• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.39-46
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• 1998

In this paper, we present a neural network identification and a control of highly complicated nonlinear left ventricular assist device(LVAD) system with a pneumatically driven mock circulation system. Generally, the LVAD system needs to compensate for nonlinearities. It is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with neural network identification(NNI). Once the NNI has learned the dynamic model of the LVAD system, the other network, called neural network controller(NNC), is designed for a control of the LVAD system. The ability and effectiveness of identifying and controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• The Journal of Information Technology
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• v.8 no.2
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• pp.93-101
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• 2005

We implemented an execution health officer system in this research. This system is the execution and can manage the health of the user systematically. A home network standard was not set up yet. The comparison observed geungedda suitable in a health officer system standard. We made efforts to realize the medical treatment network not to be activated yet. We implemented also a remote medical examination and treatment service a former chateu service alimentotherapy service. We will offer different service field and many the gear potent service.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.395-397
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• 1997

We have applied the neural network method for the neural networkmethod for the automatic scoring of the sleep stage. 17 features are extracted from the recorded EEG, EOG and EMG signals. These features are inputed to tile multilayer perceptron model. Neural network was trained with error-back propagation method. Results are compared with manual scoring of the experts, and show the possibility of application of automatic method in sleep stage scoring.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.263-266
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• 1997

The purpose of this paper is the development of Integrated Bio-signal Management System. (IBMS) using the network. IBMS is the system to manage the medical signals that measured from the each independent medical measurement system module. Each has a LAN Card. We developed the Network Application using Socket Library. Also, we developed the Graphic User Interface software for IBMS using Visual C++ on Windows 95.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.151-153
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• 1996

The purpose of this paper is the development of Integrated Bio-signal Management System(IBMS) using the network. IBMS is the system to manage the medical signals that measured from the each independent medical measurement system module. Each has a LAN. We developed the file-server network using Novell Netware. Also, we developed the Graphic User Interface software for IBMS using Visual C++ at Windows 3.1.

• Journal of the Korea Computer Industry Society
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• v.10 no.1
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• pp.29-36
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• 2009

Sensor network in Ubiquitous age that change fast made paper about U- health medical treatment administrative service construction model as a technology that attach electron tag (RFID) and procures information by real time because detects surrounding environment information to basis and uses realization information of things through this to all necessary things. Prognostication that thorough administration about contagiousness carrier with side effect of possession Asia's vaccination and AISD that often occur is difficult utilizes RFID chip aiming and did by purpose constructing more better health medical treatment administrative service.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• Journal of the korean veterinary medical association
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• v.49 no.4
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• pp.218-220
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• 2013

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.75-82
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• 2022

Medical data is one of the data that must be kept in safe containers, far from intrusion, viewing and modification. With the technological developments in hospital systems and the use of cloud computing, it has become necessary to save, encrypt and even hide data from the eyes of attackers. Medical data includes medical images, whether they are x-ray images of patients or others, or even documents that have been saved in the image format. In this review, we review the latest research and the latest tools and algorithms that are used to protect, encrypt and hide these images, and discuss the most important challenges facing these areas.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.544-546
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• 2020

의료 데이터를 이용하여 인공지능 기계학습 연구를 수행할 때 자주 마주하는 문제는 데이터 불균형, 데이터 부족 등이며 특히 정제된 충분한 데이터를 구하기 힘들다는 것이 큰 문제이다. 본 연구에서는 이를 해결하기 위해 GAN(Generative Adversarial Network) 기반 고해상도 의료 영상을 생성하는 프레임워크를 개발하고자 한다. 각 해상도 마다 Scale 의 Gradient 를 동시에 학습하여 빠르게 고해상도 이미지를 생성해낼 수 있도록 했다. 고해상도 이미지를 생성하는 Neural Network 를 고안하였으며, PGGAN, Style-GAN 과의 성능 비교를 통해 제안된 모델이 양질의 고해상도 의료영상 이미지를 더 빠르게 생성할 수 있음을 확인하였다. 이를 통해 인공지능 기계학습 연구에 있어서 의료 영상의 데이터 부족, 데이터 불균형 문제를 해결할 수 있는 Data augmentation 이나, Anomaly detection 등의 연구에 적용할 수 있다.