• v.9 no.3
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• pp.282-286
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• 2002

This study was conducted to improve the storability of onion bulbs by loading method under room temperature and to reduce the rot caused by field open storage. Allium cepa cv. Changnyungdeago, late strain, was used for the test at the storage condition of 1-row-6-stairs, 2-rows-6-stairs, 4-rows-6-stairs, 1-rows-8-stairs, 2-rows-8-stairs, and 4-rows-8-stairs. The results obtained art as follows: The mean temperature was maintained lowly 1.6∼3.2$\^{C}$ in 1-row-6-stairs and 1.3∼2.6$\^{C}$ in 2-rows-6-stairs in contrast to 4-rows-8-stairs and the relative humidity was high when loading rows increased. The rotting rates in 1-row-6-stairs, 2-rows-6-stairs, 4-rows-6-stairs, 1-rows-8-stairs, 2-rows-8-stairs, and 4-rows-8-stairs were 11.4%, 11.6%, 12.4%, 14.6%, 13.9% and 16.6%, respectively, and became higher with increased rows and stairs of loading. Total weight loss of onion bulbs were l2.2%, 12.7%, 13.8%, 15.5%, 15.2% and 18.0% in 1-row-6-stairs, 2-rows-6-stairs, 4-rows-6-stairs, 1-row-8-stairs, 2-rows-8-stairs, and 4-rows-8-stairs, respectively. The rot of onion bulbs was caused mainly by Fusarium sp., Aspergilus sp., Botrytis sp., and bacteria.

• Journal of Plant Biotechnology
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• v.32 no.4
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• pp.243-250
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• 2005

The coat protein gene (AF296280) of the Korean isolate Potato leafroll virus (PLRV) was cloned and the open reading frame (627 bp) was transformed into potato (Solanum tuberosum cv. Superior). Out of seventeen individual transgenic lines, five lines were identified to confer resistance to PLRV through the five generation's selection program in the greenhouse as well as isolated trial field. Successful introduction and genetic stability of coat protein gene in the genome of potato were confirmed by polymerase chain reaction (PCR), Southern blot hybridization and northern blot hybridization. Some of the transgenic lines were highly resistant to PLRV but did not show any resistance to less homologous Potato virus Y (PVY). Our results suggest that the resistance to PLRV is due to homology dependent gene silencing by sense strand coat protein gene. In addition, the results of field test through five generations showed that there were no significant differences comparing to nontransgenic potatoes in the morphological aspect of shoot as well as tuber, Ho remarkable differences were also observed in the major agronomic characters and yields except for the resistance to PLRV.