• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.928-933
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• 2018

Nowadays, gastropathy is a common disease. As endoscopic equipment are developed and used widely, it is possible to provide a large number of endoscopy images. Computer-aided Diagnosis (CADx) systems aim at helping physicians to identify possibly malignant abnormalities more accurately. In this paper, we present a CADx system to detect and classify the abnormalities of gastric lesions which include bleeding, ulcer, neuroendocrine tumor and cancer. We used an Inception module based deep learning model. And we used data augmentation for learning. Our preliminary results demonstrated promising potential for automatically labeled region of interest for endoscopy doctors to focus on abnormal lesions for subsequent targeted biopsy, with Az values of Receiver Operating Characteristic(ROC) curve was 0.83. The proposed CADx system showed reliable performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1847-1853
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• 2008

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.271-276
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• 2017

Cancer biomarkers are using in the diagnosis, staging, prognosis and prediction of disease progression. But, there are not sufficiently profiled and validated in early detection and risk classification of prostate cancer. In this study, we have devoted to finding a panel of serum biomarkers that are able to detect the diagnosis of prostate cancer. The serum samples were consisted of 111 prostate cancer and 343 control samples and examined. Eleven biomarkers were constructed in this study, and then nine biomarkers were relevant to candidate biomarkers by using t test. Finally, four biomarkers, PSA, ApoA2, CYFRA21.1 and TTR, were selected as the prostate cancer biomarker panel, logistic regression was used to identify algorithms for diagnostic biomarker combinations(AUC = 0.9697). A panel of combination biomarkers is less invasive and could supplement clinical diagnostic accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.187-195
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• 2019

In the fourth industrial revolution era, various commercial smart platforms for smart system implementation are being developed and serviced. However, since most of the smart platforms have been developed for general purposes, they are difficult to apply / utilize because they cannot satisfy the requirements of real-time data management, data visualization and data storage of smart factory system. In this paper, we implemented an open source based smart manufacturing big data platform that can manage highly efficient / reliable data integration for the diagnosis diagnostic system of manufacturing robots.

• ETRI Journal
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• v.37 no.2
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• pp.233-240
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• 2015

The novelty of this study resides in a 6"-wafer-level microfabrication protocol for a microdevice with a fluidic control system for the separation of circulating tumor cells (CTCs) from human whole blood cells. The microdevice utilizes a lateral magnetophoresis method based on immunomagnetic nanobeads with anti-epithelial cell adhesive molecule antibodies that selectively bind to epithelial cancer cells. The device consists of a top polydimethylsiloxane substrate for microfluidic control and a bottom substrate for lateral magnetophoretic force generation with embedded v-shaped soft magnetic microwires. The microdevice can isolate about 93% of the spiked cancer cells (MCF-7, a breast cancer cell line) at a flow rate of 40/100 mL/min with respect to a whole human blood/buffer solution. For all isolation, it takes only 10 min to process 400 mL of whole human blood. The fabrication method is sufficiently simple and easy, allowing the microdevice to be a mass-producible clinical tool for cancer diagnosis, prognosis, and personalized medicine.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.59-66
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• 2016

This study analyzed the relationship between the convergence performance in hospital. This study examined previous research and calculated centralized index using diagnosis related groups. In addition, multiple regression analysis was used based on LOS in order to understand the effect of focused strategy which quality of medical inpatient service. The centralized level was examined by analyzing national inpatient sample data using 'Internal Herfindahl-Hirshman index' This study is significant because it reviewed medical inpatient service quality by measuring hospital centralized level which has been rarely studied before Korea.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.47-53
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• 2018

The actual condition of safety management and problems for hazardous material facilities in national industrial complex are investigated and analyzed in fields of hazardous material facilities safety, fire fighting equipment safety, building safety, space safety. In most cases, the failure to comply with legal requirements and the maintenance of fire safety have been pointed out. As a result, the total number of problems identified was 466 and problems in the field of hazardous material facilities safety have been pointed out the most. Also, it was analyzed that the number of problems identified in the manufacturing facilities and general handling centers among the hazardous materials facilities was the highest. Therefore, it will contribute to strengthening the safety management capability of the national industrial complex in the future by suggesting the convergence direction of the safety diagnosis system and the systematic introduction of the precise safety diagnosis.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.119-124
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• 2018

The augmented reality(AR) technology enables to acquire various image information at the same time by combining virtual image information with the user's viewpoint. These AR technologies have been used to visualize patients' organs and tissues during surgery and diagnosis in the fields of Image-Guide Operation, Surgical Training, and Image Diagnosis by medical convergence, and provides the most effective surgical methods. In this paper, we study the technical features and application methods of each element technology for medical fusion of AR technology. In the AR technology for medical convergence, display, marker recognition and image synthesis interface technology is essential for efficient medical image. Such AR technology is considered to be a way to drastically improve current medical technology in the fields of image guide surgery, surgical education, and imaging diagnosis.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.277-287
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• 2021

The purpose of this study is to establish a model that can quantitatively diagnose personal color. Representative color systems for personal colors have limitations in that it oversimplify personal color diagnosis types or it is difficult to distinguish objective differences between diagnosis types. To develop a brand new color system that enhances this, a PCCS color system capable of logical color was introduced and reclassified based on the four main properties of color. Twenty diagnostic types, which are more diverse than the existing color system were proposed and a quantitative method was used to evaluate the degree of harmony with a subject to find an optimized type of subject. The experimenter's individual competency and subjective intervention were minimized by devising a matrix in which a type suitable for the subject is derived when the coded evaluation result is substituted. Finally a quantitative diagnosis model of personal color consisting of three stages: property diagnosis, coding, and seasonal diagnosis was constructed. It can be seen that this will give diversity, reliability, and accuracy to the existing diagnostic methods.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.