• Proceedings of the KSMRM Conference
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• 1999.11a
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• pp.92-92
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• 1999

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.459-472
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• 2011

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.

• Genomics & Informatics
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• v.3 no.3
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• pp.68-73
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• 2005

Pharmacogenomics research requires an intelligent integration of large-scale genomic and clinical data with public and private knowledge resources. We developed a web-based knowledge base for KPRN (Korea Pharmacogenomics Research Network, http://kprn.snubi. org/). Four major types of information is integrated; genetic variation, drug information, disease information, and literature annotation. Eighteen Korean pharmacogenomics research groups in collaboration have submitted 859 genotype data sets for 91 disease-related genes. Integrative analysis and visualization of the large collection of data supported by integrated biomedical path­ways and ontology resources are provided with a user-friendly interface and visualization engine empowered by Generic Genome Browser.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.77-84
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• 2023

Given the growing volume of biomedical papers, the ability to efficiently extract keywords has become crucial for accessing and responding to important information in the literature. In this study, we conduct a comprehensive evaluation of different unsupervised learning-based models and BERT-based models for keyword extraction in the biomedical field. Our experimental findings reveal that the BioBERT model, trained on biomedical-specific data, achieves the highest performance. This study offers precise and dependable insights to guide forthcoming research in biomedical keyword extraction. By establishing a well-suited experimental framework and conducting thorough comparisons and analyses of diverse models, we have furnished essential information. Furthermore, we anticipate extending our contributions to other domains by providing comparative experiments and practical guidelines for effective keyword extraction.

• Journal of Biomedical Engineering Research
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• v.27 no.1
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• pp.30-37
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• 2006

Injecting currents into an electrically conducting subject, we may measure the induced magnetic flux density distributions using an MRI scanner. The measured data are utilized to reconstruct cross-sectional images of internal conductivity and current density distributions in Magnetic Resonance Electrical Impedance Tomography (MREIT). Injection currents are usually provided in a form of mono-polar or bi-polar pulses synchronized with an MR pulse sequence. Given an MRI scanner performing the MR phase imaging to extract the induced magnetic flux density data, the current source becomes one of the key parts determining the signal-to-noise ratio (SNR) of the measured data. Since this SNR is crucial in determining the quality of reconstructed MREIT images, special care must be given in the design and implementation of the current source. This paper describes a current source design for MREIT with features including interleaved current injection, arbitrary current waveform, electrode switching to discharge any stored charge from previous current injections, optical isolation from an MR spectrometer and PC, precise current injection timing control synchronized with any MR pulse sequence, and versatile PC control program. The performance of the current source was verified using a 3T MRI scanner and saline phantoms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.107-111
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• 2009

의료기관의 응급실은 환자의 생명을 다루는 긴박한 현장으로 환자에 대한 실시간 모니터링 및 관리가 필수적으로 요구되는 곳이다. 본 연구에서는 기존 응급실 진료 프로세스를 체계적으로 관리하고 모니터링 하기 위해 BPM 아키텍처를 도입하여 응급실 업무들을 표준화, 가시화함으로써 의료진의 신속한 응급 업무 대응이 가능한 응급신속대응관리시스템(EQRMS-Emergency Quick Response Management System)를 제안한다. 의료기관에서 BPM의 도입은 단순히 병원 경영 목표나 병원 내외부의 운영을 모니터링 할 수 있는 이점 이외에 병원 업무의 이윤을 극대화 할 수 있는 다양한 효과가 있다. 또한 임상위험수치 (CV-Critical Value)를 정의함으로써 복잡한 검사의 단순화와 검사 시간 단축, 검사의 오류 발생률 감소 등 환자의 안정성 제고 측면에도 크게 기여할 것이다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.891-892
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• 2006

Photoplethsymogram (PPG) drifts due to the changes in respiration and blood perfusion as well as external light. This hinders a proper PPG measurement. We controlled DC drifts by controlling the signal ground of PPG signals. A microprocessor-based system successfully controlled DC drifts of PPG signals.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.893-894
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• 2006

DSP based on a 8 bit microprocessor was studied for ECG and PPG signals. Digital filtering has an advantage of reducing hardware components in system-on-chip design. However, low resolution such as in 8 bit data has much difficulties in DSP. We demonstrated a comparable performance of DSP filtering compared with analog filters.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.686-689
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• 2018

3D 손 포즈 추정(Hand Pose Estimation, HPE)은 스마트 인간 컴퓨터 인터페이스를 위해서 중요한 기술이다. 이 연구에서는 딥러닝 방법을 기반으로 하여 단일 RGB-Depth 카메라로 촬영한 양손의 3D 손 자세를 실시간으로 인식하는 손 포즈 추정 시스템을 제시한다. 손 포즈 추정 시스템은 4단계로 구성된다. 첫째, Skin Detection 및 Depth cutting 알고리즘을 사용하여 양손을 RGB와 깊이 영상에서 감지하고 추출한다. 둘째, Convolutional Neural Network(CNN) Classifier는 오른손과 왼손을 구별하는데 사용된다. CNN Classifier 는 3개의 convolution layer와 2개의 Fully-Connected Layer로 구성되어 있으며, 추출된 깊이 영상을 입력으로 사용한다. 셋째, 학습된 CNN regressor는 추출된 왼쪽 및 오른쪽 손의 깊이 영상에서 손 관절을 추정하기 위해 다수의 Convolutional Layers, Pooling Layers, Fully Connected Layers로 구성된다. CNN classifier와 regressor는 22,000개 깊이 영상 데이터셋으로 학습된다. 마지막으로, 각 손의 3D 손 자세는 추정된 손 관절 정보로부터 재구성된다. 테스트 결과, CNN classifier는 오른쪽 손과 왼쪽 손을 96.9%의 정확도로 구별할 수 있으며, CNN regressor는 형균 8.48mm의 오차 범위로 3D 손 관절 정보를 추정할 수 있다. 본 연구에서 제안하는 손 포즈 추정 시스템은 가상 현실(virtual reality, VR), 증강 현실(Augmented Reality, AR) 및 융합 현실 (Mixed Reality, MR) 응용 프로그램을 포함한 다양한 응용 분야에서 사용할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1137-1140
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• 2019

가상현실(Virtual Reality, VR), 증강현실(Augmented Reality, AR), 혼합현실(Mixed Reality, MR) 분야에 유용한 인간 컴퓨터 인터페이스 기술은 필수적이다. 특히 휴먼 손동작 인식 기술은 직관적인 상호작용을 가능하게 하여, 다양한 분야에서 편리한 컨트롤러로 사용할 수 있다. 본 연구에서는 뎁스 영상 기반의 1 인칭 시점 손동작 인식을 위하여 손동작 데이터베이스 생성 시스템을 구축하여, 손동작 인식기 학습에 필요한 1 인칭(Egocentric View Point) 데이터베이스를 촬영하여 제작한다. 그리고 모바일 Head Mounted Device(HMD) VR 을 위한 뎁스 영상 기반 1 인칭 시점 손동작 인식(Hand Pose Recognition, HPR) 딥러닝 Deep Residual Network 를 구현한다. 최종적으로, 안드로이드 모바일 디바이스에 학습된 Residual Network Regressor 를 이식하고 모바일 VR 에 실시간 손동작 인식 시스템을 구동하여, 모바일 VR 상 실시간 3D 손동작 인식을 가상 물체와의 상호작용을 통하여 확인 한다.