• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.61-77
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• 2022

The purpose of this study is to explain intention to use untact medical diagnosis and consultation services. We carried out the analysis of the survey data using Smart PLS 3.0 to test the hypotheses how the expected benefit variables and perceived risk variables of untact medical diagnosis and consultation services affect intention to use. According to the empirical analysis results, this study confirmed that quality of telemedicine service had a significant effects on perceived usefulness, Perceived Easy of Use. And accessibility had a significant effects on perceived easy of use, cost saving and expected benefits had a significant effects on use Intention of untact medical diagnosis and consultation services. Performance risk and service risk had a significant effect on medical risk. And medical risk had a significant negative(-) effects on use Intention of untact medical diagnosis and consultation services. This study has its meaning because it found out that it deals structurally and expansively with use intention of untact medical diagnosis and consultation services through positive and negative factors.

• Korean Journal of Oriental Medicine
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• v.13 no.3
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• pp.119-124
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• 2007

Introduction: In Oriental medicine, the status of tongue is the important indicator to diagnose one's health, because it represents physiological and clinicopathological changes of inner parts of the body. The method of tongue diagnosis is not only convenient but also non-invasive, so tongue diagnosis is most widely used in Oriental medicine. By the way, since tongue diagnosis is affected by examination circumstances a lot, its performance depends on a light source, degrees of an angle, a medical doctor's condition etc. Therefore, it is not easy to make an objective and standardized tongue diagnosis. In order to solve this problem, in this study, we tried to design a discriminant function for thick and thin coating with color vectors of preprocessed image. Method: 52 subjects, who were diagnosed as white-coated tongue, were involved. Among them, 45 subjects diagnosed as thin coating and 7 subjects diagnosed as thick coating by oriental medical doctors, and then their tongue images were obtained from a digital tongue diagnosis system. Using those acquired tongue images, we implemented two steps: Preprocessing and image analyzing. The preprocessing part of this method includes histogram equalization and histogram stretching at each color component, especially, intensity and saturation. It makes the difference between tongue substance and tongue coating was more visible, so that we can separate tongue coating easily. Next part, we analyzed the characteristic of color values and found the threshold to divide tongue area into coating area. Then, from tongue coating image, it is possible to extract the variables that were important to classify thick and thin coating. Result : By statistical analysis, two significant vectors, associated with G, were found, which were able to describe the difference between thick and thin coating very well. Using these two variables, we designed the discriminant function for coating classification and examined its performance. As a result, the overall accuracy of thick and thin coating classification was 92.3%. Discussion : From the result, we can expect that the discriminant function is applicable to other coatings in a similar way. Also, it can be used to make an objective and standardized diagnosis.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.44-55
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• 2017

Many enterprises have recently been pursuing process innovation or improvement to attain their performance goal. To comprehensively support business process execution, the concept of business process management (BPM) has been widely adopted. A life cycle of BPM is composed of process diagnosis, (re)design, and enactment. For aligning with enterprise strategies, all BPM activities must be closely related to performance metrics because the metrics are the drivers and evaluators of business process operations. The objective of this paper is to propose a life cycle-based BPM framework integrated with the process-based performance measurement model, in which business processes are systematically interrelated with key performance indicators (KPIs) during an entire BPM life cycle. By using the proposed BPM framework, company practitioners involved in process innovation projects can easily and efficiently find the most influencing processes upon enterprise performance in the process diagnosis phase, evaluate the performance of newly designed process in the process (re)design phase, monitor the KPIs of new business process, and adjust business process activities in the process execution phase through the BPM life cycle.

• Journal of Aerospace System Engineering
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• v.13 no.1
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• pp.38-46
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• 2019

There is a growing interest in engine diagnostic technology for gas turbine engines. An engine simulation program, precisely simulating the engine performance, is required in order to apply it to the engine diagnosis technology for engine health monitoring. In particular, the simulation program can predict not only design point performance but also off-design point and partial load performance in accurate. So the engine simulation program for the 2-spool separate flow type turbofan engine was developed and the JT9D-7R4G engine of PW(Pratt & Whitney) was analyzed. The steady-sate performance analysis is conducted at both design and off-design points in flight path and the differences between analysis results of takeoff and cruise conditions are compared. The effect of Reynold's correction method was analyzed as a scaling method of the engine component performance. The simulation results was compared with NPSS.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1705-1711
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• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.635-643
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• 2021

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (¹⁸F-FDG, 5.18 MBq/kg / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUV_max (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

• Korean Journal of Veterinary Research
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• v.63 no.2
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• pp.19.1-19.6
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• 2023

Rapid immunochromatography test (RICT) kits are commonly used for the diagnosis of canine parvovirus (CPV) because of their rapid turnaround time, simplicity, and ease of use. However, the potential for cross-reactivity and low sensitivity can yield false-positive or false-negative results. There are 4 genotypes of CPV. Therefore, evaluating the performance and reliability of RICT kits for CPV detection is essential to ensure accurate diagnosis for appropriate treatment. In this study, we evaluated the performance of commercial RICT kits in the diagnosis of all CPV genotypes. The cross-reactivity of 6 commercial RICT kits was evaluated using 8 dog-related viruses and 4 bacterial strains. The limit of detection (LOD) was measured for the 4 genotypes of CPV and feline panleukopenia virus. The tested kits showed no cross-reactivity with the 8 dog-related viruses or 4 bacteria. Most RICT kits showed strong positive results for CPV-2 variants (CPV-2a, CPV-2b, and CPV-2c). However, the 2 kits produced negative results for CPV-2 or CPV-2b at a titer of 10⁵ FAID₅₀/mL, which may result in inaccurate diagnoses. Therefore, some kits need to improve their LOD by increasing their binding efficiency to detect all CPV genotypes.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.