• 대한방사선협회지
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• 제27권2호
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• pp.86-94
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• 2001

I. Purpose : There are many kinds of MRI techniques and there have been new techniques spreading clinically with the development of software. Clinical diagnosis value has been comparatively studied by conducting the techniques of SE, TSE, and TGSE on the

• Journal of Multimedia Information System
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• 제6권2호
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.209-217
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• 2021

Magnetic resonance angiography (MRA) plays an important role in accurate diagnosis and appropriate treatment planning for patients with arterial disease. Contrast-enhanced (CE) MRA is fast and robust, offering hemodynamic information of arterial flow, but involves the risk of a side effect called nephrogenic systemic fibrosis. Various non-contrast-enhanced (NCE) MRA techniques have been developed by utilizing the fact that arterial blood is moving fast compared to background tissues. NCE MRA is completely free of any safety issues, but has different drawbacks for various approaches. This review article describes basic principles of CE and NCE MRA techniques with a focus on how to generate angiographic image contrast from a pulse sequence perspective. Advantages, pitfalls, and key applications are also discussed for each MRA method.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.135-142
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• 2022

Despite global medical advancements, many patients are misdiagnosed, and more people are dying as a result. We must now develop techniques that provide the most accurate diagnosis of heart disease based on recorded data. To help immediate and accurate diagnose of heart disease, several data mining methods are accustomed to anticipating the disease. A large amount of clinical information offered data mining strategies to uncover the hidden pattern. This paper presents, comparison between different classification techniques, we applied on the same dataset to see what is the best. In the end, we found that the Random Forest algorithm had the best results.

• Journal of Genetic Medicine
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• 제20권1호
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• pp.1-5
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• 2023

Until now, rare disease studies have mainly been carried out by detecting simple variants such as single nucleotide substitutions and short insertions and deletions in protein-coding regions of disease-associated gene panels using diagnostic next-generation sequencing in association with patient phenotypes. However, several recent studies reported that the detection rate hardly exceeds 50% even when whole-exome sequencing is applied. Therefore, the necessity of introducing whole-genome sequencing is emerging to discover more diverse genomic variants and examine their association with rare diseases. When no diagnosis is provided by whole-genome sequencing, additional omics techniques such as RNA-seq also can be considered to further interrogate causal variants. This paper will introduce a description of these multi-omics techniques and their applications in rare disease studies.

• Korean Journal of Radiology
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• 제22권5호
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• pp.677-687
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• 2021

Microvascular ultrasound (US) techniques are advanced Doppler techniques that provide high sensitivity and spatial resolution for detailed visualization of low-flow vessels. Microvascular US imaging can be applied to breast lesion evaluation with or without US contrast agents. Microvascular US imaging without a contrast agent uses a sophisticated wall filtering system to selectively obtain low-flow Doppler signals from overlapped artifacts. Microvascular US imaging with second-generation contrast agents amplifies flow signals and makes them last longer, which facilitates hemodynamic evaluation of breast lesions. In this review article, we will introduce various microvascular US techniques, explain their clinical applications in breast cancer diagnosis and radiologic-histopathologic correlation, and provide a summary of a recent radiogenomic study using microvascular US.

• 한국전문물리치료학회지
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• 제12권3호
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• pp.67-73
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• 2005

Prospective memory (PM) is related to remember to carry out a previously intented behaviour. The purpose of this study was to develop a paradigm for measuring PM function to diagnosis in mild cognitive impairment 1 or brain injury in patients 2. among brain injured patients Thirty-eight normal healthy subjects participated in current study. The paradigm was composed of four conditions: a baseline and three intention conditions (expectation, execution 1 and 2). In the expectation condition, subjects were asked to make a new response to intented stimuli during ongoing task, but the intented stimuli never occurred. In the execution 1 (one type of expected stimulus) and 2 (two types of expected stimuli), the intended stimuli did occur in 20% of trials. The reaction time and error rate were calculated in each condition. Repeated measures using ANOVA of subject's mean reaction times (RTs) and mean error rates (ERs) showed main effects of conditions during ongoing task. The comparison of PM tasks in executive condition 1 and 2 also showed significance in RTs and ERs. This paradigm reflects sufficiently the performance of prospective memory function during ongoing task in normal individuals. Thus, we suggest that the paradigm will be helpful to study neural network of PM function using brain imaging techniques and diagnosis of PM dysfunction.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 E
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• pp.2245-2247
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• 1999

Insulation diagnosis techniques of power equipments for the stable power supply and prevention from accidents are of high importance. Diagnosis techniques is able to prevent from large accidents before they happen by finding signs of the accidents. From this point of view, this paper simulated discharge progress and partial discharge using needle-plan electrode system in air, studied the distribution of frequency spectrum of the radiated electromagnetic waves using biconical antenna and spectrum analyzer. From results of this study. the new method was introduced for measurement and analyzation of the radiated electromagnetic waves in accordance with discharge progress in air. Besides, according to the consideration of the mutual relation between frequency spectrum of the radiated electromagnetic waves and discharge progress, it was confirmed that detecting partial discharge and estimating discharge progress were possible.

• Steel and Composite Structures
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• 제23권6호
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• pp.683-690
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• 2017

In the stability and sensitivity design and diagnosis approaches, there are various methodologies available. Bond graph modeling by lumping technique is one of the universal methodologies in methodical analysis used by many researchers in all over the world. The accuracy of the method is validated in different arenas. Bond graphs are a concise, pictorial representation of the energy storage, dissipation and exchange mechanisms of interacting dynamic systems, subsystems and components. This paper proposes a bond graph modeling for distributed parameter systems using lumping techniques. Therefore, a steel frame structure was modeled to analyze employing bond graph modeling of distributed system using lumping technique. In the analytical part, the effectiveness of bond graphs to model this system is demonstrated. The dynamic responses of the system were computed and compared with those computed from the finite element analysis. The calculated maximum deflection time histories were found to be comparable. The sensitivity and the stability of the steel frame structure was also studied in different aspects. Thus, the proposed methodology, with its simplicity, can be used for stability and sensitivity analyses as alternative to finite element method for steel structures. The major value brought in the practical design is the simplicity of the proposed method for steel structures.

• KEPCO Journal on Electric Power and Energy
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• 제7권2호
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.