• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.10
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• pp.817-820
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• 2018

In this letter, a compact multiple-input multiple-output(MIMO) antenna design for a 2.4 GHz wireless local area network(WLAN) band is proposed for use in smart glasses. To miniaturize the MIMO antenna system, a ground plane is employed within the antenna and a T-shaped ground is proposed. To achieve wideband isolation, dual resonance is formed by the ground mode. One resonance is created by the T-shaped ground and the second resonance is created by adding a slot and a capacitor between the two feed lines. The measurements show that the reflection coefficient characteristic was less than -5.1 dB, whereas the isolation obtained was less than -20 dB. The diversity performance was evaluated using the measured two-dimensional radiation patterns, and the envelope correlation coefficient(ECC) values achieved in the target band(2.4~2.5 GHz) were less than 0.1.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.791-793
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• 2015

Recently, UCC (User Created Contents) and VoD (Video on Demand), and multimedia content are growing, IP-TV, Smart TV, OHTV (Open Hybrid TV) various services such as multi platform (Multi-platform) environment, services and QoS issues. To solve this problem, the network efficiently, and improve the quality of content is necessary for the system. In this paper, the network of channels State and transmission of multimedia data based on dynamic resource usage, TCP and UDP, Adaptive dual-streaming system used for design and analysis. In addition, the existing TCP and UDP streaming system using a single protocol for analysis and verification of the effectiveness of the difference between and. This is a disaster, and medical/first aid system will be utilized in the field of feed, are ubiquitous.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.323-331
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• 2013

An extreme learning machine (ELM) is an efficient learning algorithm that is based on the generalized single, hidden-layer feed-forward networks (SLFNs), which perform well in classification applications. Many studies have demonstrated its superiority over the existing classical algorithms: support vector machine (SVM) and BP neural network. This paper presents a novel face recognition approach based on a multi-class project extreme learning machine (MPELM) classifier and 2D Gabor transform. First, all face image features were extracted using 2D Gabor filters, and the MPELM classifier was used to determine the final face classification. Two well-known face databases (CMU-PIE and ORL) were used to evaluate the performance. The experimental results showed that the MPELM-based method outperformed the ELM-based method as well as other methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.5
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• pp.75-80
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• 2015

Nowadays, flat-shaped cover glass is widely used for mobile devices. However, for its good design and convenience of use, curved cover glass has been demanded. Thus, many companies have tried to produce curved cover glass through the shaving technique, but the production efficiency is very low. Therefore, the molding technique has been adopted to increase the efficiency for the curved-glass production system. For a glass-molding system, several heating blocks are installed, and the flat cover glass is sequentially heated and molded. The production time for the cover glass is very different depending on the heating conditions; thus, the prediction of the production time for different heating conditions should be needed. Therefore, in this study, the computations were performed with different heating conditions (uniform and non-uniform) in the present cover glass-molding machine. For uniform and non-uniform heating conditions, the simple correlation between the heating time and the heater capacity and the heating time to achieve higher durability can be suggested, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1379-1386
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• 2018

Conventional underwater cameras used in fish farms can only shoot limited areas and are vulnerable to underwater contamination. There is also a problem with contaminated farms as surplus residues are deposited as a result of feed supply to farms' nets. This paper proposes underwater drones for underwater monitoring of fish farms and cleaning nets. If underwater drones are used for management of fish farms, underwater imaging, monitoring and cleaning of fish farms' nets can be possible. By using this technology, data can be collected by detecting changes in the environment of a fish farm and responding to changes that occur within a fish farm based on the data. In addition, the establishment of an integrated control system will enable to build efficient and stable smart farms.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.123-129
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• 2022

Quartz (SiO2) has high abrasion and heat resistances and excellent chemical and mechanical properties; therefore, it is used in various industries, such as machinery, chemistry, optics, and medicine. Quartz is a high-hardness and brittle material and is classified as the topmost difficult-to-cut material, which is because of the cracking or chipping at the edge during processing. Corner wear, such as cracks and chippings that occur during cutting, is a major cause for the deterioration in the machining quality. Therefore, many researchers are investigating various techniques to process quartz effectively. However, owing to the mechanical properties of quartz, most studies have been conducted on grinding, micromachining, and microdrilling. Few studies have been conducted on quartz processing. The purpose of this study was to analyze the machining characteristics according to the machining factors during the slot machining of quartz using a cubic boron nitride (CBN) tool and to select the optimal machining conditions using the Taguchi method. The machining experiment was performed considering three process variables: the spindle speed, feed rate, and depth of cut. The cutting force and surface roughness were analyzed according to the processing conditions.

• Smart Structures and Systems
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• v.29 no.2
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• pp.337-350
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• 2022

This paper focuses on predicting the post-heating mechanical properties of cementitious composites reinforced with multi-scale additives using the Artificial Neural Network (ANN) approach. A total of four different feed-forward ANN models are developed using 261 data sets collected from 18 published sources. The models are optimized using 12 input parameters selected based on a comprehensive literature review to predict the residual compressive strength, the residual flexural strengths, elastic modulus, and fracture energy of heat-damaged cementitious specimens. Furthermore, the ANN is employed to predict the impact of several variables including; the content of polypropylene (PP) microfibers and carbon nanotubes (CNTs) used in the concrete, mortar, or paste mix design, length of PP fibers, the average diameter of CNTs, and the average length of CNTs. The influence of the studied parameters is investigated at different heating levels ranged from 25℃ to 800℃. The results demonstrate that the developed ANN models have a strong potential for predicting the mechanical properties of the heated cementitious composites based on the mixing ingredients in addition to the heating conditions.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.132-141
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• 2022

We look at the rocket life stages of NaverZ's metaverse platform scaling and investigate the ignition and scale-up stage of its metaverse platform brand, Zepeto based on the Rocket Model (RM). The results are derived as follows: Firstly, NaverZ shows the event strategy by collaborating with K-pops, the piggybacking strategy by utilizing other SNSs, and the VIP strategy by investing in game and entertainment content genres in the 'attract' function. In the second 'match' function, based on the matching rule of Zepeto, the users can generate their own characters and "World" with Zepeto Studio. However, for strengthening the matching quality, NaverZ is investing in the artificial intelligence (AI) based companies consistently. In the 'connect' function, NaverZ's maximization of the positive interaction is possible by inducing feed activities in Zepeto & other SNSs and by uploading attractive content for viral effects in the ignition. For facilitating this, NaverZ expands the scale to other continents like Southeast Asia and Middle East with the localization strategy inclusive investment. Lastly, in the 'transact' function, based on three monetization experiments like Coin & ZEM, user generated content (UGC) fee, and advertising revenue in the ignition, NaverZ starts to invest in NFT platforms and abroad blockchain companies.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.