• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.62-68
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• 2022

Due to medical and economic development, various treatment methods are being studied to restore or maintain beautiful and healthy teeth. In particular, interest in aesthetic treatment procedures such as prosthetic treatment and whitening to restore tooth loss is increasing. One of the important things in the field of prosthetics and esthetic treatment is to determine the correct color of teeth because harmony with natural teeth is an important factor in determining the perfection of esthetic prostheses. This study is about the development of a colorimetry application for tooth colorimetry using a smartphone camera. The colorimetry application UI was designed, the colorimetry algorithm was derived and the application was implemented, and the validity of the application was verified through testing the implemented application.

• Applied Chemistry for Engineering
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• v.21 no.2
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• pp.154-161
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• 2010

In the study, the effect of acid and salt concentrations during the production of bio-ethanol from various brwon-algae raw materials was investigated. Especially, the possibility of the conversion of various polysaccarides contained in Laminaria japonica was studied. Bio-ethanol was produced by Saccharomyces cerevisiae KCCM1129 strains in Laminaria japonica. The maximum bio-ethanol production of 2.09 g/L using heat-treatment of Laminaria japonica was achieved. The optimum concentration for reducing sugar conversion by Laminaria japonica was found to be 3.95 g/L at the HCl concentration of 0.1 N. But bio-ethanol production was higher than the case without the non-acid pretreatment. Among the various polysaccharides, only mannitol produced maximum 3.09 g/L bio-ethanol. In case of laminaran, the ethanol was produced only at 0.15 g/L only in 0.1 N HCl pretreatment medium and cell growth was higher than other pretreatment.

• Journal of Pharmaceutical Investigation
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• v.41 no.3
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• pp.147-151
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• 2011

This paper demonstrates the development of a method for preparing micropatterned microdiscs in order to increase contact area with cells and to change the release pattern of drugs. The microdiscs were manufactured with hot embossing, where a polyurethane master structure was pressed onto both solid and porous microparticles made of polylactic-co-glycolic acid at various temperatures to form a micropattern on the microdiscs. Flat microdiscs were formed by hot embossing of porous microparticles; the porosity allowed space for flattening of the microdiscs. Three types of micro-grooves were patterned onto the flat microdiscs using prepared micropatterned molds: (1) 10 ${\mu}M$ deep, 5 ${\mu}M$ wide, and spaced 2 ${\mu}M$ apart; (2) 10 ${\mu}M$ deep, 9 ${\mu}M$ wide, and spaced 5 ${\mu}M$ apart; and (3) 10 ${\mu}M$ deep, 50 ${\mu}M$ wide, and spaced 50 ${\mu}M$ apart. This novel microdisc preparation method using hot embossing to create micropatterns on flattened porous microparticles provides the opportunity for low-cost, rapid manufacture of microdiscs that can be used to control cell adhesion and drug delivery rates.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.322-327
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• 2020

Recently, a research on obtaining a vital signal using a Doppler radar has been developed and is used as a technology applied to patients in bed. However, in the case of the measured pulse, the respiration signal is generated as noise, resulting in a problem of lowering accuracy. In this paper, we propose a bio-signal measurement algorithm based on the sampling point to improve the accuracy of the signal for measuring the pulse rate when measuring bio-signals using a Doppler radar. The proposed algorithm improves the accuracy of the measured bio-signal by removing noise generated when measuring biosignals based on two sampling points. Compared with actual medical equipment and existing bio-signal algorithms, it is more than 90% similar to medical equipment. In addition, it was confirmed that severe amplitude change was minimized compared to the existing algorithm.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.242-247
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• 2017

This study presents a bio-chemical energy harvesting system which can generate electric power from bioorganic substance contained in vermicompost. It produced electricity by inoculating microbial fuel cell(MFC) with earthworm-composted food waste. The generated electricity was converted into usable voltage level for mobile electronics through power conditioning circuits. The implemented prototype showed $200{\mu}W$ of maximum output electric power, which successfully supplied a beacon device which continuously transmitted data to nearby smartphone without a battery. The proposed system can help develop portable or bio-mimetic energy supply for sustainable use with further improvement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1337-1342
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• 2019

Medical institutions face the challenge of securing competitiveness among medical institutions due to the rapid spread of ICT convergence, and managing data that is growing at an enormous rate due to the emergence of big data and the emergence of the Internet of Things. The big data paradigm of the medical community is not just about large data or tools and processes for processing and analyzing it, but also means a computerized shift in the way people live, think and study. As the medical data is recently released, the demand for the use of medical data is increasing. Therefore, the research on disease detection system based on public data using open source that can help rational and efficient decision making was conducted. As a result of the experiment, unlike a simple disease inquiry or a symptom inquiry about a single disease provided by a public institution, related diseases are searched by a symptom or a cause.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1001-1002
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• 2009

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.601-605
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• 2007

In this paper, we propose a simple and relative electrode contact monitoring method. By exploiting the power line interference, which is regarded as one of the worst noise sources for bio-potential measurement, the relative difference in electrode impedance can be measured without a current or voltage source. Substantial benefits, including no extra circuit components, no degradation of the body potential driving circuit, and no electrical safety problem, can be achieved using this method. Furthermore, this method can be applied to multi-channel isolated bio-potential measurement systems and home health care devices under a steady measuring environment.

• IMMUNE NETWORK
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• v.10 no.4
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• pp.109-114
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• 2010

The molecular mechanisms involved in the pathogenesis of chronic obstructive pulmonary disease (COPD) are poorly defined. Accumulating evidences indicate that chronic inflammatory responses and adaptive immunity play important roles in the development and progression of the disease. Recently, it has been shown that IL-17 producing CD4 T cells, named Th17 cells, which have been implicated in the pathogenesis of several inflammatory and autoimmune diseases, are involved in airway inflammation and COPD. In addition, we and others suggest that autoimmunity may play a critical role in the pathogenesis of COPD. Here, we will review the current understanding of roles of Th17 cells and autoimmune responses in COPD.