• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.731-733
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• 1988

This paper describes a design of hardware system for real time automatic diagnosis of ECG arrhythmia based on pipeline processor consisting of the three microcomputer. ECG data is acquisited by 12 bit A/D converter with hardware QRS triggered detector. Four diagnostic parameters - heart rate, morphology, axis, and ST segment - are used for the classification and the diagnosis of arrhythmia. The functions of the main CPU were distributed and processed with three microcomputers. There-fore the effective data process and the real time process using microcomputer can be obtained. The interconnection structure consisting of two common memory units is designed to decrease the delay time caused by data transfer between processors and by which the delay time can be taken 1 % of one clock period.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.73-76
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• 1996

Computer simulation and magnetic resonance angiograms(MRAs) are used to understand for flow patterns in the carotid arterial bifurcation. Steady momentum equation is solved by the finite volume method. A Phantom of the carotid artery made of bioacrylic material is used for MRA observation. Flow Patterns are observed by using MRA for flow in the phantom of an automatic closed-type circulatory system filled with sugar 4 w% solution. For numerical analysis the idealized geometric shape of the carotid artery is constructed to portray the phantom. Results of numerical analysis are compared with those of MRA. The flow patterns of the phantom on MRA are almost identical to those of the computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.101-108
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• 1999

In this paper, a real time diagnostic algorithm for estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). First, ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. Second, IEM by use of the arrival time method estimates the impact location of loose parts. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.295-300
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• 1998

In this paper, a real time diagnostic algorithm fur estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. When the decision from ADM is concluded as the impact signal, the beginning time of burst-type signal, which the impact signal has usually such a form in time domain, provides the necessary data fur IEM. IEM by use of the arrival time method estimates the impact location of loose parts. The overall results of the estimated impact location are displayed on a computer monitor by the graphical mode and numerical data composed of the impact point, and thereby a plant operator can recognize easily the status of the impact event. This algorithm can perform the diagnosis process automatically and hence the operator's burden and the possible operator's error due to lack of expert knowledge of impact signals can be reduced remarkably. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.362-369
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• 2014

This study aimed to propose an accurate diagnostic method for osteoporosis by realizing a computer-aided diagnosis system with the application of the statistical analysis of texture features using digital images of lateral lumbar spine of patients with osteoporosis and providing reliable supplementary diagnostic information by model experimental research for early diagnosis of diseases. For these purposes, digital images of lateral lumbar spine of normal individuals and patients with osteoporosis were used in the experiments, and the values of statistical texture features on the set ROI were expressed in six parameters. Among the texture feature values of the six parameters of osteoporosis, the highest and lowest recognition rates of 95 and 80% were shown in average gray level and uniformity, respectively. Moreover, all the six parameters showed recognition rates of over 80% for osteoporosis: 82.5% in average contrast, 90% in smoothness, 87.5% in skewness, and 87.5% in entropy. Therefore, if a program developing into a computer-aided diagnosis system for medical images is coded based on the results of this study, it is considered possible to be applied to preliminary diagnostic data for automatic detection of lesions and disease diagnosis using medical images, to provide information for definite diagnosis of diseases, to diagnose by limited device, and to be used to shorten the time to analyze medical images.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.619-627
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• 2016

The purpose of this study is to investigate the effect of CT contrast agent and MRI contrast agent on the area dose in the body when using automatic exposure control system in general radiography. After making rectangular holes in the center of the abdominal thickness paraffin phantom, CT contrast agent and MRI contrast agent were respectively diluted with physiological saline solution for contrast medium dilution ratio of 10:0, 9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, 1:9, 0:10%. Each experiment was set to 78 kVp, 320 mA, which is the proper condition for KUB photography, and thereafter a total of 30 inspections were made for each dilution ratio using an automatic exposure control device, and the area dose corresponding to the dilution ratio of each contrast agent, Average comparison and correlation analysis were performed on the exposure index. As a result, the CT contrast agent and the MRI contrast agent appeared different in area dose according to the dilution ratio(p<0.05), and as the dilution ratio increased, the area dose increased for CT contrast agent and MRI contrast agent(P<0.05). In each test, the exposure index showed the manufacturer's recommendation of 200-800 EI value, and the exposure index and area dose increased as the area dose increased(p<0.05). In conclusion, CT contrast agent and MRI contrast agent confirmed to increase the area dose by general imaging test using all automatic exposure control device. Therefore, it is considered that it is necessary to perform it after the contrast medium has been excreted sufficiently when using usual imaging test after using the contrast agent in CT and MRI examination.

• Journal of Digital Contents Society
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• v.9 no.4
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• pp.601-606
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• 2008

U-Healthcare is one of the major applications in ubiquitous sensor network. U-Healthcare has potential to become a critical service for the people who immediately require emergency ambulatory attention. This paper describes about the real time pulse monitoring and reporting system, consisting of two components: thus, the one is a reliable bio-sensor that continuously monitors the pulse information of the subject, and the other is the automatic transfer system that transmits pulse information to both his/her family and hospital care system through the Base Station. In the hospital, this bio-information can be used to treat the patient accordingly. I designed the pulse information monitored by a bio-sensor module that transfers the pulse information to both the Base Station and the central monitoring system through transmitting protocols such as Zigbee and TCP/IP, as well as designed the architecture of information packets for the corresponding protocols. Furthermore, the central monitoring system automatically parses the pulse information of the subject into the web database server, which can continuously provides the real time information and status of the subject via an internet browser to the clients who are family members of the subject and the authenticated medical care personnel as well.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.399-402
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• 2005

For efficient and accurate diagnosis of ultrasound images, the time gain compensation (TGC) and dynamic range (DR) control of the ultrasound echo signal are important. TGC is for compensating the attenuation of the ultrasound echo signal along the depth, and DR is used to control the image contrast. In this paper, we propose an algorithm for finding the optimized values of TGC and DR automatically. For TGC, the degree of compensation is determined along the depth based on the effective attenuation estimation of ultrasound signal. For DR optimization, we introduce a novel cost function on the basis of the characteristics of ultrasound image, which provides the minimum value at the optimal DR. Experiments have been performed by applying the proposed algorithm to a real US imaging system. The results show that the algorithm automatically can determine the values of TGC and DR in realtime so that the subjective quality of the corresponding US image may be good enough for diagnosis.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.