• Proceedings of the Acoustical Society of Korea Conference
• /
• 1985.10a
• /
• pp.68-71
• /
• 1985

In this paper, Sentence-Sip E Il Ka Gi Seo U1 E Gan Da was spoken by 4 men and 3 see sound is used for the experiment. A/D conversion time is 30 sec. Data are obtained using the microcomputer and compressed by ADPCM Rate of compression is 1/8. Data compressed by ADPCM are synthesized and compared to the original sound. Rate of speech identification is analysed using the sound pressure, white noise. Coding of ADPCM is done for 5bit. As the result of fixing starting voltage by 2.6V. It is acertained that variable value increases in initial speech signal and then process is made by minimum value "3". From the result of processing, synthesized sound is almost eaual to original sound. Minimum values cause distorition, Dummy Head System is used in this experiment.xperiment.

• Preventive Nutrition and Food Science
• /
• v.9 no.1
• /
• pp.29-33
• /
• 2004

Cranberries have long been used by lay people to relieve the symptoms of urinary tract infections. Recent research has determined that the component of cranberry called proanthocyanidin (PAC) is the primary mechanism for inhibiting P-fimbriated E.coli adhesion to uroepithelial cells in vitro. A series of experiments were performed to determine the effects of PAC on growth and adhesion of uropathogenic E. coli and Staphylococcus aureus to urinary catheter material. The results showed that PAC-inhibited binding of Gram positive S. aureus to collagen coated surfaces and significantly decreased the growth of these bacteria. P-fimbriated E.coli did not bind well to the biomaterial and their growth was unaffected by the cranberry extract with the exception of some loss in viability at 1000 $\mu\textrm{g}$/mL after 5 to 18 hours of exposure. This is the first report of the potential for cranberries to interfere with the adhesion and growth of S. aureus, a multi-drug resistant organisms responsible for morbidity and mortality especially in hospitalized patients.

• Nutrition Research and Practice
• /
• v.16 no.6
• /
• pp.801-812
• /
• 2022

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.4
• /
• pp.789-795
• /
• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2006.11a
• /
• pp.315-316
• /
• 2006

We fabricated GaN nanopores m the etching process of anodic oxidation of aluminum. The aluminum was deposited by using E-beam evaporator on p-type GaN. After the aluminum was anodized GaN structure was exposed to the electric field with the oxidat species. The fabricated nanopore structure provides the enhanced intensity of light emission at the wavelengths 470 nm. We investigated the structure of the GaN nanopores from FE-SEM and EDS measurements.

• Korean Journal of Materials Research
• /
• v.15 no.2
• /
• pp.106-111
• /
• 2005

Effects of stainless steel-making dusts and binder content on compacting $density(\rho)$ and magnetic properties were evaluated. Cores compacted with the mixture of pure Fe powders, $5wt.\%$ dusts and $0.25wt.\%$ binder showed good AC magnetic properties. For example, permeability$({\mu}a)$ and core loss(P) of the cores containing $5wt.\%$ dusts at 500 kHz were 62 and $4008\;{\mu}W/cm^3$, respectively. These properties are almost equivalent to those of competitor's products (i.e, Ancorsteel TC 80 produced by $H\ddot{o}gan\ddot{a}s$ Corp.). The powdered cores obtained from the present work are expected to apply for high-performance soft magnetic components such as normal mode choke filter and pulse transformer.

• Journal of The Korean Astronomical Society
• /
• v.29 no.spc1
• /
• pp.295-298
• /
• 1996

Non-LTE calculations, with the non-thermal ionization effects included, indicated that for electron bombardment, the H$\alpha$ line is widely broadened and shows a strong central reversal. Significant enhancements at the line wings of Ly$\alpha$ and Ly$\beta$ are also predicted at the beginning of the impulsive phase of flares. For the proton bombardment, no strong broadening and no large central reversal are expected. However, due to proton-hydrogen charge exchange, the enhancements at the red wings of Ly$\alpha$ and Ly$\beta$ lines at the early impulsive phase of flares are significant. Our results show that the electron beam can also in some cases generate visible and UV continuum emission in white-light flares. However, at the onset phase, a negative flare may appear within several seconds, due to the increase of the H- opacity. Another spectroscopic signature of energetic particles, i.e. the impact polarization of atomic lines, is also mentioned.

• Electronics and Telecommunications Trends
• /
• v.36 no.4
• /
• pp.145-153
• /
• 2021

Data imputation is a crucial issue in data analysis because quality data are highly correlated with the performance of AI models. Particularly, it is difficult to collect quality time-series data for uncertain situations (for example, electricity blackout, delays for network conditions). Thus, it is necessary to research effective methods of time-series data imputation. Many studies on time-series data imputation can be divided into 5 parts, including statistical based, matrix-based, regression-based, deep learning (RNN and GAN) based methodologies. This study reviews and organizes these methodologies. Recently, deep learning-based imputation methods are developed and show excellent performance. However, it is associated to some computational problems that make it difficult to use in real-time system. Thus, the direction of future work is to develop low computational but high-performance imputation methods for application in the real field.

• Geomechanics and Engineering
• /
• v.32 no.5
• /
• pp.541-551
• /
• 2023

Snap-buckling is one of the main failure modes of structures, because it will lead to the reduction of structural bearing capacity, durability loss and even structural damage. Boundary condition plays an important role in the research of engineering mechanics. Further discussion on the boundary conditions problems will help to analyze the dynamic and static behavior of structures more accurately. Therefore, in order to understand the dynamic and static behavior of curved beams more comprehensively, this paper mainly studies the nonlinear snap-through buckling and forced vibration characteristics of functionally graded graphene reinforced composites (FG-GPLRCs) curved beams with two different boundary conditions (including clamped-hinged and hinged-hinged) using Euler-Bernoulli beam theory (E-BBT). In addition, the effects of the curved beam radius, the GLPs distributions, number of GLPs layers, the mass fraction of GLPs and elastic foundation parameters on the nonlinear snap-through buckling and forced vibration behavior are discussed respectively.

• The Journal of the Korean Society for Microbiology
• /
• v.21 no.1
• /
• pp.47-52
• /
• 1986

A heat-labile enterotoxin and no heat-stable enterotoxin producing($LT^+ST^-$) plasmid (110 kilobases in size) was isolated from an enterotoxigenic Escherichia coli of human strain O15:H11 and used for analysis of the $LT^+$ deoxyrionucleic acid region using recombinant DNA technology. A DNA segment containing the $LT^+$ DNA region which was one restriction endonuclease BamHl fragment(6.2 kb in size) was joind to a small multicopy plasmid, pUC9. E. coli K-12 strain, JM103 harboring the chimeric plasmid produced greater amounts of LT than did the enterotoxigenic E. coli O15:H11 strain. The BamHl fragment was further digested with various restriction endonucleases and contained no HindIll restriction site which is an essential in $LT^+ST^+$ plasmid. The detailed DNA sequencing of this $LT^+ST^-$ plasmid is required.