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홍현지(Hyeonji Hong),정미림(Mirim Jung),염은섭(Eunseop Yeom) 한국가시화정보학회 2017 한국가시화정보학회지 Vol.15 No.3
Increase of blood viscosity significantly changes the flow resistance and wall shear stress which are related with cardiovascular diseases. For measurement of blood viscosity, microfluidic method has proposed by monitoring pressure between sample and reference flows in the downstream of a microchannel with two inlets. However, it is difficult to apply this method to unknown flow conditions. To measure blood viscosity under unknown flow conditions, a microfluidic method based on micro particle image velocimetry(PIV) is proposed in this study. Flow rate in the microchannel was estimated by assuming velocity profiles represent mean value along channel depth. To demonstrate the measurement accuracy of flow rate, the flow rates measured at the upstream and downstream of a T-shaped microchannel were compared with injection flow rate. The present results indicate that blood viscosity could be reasonably estimated according to shear rate by measuring the interfacial width and flow rate of blood flow. This method would be useful for understanding the effects of hemorheological features on the cardiovascular diseases.
PIV 측정 및 수치해석을 이용한 구강암 수술에 따른 기도 형상 내 유동 특성
홍현지(Hyeonji Hong),안세현(Se Hyeon An),서희림(Heerim Seo),송재민(Jae Min Song),염은섭(Eunseop Yeom) 한국가시화정보학회 2021 한국가시화정보학회지 Vol.19 No.3
Oral cancer surgery typically consists of resection of lesion, neck dissection and reconstruction, and it has an impact on the position of hyoid bone. Therefore, morphological change of airway can occur since the geometric parameter of airway is correlated with the hyoid bone. Airflow is affected by geometry of the airway. In this study, flow characteristics were compared between pre- and post-surgery models by both particle image velocimetry (PIV) and numerical simulation. 3D model of upper airway was reconstructed based on CT data. Velocity is accelerated by the reduced channel area, and vortex and recirculation region are observed in pre- and post-surgery models. For the post-surgery model, high pressure distribution is developed by significantly decreased hydraulic diameter, and the longitudinal flow stream is also interrupted.
Gated recurrent unit (GRU) 신경망을 이용한 적혈구 침강속도 예측
이재진(Jaejin Lee),홍현지(Hyeonji Hong),송재민(Jae Min Song),염은섭(Eunseop Yeom) 한국가시화정보학회 2021 한국가시화정보학회지 Vol.19 No.1
In order to determine erythrocyte sedimentation rate (ESR) indicating acute phase inflammation, a Westergren method has been widely used because it is cheap and easy to be implemented. However, the Westergren method requires quite a long time for 1 hour. In this study, a gated recurrent unit (GRU) neural network was used to reduce measurement time of ESR evaluation. The sedimentation sequences of the erythrocytes were acquired by the camera and data processed through image processing were used as an input data into the neural network models. The performance of a proposed models was evaluated based on mean absolute error. The results show that GRU model provides best accurate prediction than others within 30 minutes.
스마트 폰 기반 3D 프린팅 칩을 이용한 적혈구 변형성 측정
이수환(Suhwan Lee),홍현지(Hyeonji Hong),염은섭(Eunseop Yeom),송재민(Jae Min Song) 한국가시화정보학회 2020 한국가시화정보학회지 Vol.18 No.3
RBC (red blood cell) deformability is one of factors inducing blood shear thinning effect. Reduction of RBC deformability increases blood viscosity in high shear region. In this study, 3D printed chip with proper distribution of wall shear rate (WSR) was proposed to measure RBC deformability of blood samples. To fabricate 3D printed chip, the design of 3D printed chip determined through numerical simulation was modified based on the resolution of the 3D printer. For the estimation of pressure drop in the 3D printed chip, two bypass outlets with low and high WSR are exposed to atmospheric pressure through the needles. By positioning the outlet of needles in the gravity direction, the formation of droplets at bypass outlets can be captured by smartphone. Through image processing and fast Fourier transform (FFT) analysis, the frequency of droplet formation was analyzed. Since the frequency of droplet formation is related with the pressure at bypass, high pressure drop caused by reduction of RBC deformability can be estimated by monitoring the formation of blood droplets using the smartphone.
ARIMA를 활용한 실시간 SCR-HP 밸브 온도 수집 및 고장 예측
이수환(Suhwan Lee),홍현지(Hyeonji Hong),박지수(Jisoo Park),염은섭(Eunseop Yeom) 한국가시화정보학회 2021 한국가시화정보학회지 Vol.19 No.1
Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.
이재진(Jaejin Lee),홍현지(Hyeonji Hong),염은섭(Eunseop Yeom) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
Erythrocyte sedimentation rate (ESR) can indirectly measure the degree of inflammation of blood in the body. Conventional ESR analysis requires a relatively long measurement time about 1 hour. In this study, Deep Learning models are applied to reduce the measurement time of ESR considering that Artificial Intelligence(AI) has been developed unprecedentedly in every region. Through image processing methods, the sedimentation information of the blood samples is acquired in time series. For the prediction of ESR value based on Deep Learning, Mean Absolute Error(MAE) is used as the measurement for the comparison of each model. From the results, the ensemble of Wide and Deep Neural Network model shows the best fit. This model has a good performance of predicting ESR value in a stable way, with lowest MAE (0.0164) and standard error (0.0030) among other models. By applying Deep Learning, the measurement time of ESR diagnosis can be significantly reduced with less uncertainty.