• 대한의용생체공학회:의공학회지
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• 제18권4호
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• pp.373-380
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• 1997

본 논문은 개발된 환자 모니터링 시스템에서 사용하고 있는 인트라베드 통신망에 대하여 기술한다. 인트라베드 통신망은 모듈형 환자 모니터의 본체와 이에 연결되는 여러 종류의 생체 신호 측정 모듈들 사이의 데이터 통신망을 의미한다. 모듈형 환자 모니터에서의 데이터 통신에 대한 필요 사항들에 대한 분석을 기초로 하여, 인트라베드 통신망은 1Mbps의 통신 속도를 가지도록 하였고, RS-485와 HDLC통신 규격을 채용하였다. 데이터의 교환은 패킷, 프레임 및 스트림의 세가지 데이터 구조에 의해 구현하였다. 개발된 환자 모니터에서의 인트라베드 통신망의 설계 및 구현과 구 사양 및 성능을 기술한다.

• 대한의용생체공학회:의공학회지
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• 제18권4호
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• pp.381-388
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• 1997

본 논문은 환자 모니터링 시스템에서 환자 모니터, 중앙 환자 모니터, DB서버 그리고 임상 의사용 워커스테이션을 연결하기 위한 인터베드 통신망의 통신방식의 설계와 구현에 대해 기술하였다. 실시간 생체신호 모니터링을 위해 필요한 조건들을 바탕으로 각종 메시지 형식과 교환방식을 결정하였으며, 객체지향 설계기법을 적용하여 구현하였다. 현재, 인터베드 통신방식을 사용하여 구현된 서비스는 모니터링 중인 환자들에 대한 정보를 제공하기 위한 환자 위치 결정 서비스와 실시간으로 환자의 생체 신호 정보를 전달하기 위한 원격 환자 모니터링 서비스이다. 임상 현장에서 실험한 결과 이들 서비스들이 실시간 생체신호 모니터링에 필요한 조건들을 모두 만족하고 있음을 확인하였다.

• 대한의용생체공학회:의공학회지
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• 제35권3호
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• pp.68-74
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• 2014

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2006년도 추계학술대회 논문집
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• pp.121-124
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• 2006

The arteries are very important in cardiovascular system and easily adapt to varying flow and pressure conditions by enlarging or shrinking to meet the given hemodynamic demands. The blood flow in arteries is dominated by unsteady flow phenomena due to heart beating. In certain circumstances, however, unusual hemodynamic conditions cause an abnormal biological response and often induce circulatory diseases such as atherosclerosis, thrombosis and inflammation. Therefore quantitative analysis of the unsteady pulsatile flow characteristics in the arterial blood vessels plays important roles in diagnosing these circulatory diseases. In order to verify the hemodynamic characteristics, in-vivo measurements of blood flow inside the extraembryonic arterial bifurcation cascade of chicken embryo were carried out using a micro-PIV technique. To analyze the unsteady pulsatile flow temporally, the (low images of RBCs were obtained using a high-speed CMOS camera at 250fps with a spatial resolution of $30{\mu}m\times30{\mu}m$ in the whole blood vessels. In this study, the unusual flow conditions such as flow separation or secondary flow were not observed in the arterial bifurcations. However, the vorticity has large values in the inner side of curvature of vessels. In addition, the mean velocity in the arterial blood vessel was decreased and pulsating frequency obtained by FFT analysis of velocity data extracted in front of the each bifurcation was also decreased as the bifurcation cascaded.

• 대한의용생체공학회:의공학회지
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• 제21권5호
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• pp.527-535
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• 2000

대부분의 포탈영상이 그에 상응하는 시뮬레이터 영상을 참조 영상으로 하여 방사선치료 계획을 수행하고 있다. 이것은 선형가속기의 높은 에너지 X선으로서 얻어지는 포탈 영상의 물리적 특성 때문에, 구조적으로 대단히 불량한 포탈 영상의 개선과 잃어버린 영상 정보의 복원에 시뮬레이터 영상 자체에서의 영상정보를 이용할 수 있다는 가능성을 보여주고 있는 것이다. 본 연구에서는 최대 퍼지 엔트로피를 평가함수로 이용한 유전자 알고리듬을 사용하여 영상에서의 퍼지 영역을 자동적으로 결정하고, 그것을 멤버쉽 함수에서 적용하여 퍼지영상 개선 기법으로서 포탈 영상과 시뮬레이터 영상을 개선한 후, 잡음이 중첩된 시뮬레이터 영상들로서 연관기억장치를 학습시키고 여기에 퍼지 방법으로 개선시킨 포탈 영상을 입력하여 기존의 영상기법으로 처리된 영상보다 좋은 포탈 영상을 얻을 수 있었다.

• 한국환경과학회지
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• 제27권11호
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• pp.1073-1081
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• 2018

In this study, an operational data set was analysed by establishing a path model to figure out the actual cause-effect relationship of a wastewater treatment plant (WWTP); in particular, for the effluent concentrations of T-N and T-P. To develop the path models, data sets of operational records including effluent concentrations and operational factors were obtained from a field scale WWTP of $680,000m^3$ of treatment capacity. The models showed that the relationship networks with the correlation coefficients between variables for objective expressions indicated the strength of each relationship. The suggested path models were verified according to whether the analyzation results matched known theories well, but sophisticated minute theoric relationships could not be cropped out distinctly. This indicates that only a few paths with strong theoric casual relationships were represented as measured data due to the high non-linearity of the mechanism of the removal process in a biological wastewater treatment.

• Molecules and Cells
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• 제45권7호
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• pp.435-443
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• 2022

In response to environmental changes, signaling pathways rewire gene expression programs through transcription factors. Epigenetic modification of the transcribed RNA can be another layer of gene expression regulation. N⁶-adenosine methylation (m⁶A) is one of the most common modifications on mRNA. It is a reversible chemical mark catalyzed by the enzymes that deposit and remove methyl groups. m⁶A recruits effector proteins that determine the fate of mRNAs through changes in splicing, cellular localization, stability, and translation efficiency. Emerging evidence shows that key signal transduction pathways including TGFβ (transforming growth factor-β), ERK (extracellular signal-regulated kinase), and mTORC1 (mechanistic target of rapamycin complex 1) regulate downstream gene expression through m⁶A processing. Conversely, m⁶A can modulate the activity of signal transduction networks via m⁶A modification of signaling pathway genes or by acting as a ligand for receptors. In this review, we discuss the current understanding of the crosstalk between m⁶A and signaling pathways and its implication for biological systems.

• 대한의용생체공학회:의공학회지
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• 제43권5호
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• pp.299-307
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• 2022

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into training, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.

• Asian Journal of Innovation and Policy
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• 제11권3호
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• pp.397-420
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• 2022

This research was conducted by applying a bibliometric analysis to determine the dynamics of research topics from ten percent of research output (international publications) generated by Indonesian scientists from the period of 1945-2021. This study utilizes VOSviewers version 1.6.18 for analysis and visualization of bibliometric networks. The research results indicate that 50.24% of Indonesian international publications are published in the form of articles, with subjects such as: Agricultural and Biological Sciences, Medicine, and Earth and Planetary Sciences as the most dominating subject areas. Regarding the author, Tjia, MO from Bandung Institute of Technology was acknowledged as the top author in terms of the number of publications produced for two periods. The article entitled "Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the Global Burden of Disease Study 2013" (Ng et al., 2014) became the most cited one.

• 대한의용생체공학회:의공학회지
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• 제44권4호
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• pp.293-301
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• 2023

Scoliosis is a three-dimensional deformation of the spine that is a deformity induced by physical or disease-related causes as the spine is rotated abnormally. Early detection has a significant influence on the possibility of nonsurgical treatment. To train a deep learning model with preprocessed images and to evaluate the results with and without data augmentation to enable the diagnosis of scoliosis based only on a chest X-ray image. The preprocessed images in which only the spine, rib contours, and some hard tissues were left from the original chest image, were used for learning along with the original images, and three CNN(Convolutional Neural Networks) models (VGG16, ResNet152, and EfficientNet) were selected to proceed with training. The results obtained by training with the preprocessed images showed a superior accuracy to those obtained by training with the original image. When the scoliosis image was added through data augmentation, the accuracy was further improved, ultimately achieving a classification accuracy of 93.56% with the ResNet152 model using test data. Through supplementation with future research, the method proposed herein is expected to allow the early diagnosis of scoliosis as well as cost reduction by reducing the burden of additional radiographic imaging for disease detection.