• Membrane Journal
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• v.15 no.4
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• pp.349-354
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• 2005

YSZ (yttria-stabilized zirconia) determined with an electrolyte that analyzed thermal stability along sintering condition and an electric characteristic. As sintering temperature increases by SEM, grain grows and it showed that pore decreases relatively. and confirmed effect by grain size. It evaluated that particle internal resistance and electric performance by resistance in an electrolyte and electricity conductivity measurement through ac impedance measurement in temperature of $800\~1000^{\circ}C$ in 2-probe method In order to recognize an electric characteristic. In dry process and wet process, density was each 6.13, 6.25 $g/cm^3$ and the relative density was each 98, 99$\%$ when sintering condition is $1400^{\circ}C$.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.84-95
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• 2008

Wireless sensor networks have recently emerged as a platform for several applications. By deploying wireless sensor nodes and constructing a sensor network, we can remotely obtain information about the behavior, conditions, and positions of objects in a region. Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable to prolong the lifetime of a sensor network as long as possible. In this paper, we propose a novel clustering algorithm that distributes the energy consumption of a cluster head. First, we analyze the energy consumption if cluster heads and divide each cluster into a collection layer and a transmission layer according to their roles. Then, we elect a cluster head for each layer to distribute the energy consumption of single cluster head. In order to show the superiority of our clustering algorithm, we compare it with the existing clustering algorithm in terms of the lifetime of the sensor network. As a result, our experimental results show that the proposed clustering algorithm achieves about $10%{\sim}40%$ performance improvements over the existing clustering algorithms.

• Membrane Journal
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• v.16 no.4
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• pp.252-258
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• 2006

A numerical analysis was performed for a separation process of carbon dioxide from a flue gas stream using polyethersulfone hollow fiber membranes. Countercurrent flow governing equations were regarded to be two point boundary-value problem and the nonlinear ordinary differential equation were simultaneously solved using the finite- difference method. A computer program was developed using the Compaq Visual Fortran 6.6 software. The carbon dioxide permeate driving force and the fred gas residence time at the inside of membrane were found to be very important factors affecting the permeation characteristics of carbon dioxide. The carbon dioxide concentration in the permeate and the flow rate of the permeate were found to be slightly larger by a few percent with a countercurrent flow analysis than those with a cocurrent flow analysis.

• Membrane Journal
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• v.31 no.4
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• pp.268-274
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• 2021

One of the chronic problems in the issue of global warming is the emission of greenhouse gases. Carbon dioxide (CO₂), which accounts for the highest proportion of various greenhouse gases, has been continuously researched by humans to separate it. From this point of view, a poly(vinyl alcohol) (PVA)-based copolymer with acrylic acid monomer was utilized in a gas separation membrane in this study. We employed a free radical polymerization to fabricate PVA-g-PAA (VAA) graft copolymer. It was utilized in the form of a composite membrane on a polysulfone substrate. The proper amount of acrylic acid reduced the crystallinity of PVA and increased CO₂ solubility in separation membranes. In this perspective, we suggest the novel approach in CO₂ separation membrane area by grafting and solution-diffusion.

• Membrane Journal
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• v.30 no.6
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• pp.433-442
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• 2020

In this study, in order to build a molecular dynamics simulation model of ionomer for water electrolysis, an ionomer model that reflects the characteristics of a water electrolysis system in which excess water molecules exist was compared to an ionomer built according to the conventional simulation method of the fuel cells membrane. The final ionomer MD models have a strong phase separation and water channel that is one of the important characteristics of the perfluorinated ionomer, and are stable and water-insoluble under excessive water and high temperature conditions. In the ionomer MD models built in this study, the excess water molecules decrease an ion conductivity due to the dilution of ions, but increase a hydrogen diffusivity. Therefore, it is necessary to design the molecular structure of ionomers for water electrolysis in experimental studies as well as molecular dynamics studies according to the characteristics of the water electrolysis system reported in this study.

• Membrane Journal
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• v.33 no.4
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• pp.201-210
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• 2023

Dust filter membranes play a crucial role in human life and various industries, as they contribute to several important aspects of human health, safety, and environmental protection. This study presents the development of a polysulfone@polyphenylene sulfide/polytetrafluoroethylene (PSf@PPS/ePTFE) composite dust filter membrane with excellent thermal stability and adhesion properties for high-temperature conditions. FT-IR analysis confirms successful impregnation of PSf adhesive onto PPS fabric and interaction with ePTFE support. FE-SEM images reveal improved fiber interconnection and adhesion with increased PSf concentration. PSf@PPS/ePTFE-5 exhibits the most suitable porous structure. The composite membrane demonstrates exceptional thermal stability up to 400℃. Peel resistance tests show sufficient adhesion for dust filtration, ensuring reliable performance under tough, high-temperature conditions without compromising air permeability. This membrane offers promising potential for industrial applications. Further optimizations and applications can be explored.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.9-15
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• 2023

Metaverse is a modern new technology that is advancing quickly. The goal of this study is to investigate this technique from the perspective of computer vision as well as general perspective. A thorough analysis of computer vision related Metaverse topics has been done in this study. Its history, method, architecture, benefits, and drawbacks are all covered. The Metaverse's future and the steps that must be taken to adapt to this technology are described. The concepts of Mixed Reality (MR), Augmented Reality (AR), Extended Reality (XR) and Virtual Reality (VR) are briefly discussed. The role of computer vision and its application, advantages and disadvantages and the future research areas are discussed.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Membrane Journal
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• v.28 no.2
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• pp.121-128
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• 2018

Terpolymers, which are chemical compounds composed of three different chemical compounds, have rarely been utilized for gas separation membranes. In this study, we demonstrate a simple process to fabricate a composite membrane for $CO_2/N_2$ separation based on a terpolymer synthesized from poly(2-[3-(2H-benzotriazol-2-yl)-4-hydroxyphenyl] ethylmethacrylate)(PBEM), poly(oxyethylene methacrylate)(POEM), and methyl methacrylate (MMA) via free radical polymerization. A solution of the as-synthesized PBEM-PMMA-POEM was coated onto a microporous polysulfone (PSf) support to form a composite membrane. The successful polymerization and the characteristics and morphology of the membrane were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) analysis, thermogravimetric analysis (TGA), and field emission scanning electron microscopy (FE-SEM). The gas permeance and $CO_2/N_2$ selectivity of the PBEM-PMMA-POEM terpolymer membrane were measured at $25^{\circ}C$. A maximum $CO_2/N_2$ selectivity of 30.2 was obtained at a $CO_2$ permeance of 57.4 GPU ($1GPU=10^{-6}cm^3$(STP)/($s\;cm^2\;cmHg$)).

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.41-49
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• 2019

An end-to-end speech recognition model consisting of a single integrated neural network model was recently proposed. The end-to-end model does not need several training steps, and its structure is easy to understand. However, it is difficult to understand how the model recognizes speech internally. In this paper, we visualized and analyzed the attention-based end-to-end model to elucidate its internal mechanisms. We compared the acoustic model of the BLSTM-HMM hybrid model with the encoder of the end-to-end model, and visualized them using t-SNE to examine the difference between neural network layers. As a result, we were able to delineate the difference between the acoustic model and the end-to-end model encoder. Additionally, we analyzed the decoder of the end-to-end model from a language model perspective. Finally, we found that improving end-to-end model decoder is necessary to yield higher performance.