• Advances in nano research
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• v.16 no.4
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• pp.353-363
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• 2024

The incorporation of nanoplatelets in composite and polymeric materials represents a recent and innovative approach, holding substantial promise for diverse property enhancements. This study focuses on the application of nanocomposites in the production of sports equipment, particularly soccer balls, aiming to bridge the gap between theoretical advancements and practical implications. Addressing the longstanding challenge of suboptimal interaction between carbon nanofillers and epoxy resin in epoxy composites, this research pioneers inventive solutions. Furthermore, the investigation extends into unexplored territory, examining the integration of glass fiber/epoxy composites with nanoparticles. The incorporation of nanomaterials, specifically expanded graphite and graphene, at a concentration of 25.0% by weight in both the epoxy structure and the composite with glass fibers demonstrates a marked increase in impact resistance compared to their nanomaterial-free counterparts. The research transcends laboratory experiments to explore the practical applications of nanocomposites in the design and production of sports equipment, with a particular emphasis on soccer balls. Analytical techniques such as infrared spectroscopy and scanning electron microscopy are employed to scrutinize the surface chemical structure and morphology of the epoxy nanocomposites. Additionally, an in-depth examination of the thermal, mechanical, viscoelastic, and conductive properties of these materials is conducted. Noteworthy findings include the efficacy of surface modification of carbon nanotubes in preventing accumulation and enhancing their distribution within the epoxy matrix. This optimization results in improved interfacial interactions, heightened thermal stability, superior mechanical properties, and enhanced electrical conductivity in the nanocomposite.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.640-655
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• 2014

Many of today's video systems have additional depth camera to provide extra features such as 3D support. Thanks to these changes made in multimedia system, it is now much easier to obtain depth information of the video. Depth information can be used in various areas such as object classification, background area recognition, and so on. With depth information, we can achieve even higher coding efficiency compared to only using conventional method. Thus, in this paper, we propose the 2D video coding algorithm which uses depth information on top of the next generation 2D video codec HEVC. Background area can be recognized with depth information and by performing HEVC with it, coding complexity can be reduced. If current CU is background area, we propose the following three methods, 1) Earlier stop split structure of CU with PU SKIP mode, 2) Limiting split structure of CU with CU information in temporal position, 3) Limiting the range of motion searching. We implement our proposal using HEVC HM 12.0 reference software. With these methods results shows that encoding complexity is reduced more than 40% with only 0.5% BD-Bitrate loss. Especially, in case of video acquired through the Kinect developed by Microsoft Corp., encoding complexity is reduced by max 53% without a loss of quality. So, it is expected that these techniques can apply real-time online communication, mobile or handheld video service and so on.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1147-1155
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• 2014

Biomechanical models are often used to predict muscle and joint forces in the human body. For estimation of muscle forces, the body and muscle properties have to be known. However, these properties are difficult to measure and differ from person to person. Therefore, it is necessary to predict the change in muscle forces depending on the body and muscle properties. The objective of the present study is to develop a numerical procedure for estimating the muscle forces in the human lower extremity under uncertainty of body and muscle properties during rising motion from a seated position. The human lower extremity is idealized as a multibody system in which eight Hill-type muscle force models are employed. Each model has four degrees of freedom and is constrained in the sagittal plane. The eight muscle forces are determined by minimizing the metabolic energy consumption during the rising motion. Uncertainty analysis is performed using a first-order reliability method. The one-standard-deviation range of agonistic muscle forces is calculated to be about 150-300 N.

• Journal of IKEEE
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• v.23 no.4
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• pp.1440-1447
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• 2019

Currently, the prevalence of autism spectrum disorders in children is reported to be higher and shows various types of disorders. In particular, they are having difficulty in communication due to communication impairment in the area of social communication and need to be improved through training. Thus, this study proposes a method of acquiring voice information through a microphone mounted on a robot designed through preliminary research and using this information to make intelligent motions. An ANN(Artificial Neural Network) was used to classify the speech data into robot motions, and we tried to improve the accuracy by combining the Recurrent Neural Network based on Convolutional Neural Network. The preprocessing of input speech data was analyzed using MFCC(Mel-Frequency Cepstral Coefficient), and the motion of the robot was estimated using various data normalization and neural network optimization techniques. In addition, the designed ANN showed a high accuracy by conducting an experiment comparing the accuracy with the existing architecture and the method of human intervention. In order to design robot motions with higher accuracy in the future and to apply them in the treatment and education environment of children with autism.

• Progress in Medical Physics
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• v.13 no.4
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• pp.224-228
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• 2002

Myocardial tagging technique such as spatial modulation of magnetization (SPAMM) allows the study of myocardial motion with high accuracy. However, the accuracy of the estimation of tag intersection can be affected by tagline spacing. The aim of this study was to investigate the relationship between tagline spacing of SPAMM image and tagging contrast-to-noise ratio (CNR) in in-vivo study. Two healthy volunteers were undergone electrocardiographically triggered MR imaging with SPAMM-based tagging pulse sequence at a 1.5T MR scanner. Horizontally modulated stripe patterns were imposed with a range from 3.6 to 9.6 mm of tagline spacing. Images of the left ventricle(LV) wall were acquired at the mid-ventricle level during cardiac cycle with FE-EPI (TR/TE = 5.8/2.2 msec, FA= 10$^{\circ}$. Tagging CNR for each image was calculated with a software which developed in our group. During contraction, tagging CNR was more rapidly decreased in case of narrow tagline spacing than in case of wide tagline spacing. In the same heart phase, CNR was increased corresponding with tagline spacing. Especially, at the fully contracted heart phase, CNR was more rapidly increased than the other heart phases as a function of tagline spacing. The results indicated that the optimization of tagline spacing provides better tagging CNR in order to analyze the myocardial motion more accurately.

• Progress in Medical Physics
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• v.25 no.2
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• pp.79-88
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• 2014

The dose distributions within the real volumes of tumor targets and critical organs during internal target volume-based intensity-modulated radiation therapy (ITV-IMRT) for liver cancer were recalculated by applying the effects of actual respiratory organ motion, and the dosimetric features were analyzed through comparison with gating IMRT (Gate-IMRT) plan results. The ITV was created using MIM software, and a moving phantom was used to simulate respiratory motion. The doses were recalculated with a 3 dose-volume histogram (3DVH) program based on the per-field data measured with a MapCHECK2 2-dimensional diode detector array. Although a sufficient prescription dose covered the PTV during ITV-IMRT delivery, the dose homogeneity in the PTV was inferior to that with the Gate-IMRT plan. We confirmed that there were higher doses to the organs-at-risk (OARs) with ITV-IMRT, as expected when using an enlarged field, but the increased dose to the spinal cord was not significant and the increased doses to the liver and kidney could be considered as minor when the reinforced constraints were applied during IMRT plan optimization. Because the Gate-IMRT method also has disadvantages such as unsuspected dosimetric variations when applying the gating system and an increased treatment time, it is better to perform a prior analysis of the patient's respiratory condition and the importance and fulfillment of the IMRT plan dose constraints in order to select an optimal IMRT method with which to correct the respiratory organ motional effect.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.5
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• pp.242-248
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• 2002

It is important to develop new effective technologies to increase the interruption capacity and to reduce the size of a UB(Gas Circuit Breakers). Major design parameters such as nozzle geometries and interrupting chamber dimensions affect the cooling of the arc and the breaking performance. But it is not easy to test real GCB model in practice as in theory. Therefore, a simulation tool based on a computational fluid dynamics(CFD) algorithm has been developed to facilitate an optimization of the interrupter. Special attention has been paid to the supersonic flow phenomena between contacts and the observation of hat-gas flow for estimating the breaking performance. However, there are many difficult problems in calculating the flow characteristics in a GCB such as shock wave and complex geometries, which may be either static or in relative motion. Although a number of mesh generation techniques are now available, the generation of meshes around complicated, multi-component geometries like a GCB is still a tedious and difficult task for the computational fluid dynamics. This paper presents the CFD program using CFB-CAD integration technique based on Cartesian cut-cell method, which could reduce researcher's efforts to generate the mesh and achieve the accurate representation of the geometry designed by a CAD tools.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1183-1190
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• 1999

This paper presents an efficient hardware architecture to improve the frame memory interface occupying the largest hardware area together with motion estimator in implementing MPEG-2 video encoder as an ASIC chip. In this architecture, the memory size for internal data buffering and hardware area for frame memory interface control logic are reduced through the efficient memory map organization of the external SDRAM having dual bank and memory access timing optimization between the video encoder and external SDRAM. In this design, 0.5 m, CMOS, TLM (Triple Layer Metal) standard cells are used as design libraries and VHDL simulator and logic synthesis tools are used for hardware design add verification. The hardware emulator modeled by C-language is exploited for various test vector generation and functional verification. The architecture of the improved frame memory interface occupies about 58% less hardware area than the existing architecture[2-3], and it results in the total hardware area reduction up to 24.3%. Thus, the (act that the frame memory interface influences on the whole area of the video encoder severely is presented as a result.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.223-236
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• 2012

We develop a video processing method that allows the more advanced human perception oriented video coding. The proposed method necessarily reflects all influences by the rate-distortion based optimization and the human visual perception that is affected by the visual saliency, the limited space-time resolution and the regional moving history. For reflecting the human perceptual effects, we devise an online moving pattern classifier using the Hedge algorithm. Then, we embed the existing visual saliency into the proposed moving patterns so as to establish a human visual perception model. In order to realize the proposed human visual perception model, we extend the conventional foveation filtering method. Compared to the conventional foveation filter only smoothing less stimulus video signals, the developed foveation filter can locally smooth and enhance signals according to the human visual perception without causing any artifacts. Due to signal enhancement, the developed foveation filter more efficiently transfers the bandwidth saved at smoothed signals to the enhanced signals. Performance evaluation verifies that the proposed video processing method satisfies the overall video quality, while improving the perceptual quality by 12%~44%.