• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.49-60
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• 2024

The purpose of this study establishes the deep neural network (DNN) and Decoder models to predict the flow and thermal fields of three-dimensional wavy wings as a passive flow control. The wide ranges of the wavy geometric parameters of wave amplitude and wave number are considered for the various the angles of attack and the aspect ratios of a wing. The huge dataset for training and test of the deep learning models are generated using computational fluid dynamics (CFD). The DNN and Decoder models exhibit quantitatively accurate predictions for aerodynamic coefficients and Nusselt numbers, also qualitative pressure, limiting streamlines, and Nusselt number distributions on the surface. Particularly, Decoder model regenerates the important flow features of tiny vortices in the valleys, which makes a delay of the stall. Also, the spiral vortical formation is realized by the Decoder model, which enhances the lift.

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.79-84
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• 2024

As microchips' degree of integration is getting higher, its cooling problem becomes important more than ever. One of the promising methods is using fractal microchannel heat sink by mimicking nature's Murray networks. However, most of the related works have been progressed only by numerical analysis. Perhaps such lack of direct experimental studies is due to the technical difficulty of the temperature and heat flux measurement in complex geometric channels. Here, we demonstrate the direct visualization of in situ temperature profile in a fractal microchannel heat sink. By using the temperature-sensitive fluorescent dye and a transparent Polydimethylsiloxane window, we can map temperature profiles in silicon-based fractal heat sinks with various fractal scale factors (a=1.5-3.5). Then, heat transfer rates and pressure drops under a fixed flow rate were estimated to optimize hydrodynamic and thermal characteristics. Through this experiment, we found out that the optimal factor is a=1.75, given that the differences in heat transfer among the devices are marginal when compared to the variances in pumping power. This work is expected to contribute to the development of high-performance, high-efficiency thermal management systems required in various industrial fields.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.107-123
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• 2013

In a wireless sensor network environment, it is required to ensure anonymity by keeping sensor nodes' identifiers not being revealed and to support real-time authentication, lightweight authentication and synchronization. In particular, there exist possibilities of location information leakage by others, privacy interference and security vulnerability when it comes to wireless telecommunications. Anonymity has been an importance issue in wired and wireless network environment, so that it has been studied in wide range. The sensor nodes are interconnected among them based on wireless network. In terms of the sensor node, the researchers have been emphasizing on its calculating performance limit, storage device limit, and smaller power source. To improve of biometric-based D. He scheme, this study proposes a real-time authentication protocol using Unique Random Sequence Code(URSC) and variable identifier for enhancing network performance and retaining anonymity provision.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.199-208
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• 2014

At present, the effect of oriental treatment system is proved in the west and using the data of tongue and pulse of body, the doctor can decide the patient's body state without Xray and CT data of large machines. In this paper, the patient's medical data is transmitted to the doctor and the real time decision algorithm is developed and so the doctor can decide the medical treatments. Using the mobile phone, the pulse data and bio data can be sent to the doctor and therefore the patients, who can't care in real time, can be treated in real time in the impossible medical treatment areas. Therefore in this paper, the oriental medical treatment system algorithm and artificial intelligence electrical needle simulation are processed for real time and checked and treated, so anyone can decide patient's state using mobile phone.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.249-257
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• 2003

The bio-telemetry technologies, that use the wireless miniaturized telemetry module implanted in the human body and transmits several biomedical signal from inside to outside of the body, have been expected to solve the problem such as the patient's inconvenience and the limit for diagnosis. In the case of transceiver system using the wireless RF transmission method, the method of three-dimensional localization for implantable miniaturized telemetry module is necessary to detect the exact position of disease. A new method for three-dimensional localization using small loop antenna in the implantable miniaturized telemetry module was proposed in this paper. We proposed a method that can accurately determine the position of telemetry module by analyzing the differences in the strength of signal, which is received at each of the small size RF receiver array installed on the body surface.

• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Journal of Korean Academy of Nursing
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• v.52 no.4
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• pp.435-450
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• 2022

Purpose: The purpose of the study was to understand the care experiences of the family of living donor liver transplantation (LDLT) patients where the donation had occurred within the family. Methods: Participants were eight family caregivers who cared for recipients and donors of LDLT. Data were collected through individual in-depth interviews from November, 2020 to April, 2021. Data analysis was performed through a cyclical process of data collection and analysis by applying Giorgi's phenomenological research method. Results: The five main components extracted from the experiences of the family caregivers were: "A double-edged choice to save the family", "The harsh daily life of liver transplantation care", "The yoke of double care on both shoulders", "The power to withstand the adversity of caring", and "The recovery and growth of life pursued by trusting each other". Conclusion: The participants tried to do their best in their daily lives, while providing reassurance and care to the LDLT patients in the family; however, they expressed some worry and hardship while doing so. The results of this study provide a deeper understanding of the caring experience of the family caregivers, which may contribute to the development of nursing interventions that will aid these caregivers in providing care to their LDLT family members. Furthermore, the development and application of an integrated management program for LDLT patients in the family is required.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.661-669
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• 2008

Nowadays, authentication is essential to identify the legal users in a network communication. Usually, there are few wars to achieve authentication over a publicly accessible network system in order to protect certain private data from the unauthorized users, ranging from simple ID/Password to Biometrics System. One of the most active areas in OTP(One Time Password) research today aims at exploiting OTP to provide authentication in the finance and security industry. OTP is usually discarded once it has been used. this prevents huge loophole of traditional authentication system which employs the same ID and Password every time. However this OTP system also has its weaknesses in surviving some attacks. this paper proposes an advanced OTP protocol using PingPong-128 without loop hole of pre-existing OTP.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.928-932
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• 2009

Most diseases are caused by complex processes of various factors. Although previous researches have tried to identify the causes of the disease, there are still lots of limitations to clarify the complex factors. Here, we present a disease classification model based on an evolutionary learning approach of combinatorial features using the data sets from the genetics and cohort studies. We implemented a system for finding the combinatorial risk factors and visualizing the results. Our results show that the proposed method not only improves classification accuracy but also identifies biologically meaningful sets of risk factors.