• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.5
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• pp.79-89
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• 1984

In this paper we present development of microprocessor-based 8-channel statistical multiplexer (SMUX). The hardware design includes one Z-80A CPU board with the clock rate of 4 MHz, one 16 Kbyte ROM board for program storage, one 16 Kbyte dynamic RAM board and three I/O boards, all connected through an S-100 compatible tristate bus. The SMUX can presently multiplex 8 channels with data rates ranging 50 bps to 9600 bps, but can be reduced to accommodate 4 channels by having a slight modification of software and removing one terminal I/O board. The system specifications meet CCITT recommendations X.25 link level, V.24, V.28, X.3 and X.28. Significant features of the SMUX are its capability of handling 4 input codes (ASCII, EBCDIC, Baudot, Transcode), the use of a dynamic buffer management algorithm, a diagnostic facility, and the efficient use of a single CPU for all system operation. Throughout the paper, detailed explanations are given as to how the hardware and software of the SMUX system have been designed efficiently.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.11
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• pp.1199-1204
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• 2011

The capability to bilaterally telemanipulate soft-tissues for medical applications could increase the quality of telemanipulation systems. Since most soft-tissue manipulation tasks include constrained motion interacting with an unknown and dynamic bioenvironment through contact, bilateral telemanipulation raises problems due to stability and transparency issues. It is well understood that knowledge of environments plays an important role in pursuing transparent telemanipulation and achieving telepresence, and in particular, online estimation of environmental parameters with an explicit environment model can improve these systems' performance. In this study, a continuum mechanics-based environment model with an online environmental property estimation algorithm and an adaptive telemanipulation control scheme is proposed. The proposed method can improve the telemanipulation performance in terms of stability and transparency and can offer valuable information (e.g., elastic modulus of soft tissues) pertaining to diagnostic examinations.

• Journal of Sasang Constitutional Medicine
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• v.26 no.3
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• pp.213-223
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• 2014

Objectives This study was aimed to develop the clinical practice guideline for Soyangin symptomatology. It discussed the principle and method of application of clinical practice guideline for Soyangin symptomatology which focuses on symptomatology, not disease. Methods Based on the previous guidelines, we assessed the guidelines by Appraisal of Guidelines for Research and Evaluation (AGREE II). After AGREE II assessment, we chose and revised the clinical practice guideline. Member of writing committee reviewed and examined "Donguisusebowon" and many articles for developing clinical practice guidelines. Draft of clinical practice guideline was reviewed by advisory committee and approved by Society of Sasang Constitutional Medicine. Results & Conclusions By researching and discussing the Soyangin symptomatology, we establish the evaluation criteria for diagnosis including classification, definition and develop diagnostic algorithm and treatment assessing tool.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.467-474
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• 1999

In this paper, Fault tolerant digital governor, using duplex I/O module and triplex CPU module and also 2 out of 3 voting algorithm and adding self diagnostic ability, is designed to realize ceaseless controlling and to improve the reliability of control system. The processor module of the system(SIDG-3000) is developed based on MC68EC040 32 Bit of Motorola, which guaranteed high quality of the module ,and SRAM for data also SRAM for command are separated. The process module also includes inter process communication function and power back up function (SRAM for back-up). System reliability is estimated by using the model of Markov process. The reliability of triplex system in mission time can be improved about 1.8 times in reliability 86%. 2.8 times in 95 %, 6 times in 99 % compared with a single control system. Designed digital governor system is applied after modelling of the steam turbine generator system of Buk-Cheju Thermal Power Plant. Simulation is carried out to prove the effectiveness of the designed digital governor system

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of KIISE
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• v.42 no.7
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• pp.910-918
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• 2015

This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2077-2082
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• 2008

Recent the advanced technologies in medical imaging such as magnetic resonance imaging (MRI) and computed tomography (CT) make doctors improve the diagnostic skill with detailed anatomical information. In general, it is necessary to get a number of MRI images in order to obtain more detail information. However, the performance of MRI machines of privately run hospitals is not good and thus we may obtain only a few of MRI images. If 3D surface reconstruction is accomplished with a few slices, then it generates 3D surface of poor qualify. This paper propose a way to Set a 3D surface of high quality from a few of number of slices. First of all, our algorithm detects the boundary of tissues which we want to reconstruct as a 3D object and find out the set of vortices on the boundary. And then we generate a 3D implicit surface to interpolate the boundary points by using radial basis function. Lastly, we render the 3D implicit surface by using Marching cube algorithms.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.308-313
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• 2019

Parkinson's disease, one of the three major diseases in old age, has more than 70 % of patients with speech disorders, and recently, diagnostic methods of Parkinson's disease through speech signals have been devised. In this paper, we propose a method of diagnosis of Parkinson's disease based on deep residual gated recurrent neural network using speech features. In the proposed method, the speech features for diagnosing Parkinson's disease are selected and applied to the deep residual gated recurrent neural network to classify Parkinson's disease patients. The proposed deep residual gated recurrent neural network, an algorithm combining residual learning with deep gated recurrent neural network, has a higher recognition rate than the traditional method in Parkinson's disease diagnosis.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.100-106
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• 2021

Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.293-309
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• 2022

Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns. Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called "problematic smartphone use survey" was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed. Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users' age, marriage and smartphone usage patterns can be used as predictors to determine whether users are addicted to smartphones. Other demographic characteristics such as sex or region did not appear to significantly affect smartphone overdependence levels. Research implications or Originality - While there are some studies that predict smartphone overdependence level using machine learning techniques, but the studies only present algorithm performance based on survey data. In this study, based on the information gain measure, questions that have more influence on the smartphone overdependence level are presented, and the performance of algorithms according to the questions is compared. Through the results of this study, it is shown that smartphone overdependence level can be predicted with less information if questions about smartphone use are given appropriately.