• International journal of advanced smart convergence
• /
• 제12권4호
• /
• pp.142-146
• /
• 2023

We present a method for generating 3D structures and rendering objects by combining VAE (Variational Autoencoder) and GAN (Generative Adversarial Network). This approach focuses on generating and rendering 3D models with improved quality using residual learning as the learning method for the encoder. We deep stack the encoder layers to accurately reflect the features of the image and apply residual blocks to solve the problems of deep layers to improve the encoder performance. This solves the problems of gradient vanishing and exploding, which are problems when constructing a deep neural network, and creates a 3D model of improved quality. To accurately extract image features, we construct deep layers of the encoder model and apply the residual function to learning to model with more detailed information. The generated model has more detailed voxels for more accurate representation, is rendered by adding materials and lighting, and is finally converted into a mesh model. 3D models have excellent visual quality and accuracy, making them useful in various fields such as virtual reality, game development, and metaverse.

• 한국복합재료학회:학술대회논문집
• /
• 한국복합재료학회 2002년도 추계학술발표대회 논문집
• /
• pp.52-55
• /
• 2002

This paper examines the effective thermal conductivity of 3-D braided glass/epoxy composites. 3-D braided composites have a number of advantage over conventional laminate composites, including through-thickness reinforcement, and high damage tolerance and processability. The thermal properties of composites depend primarily on the microstructure of the braided preform and properties of constituent materials. A thermal resistance network model based on structure of the braided preform is proposed by using thermal-electrical analogy. In order to affirm the applicability theses solutions, thermal conductivities of 3-D braided glass/epoxy composites are measured

• 한국정보통신학회논문지
• /
• 제23권11호
• /
• pp.1482-1484
• /
• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.

• Journal of information and communication convergence engineering
• /
• 제12권3호
• /
• pp.135-139
• /
• 2014

Opportunistic routing was originally introduced in various multihop network environments to reduce the number of hops in such a way that, among the relays that decode the transmitted packet for the current hop, the one that is closest to the destination becomes the transmitter for the next hop. Unlike the conventional opportunistic routing case where there is a single active S-D pair, for an ad hoc network in the presence of fading, we investigate the performance of parallel opportunistic multihop routing that is simultaneously performed by many source-destination (S-D) pairs to maximize the opportunistic gain, thereby enabling us to obtain a logarithmic gain. We first analyze a cut-set upper bound on the throughput scaling law of the network. Second, computer simulations are performed to verify the performance of the existing opportunistic routing for finite network conditions and to show trends consistent with the analytical predictions in the scaling law. More specifically, we evaluate both power and delay with respect to the number of active S-D pairs and then, numerically show a net improvement in terms of the power-delay trade-off over the conventional multihop routing that does not consider the randomness of fading.

• 한국멀티미디어학회논문지
• /
• 제21권5호
• /
• pp.626-637
• /
• 2018

Human exchanges information not only through words, but also through body gesture or hand gesture. And they can be used to build effective interfaces in mobile, virtual reality, and augmented reality. The past 2D gesture recognition research had information loss caused by projecting 3D information in 2D. Since the recognition of the gesture in 3D is higher than 2D space in terms of recognition range, the complexity of gesture recognition increases. In this paper, we proposed a real-time gesture recognition deep learning model and application in 3D space using deep learning technique. First, in order to recognize the gesture in the 3D space, the data collection is performed using the unity game engine to construct and acquire data. Second, input vector normalization for learning 3D gesture recognition model is processed based on deep learning. Thirdly, the SELU(Scaled Exponential Linear Unit) function is applied to the neural network's active function for faster learning and better recognition performance. The proposed system is expected to be applicable to various fields such as rehabilitation cares, game applications, and virtual reality.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2001년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
• /
• pp.125-127
• /
• 2001

Switched reluctance motor (SRM) has some advantages such as low cost, high torque density etc. However SRM has inevitably high torque ripple due to the double salient structure. To apply SRM to industrial field, we have to minimize torque ripple, which is the weak-Point of SRM. This paper presents optimal design process of SRM using numerical method such as 2D finite element method (FEM) and 3D equivalent magnetic circuit network method (EMCNM). The electrical and geometrical design parameters have been adopted as 2D design variables. The overhang structure of rotor has been also adopted as 3D design variable. From this work, we can obtain the optimal design, which minimize the torque ripple and maximize energy conversion loop.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2006년도 하계종합학술대회
• /
• pp.175-176
• /
• 2006

본 논문은 IMS 개발-에 있어서 컨버전스 서비스를 지향하는 망 진화 방향을 제안한다. 4 세대 라고 언급하고 있는 4G 네트워크의 핵심인 IMS 는 3GPP 에서 표준화를 진행하며, ITU-T, MSF, TISPAN 과 같은 표준화 단체에서도 유사한 망구조로 제안하고 있다. 그러나 IMS망을 통하여 생성되는 컨버전스 서비스로의 발전에는 고려해야 할 사항들이 많이 있다. 본 논문에서는 망구조를 통하여 단말의 입장과 서비스의 진화방향을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권11호
• /
• pp.2924-2944
• /
• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Journal of electromagnetic engineering and science
• /
• 제12권2호
• /
• pp.155-160
• /
• 2012

A wideband circularly polarized pinwheel-shaped planar monopole antenna fed by a wideband feeding network is presented in this paper. The proposed antenna is formed by four wideband planar monopole antenna elements with aquadruple feeding network in order to improve the performance of circular polarization. Additionally, the antenna, which is introduced here, has a high gain in the z axis direction because of its folded antenna structure. The attractive characteristics of the proposed antenna are the wide impedance bandwidth of 87.3 % (1 GHz to 2.55 GHz), the 3 dB axial ratio (AR) bandwidth of 92.3 % (1.05 GHz to 2.85 GHz), and the maximum gain within the 3 dB AR bandwidth is about 8.24 dBic.

• 한국정보통신학회논문지
• /
• 제25권10호
• /
• pp.1452-1455
• /
• 2021

Recently, point clouds are generated by capturing real space in 3D, and it is actively applied and serviced for performances, exhibitions, education, and training. These point cloud data require post-correction work to be used in virtual environments due to errors caused by the capture environment with sensors and cameras. In this paper, we propose an enhancement technique for 3D point cloud data by applying generative adversarial network(GAN). Thus, we performed an approach to regenerate point clouds as an input of GAN. Through our method presented in this paper, point clouds with a lot of noise is configured in the same shape as the real object and environment, enabling precise interaction with the reconstructed content.