• International journal of advanced smart convergence
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• 제9권4호
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• pp.62-73
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• 2020

In this paper, we present electronic acupuncture needles we have developed using intelligence technology based on Bluetooth in order to allow anyone to simply receive customized remote diagnosis and treatment by clicking on the menu of the smartphone regardless of time and place. In order to determine the health condition and disease of patients, we have developed a software and a hardware of electronic acupuncture needles, operating on Bluetooth which transmits biometric data to oriental medical doctors using the functions of automatically determining pulse diagnosis, tongue diagnosis, and oxygen saturation; the functions are most commonly used in herbal treatment. In addition, using fuzzy logic and reasoning based on smartphones, we present in this paper an algorithm and the results of completion of hardware implementation for electronic acupuncture needles, appropriate for the body conditions of patients; the algorithm and the hardware implementation are for a treatment time duration by electronic acupuncture needles, an automatic determinations of pulse diagnosis, tongue diagnosis, and oxygen saturation, a function implementation for automatic display of acupuncture point, and a strength adjustment of electronic acupuncture needles. As a result of our simulation, we have shown that the treatment of patients, performed using an Electronic Acupuncture Needles based on intelligence, is more efficient compared to the treatment that was performed before.

• 대한의용생체공학회:의공학회지
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• 제13권1호
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• pp.69-78
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• 1992

This Paper describes implementation of a remote ECG diagnostic system using telephone line. The overall system includes ECG data acquisition system, ECG terminal, system control software, automatic diagnosis system, and transmission system.'The proposed system provides various functions, which are ECG data acquisition, transmission, receiving, diagnosis and dialogue between patients and medical doctors. Thls system is very simple and convienient to use. We evaluate the performance of modem and the accuracy of automatic diagnosis algorithm. The obtained results suggest the Possibilities of a remote ECG diagnostic system using the only existed telephone line.

• 한국산학기술학회논문지
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• 제14권1호
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• pp.358-362
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• 2013

본 논문을 통해 연구된 자가 진단 기능을 갖는 RFID 리더 platform은 자동 상태 인식 기능 및 보고 기능이 탑재되어 있고, 자동 업그레이드 기능과 복수 리더 환경에서 주파수 운영 정책을 사용하기 때문에 앞으로 시스템의 전체적인 운영 환경이 점점 대형화 되고, 멀티 환경에서의 활용도에서 많으며, 국내 RFID 시장 뿐 아니라 세계시장에서 많이 발생하는 요구를 충족할 수 있는 기술이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.107.3-107
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• 2001

This paper is concerned with the development of a simple digital stethoscope system for diagnosis of cardiac disorders. This system consists of an electronic stethoscope, IC sound recorder and a notebook computer. The cardiac sound is easily acquired by the electronic stethoscope and then recorded in IC memory stick so that the digital cardiac signal can be simply transmitted to the computer for signal display, disease diagnosis, and personal history record. A software is built with functions displaying the sound graphically and replaying the sound clearly. Further, a neural network recognition system for automatic diagnosis of cardiac disorders is also added to the software.

• 대한전기학회논문지
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• 제39권8호
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• pp.888-897
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• 1990

In this paper, a multi-processor-based supervisory control for automatic assembly system is presented. The proposed supervisory control is organized in terms of C-language and with structured and easily expandable characteristics. Also the controller is designed to possess diagnostic capability including self-diagnosis of processor module. The developed supervisory control has been shown to be very useful via a high speed automatic assembly system with vision capability.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.15-19
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• 2002

Automatic detection of interstitial lung disease using Neural Network is presented. The rounded opacities in the pneumoconiosis X-ray photo are picked up quickly by a back propagation (BP) neural network with several typical training patterns. The training patterns from 0.6 mm ${\O}$ to 4.0 mm ${\O}$ are made by simple circles. The total evaluation is done from the size and figure categorization. Mary simulation examples show that the proposed method gives much reliable result than traditional ones.

• Journal of Chest Surgery
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• 제25권9호
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• pp.905-911
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• 1992

Automatic junctional tachycardia is one of common atrial arrhythmia after open heart surgery which is often refractory to antiarrhythmic agents. We have experienced refractory automatic junctional tachycardia in two patients. In the first, it occured after cryosurgery for AV nodal reentry tachycardia and simultaneous dissection of a posterior septal bypass tract. In the second, it complicated the postoperative course of a patient who received intracardiac repair for double outlet right ventricle, ventricular septal defect, and pulmonary stenosis. Conventional therapy with atrial pacing, verapamil, digoxin, and electrical cardioversion were ineffective. Therefore, amiodarone was administered intravenously and it controlled automatic junctional tachycardia. The need for accurate and rapid diagnosis of this condition along with results of treatment are discussed.

• 제어로봇시스템학회논문지
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• 제10권5호
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• 센서학회지
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• 제23권1호
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• pp.58-65
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• 2014

We developed a low-cost automatic diagnosis system for water quality in cooling towers to measure the concentrations of key ingredients such as $Ca^{2+}$, $Cl^-$, $PO{_4}^{3-}$, and $Fe^{2+}$. $Ca^{2+}$, and $Cl^-$ are the main factors that cause the generation of scale, corrosion, and sludge in water pipes. $PO{_4}^{3-}$ prevents corrosion, sludge and scale by inhibiting the ions (i.e., $Ca^{2+}$, $Cl^-$) from sticking to the pipes. $Fe^{2+}$ is an indicator of pipe corrosion. The proposed system consists of a microprocessor, a specimen container and heater, a precision pump, relays and valves, LED optical sources, and photo detectors. It automatically collects water samples and carries out pretreatment for determining the concentration of each chemical, and then estimates the concentration of each ion using low-cost LED optical sources and detectors. Experimental results showed that the accuracy of the proposed system is sufficiently high for water quality diagnosis and management of cooling towers, demonstrating the possibility of the proposed system's wide usage in real environments.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.