• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.428-429
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• 2011

Recently, we performed ab initio total energy calculation and tight-binding molecular dynamics (TBMD) simulation to study structures and the reconstruction of native defects in graphene. In the previous study, we predicted by TBMD simulation that a double vacancy in graphene is reconstructed into a 555-777 composed of triple pentagons and triple heptagons [1]. The structural change from pentagon-octagon-pentagon (5-8-5) to 555-777 has been confirmed by recent experiments [2,3] and the detail of the reconstruction process is carefully studied by ab initio calculation. Pentagon-heptagon (5-7) pairs are also found to play an important role in the reconstruction of vacancy in graphene and single wall carbon nanotube [4]. In the TBMD simulation of graphene nanoribbon (GNR), we found the evaporation of carbon atoms from both the zigzag and armchair edges is preceded by the formation of heptagon rings, which serve as a gateway for carbon atoms to escape. In the simulation for a GNR armchair-zigzag-armchair junction, carbon atoms are evaporated row-by-row from the outermost row of the zigzag edge [5], which is in excellent agreement with recent experiments [2, 6]. We also present the recent results on the formation and development of dislocation in graphene. It is found that the coalescence of 5-7 pairs with vacancy defects develops dislocation in graphene and induces the separation of two 5-7 pairs. Our TBMD simulations also show that adatoms are ejected and evaporated from graphene surface due to large strain around 5-7 pairs. It is observed that an adatom wanders on the graphene surface and helps non-hexagonal rings change into stable hexagonal rings before its evaporation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1525-1530
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• 2021

By applying AI-based efficient inventory management and logistics optimization technology using the smart factory's production plan and manufacturing data, the company's productivity improvement and customer satisfaction can be expected to increase. In this paper, we proposed a system that collects data from the factory's production process, stores it in the cloud, and uses the manufacturing data stored there to apply AI-based supply chain optimization technology later. While the existing system supported approximately 10 to 20 data types, the proposed system is designed and developed to support more than 100 data types. In addition, in the case of the collection cycle, data can be collected 1-2 times per second, and data collection in TB units is possible. Therefore This system is designed to be applied to the existing factory of past in addition to the smart factory.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.442-454
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• 2017

As the demand for meat products has steadily increased in Korea, so the livestock industry has full grown. However, the opening of import meat products is taking a toll on the local industry. Cost reduction on livestock feed, which comprises the majority of costs involved in the industry is urgent to gain a competitive edge. As Internet of Things (IoT) technologies are being applied across a multiple of industries, so are the cases of applied Smart Farm technology for efficient production. The following research aims to utilize IoT technologies to measure, in real time, the rate of depletion of feed and remaining amount and to propose an effective automated reorder & delivery system. First, a method of utilization of ultrasonic and temperature/humidity sensors to obtain corresponding data of remaining feed in the Feedbin is proposed. In addition, a method of sending the obtained data via on-the-farm gateway to Supply Chain Management (SCM) servers is proposed. Finally, utilization of the stored data to construct an automated reorder & delivery service system is proposed. It is in the researcher's intention to contribute to and enable the local livestock industry with the application of various IoT services.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.

• 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.