• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• Plant Journal
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• v.15 no.2
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• pp.56-61
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• 2019

In this study, the failure analysis of the internal tube occurred in the high pressure feedwater heater for power generation boiler of 500 MW supercritical pressure coal fired power plant was investigated. I suggested a prediction model that can diagnose internal tube failure by changing the position of level control valve on the shell side and the suction flow rate of the boiler feedwater pump. The suggested prediction model is demonstrated through additional cases of feedwater system unbalance. The simultaneous comparison of the shell side level control valve position and the suction flow rate of the boiler feedwater pump compared to the normal operating state value, even in the case of the high pressure feedwater heater for the power boiler, It can be a powerful prediction diagnosis.

• Macromolecular Research
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• v.16 no.1
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• pp.15-20
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• 2008

The enhanced permeability and retention (EPR) effect is used extensively for the passive targeting of many macromolecular drugs for tumors. Indeed, the EPR concept has been a gold standard in polymeric anticancer drug delivery systems. This study investigated the tumoral distribution of self-assembled nanoparticles based on the EPR effect using fluorescein and radio-labeled nanoparticles. Self-assembled nanoparticles were prepared from amphiphilic chitosan derivatives, and their tissue distribution was examined in tumor-bearing mice. The size of the nanoparticles was controlled to be 330 run, which is a size suited for opening between the defective endothelial cells in tumors. The long-circulating polymer nanoparticles were allowed to gradually accumulate in the tumors for 11 days. The amount of nanoparticles accumulated in the tumors was remarkably augmented from 3.4%ID/g tissue at 1 day to 25.9%ID/g tissue at 11 days after i.v. administration. The self-assembled nanoparticles were sustained at a high level throughout the 14 day experimental period, indicating their long systemic retention in the blood circulation. The ${\gamma}$-images provided clear evidence of selective tumor localization of the $^{131}I$-labeled nanoparticles. Confocal microscopy revealed the fluorescein-labeled nanoparticles to be preferentially localized in the perivascular regions, suggesting their extravasation to the tumors through the hyperpermeable angiogenic tumor vasculature. This highly selective tumoral accumulation of nanoparticles was attributed to the leakiness of the blood vessels in the tumors and their long residence time in the blood circulation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.148-160
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• 2012

This study proposed more reasonable prediction models on compressive strength and carbonation of concrete structure and developed a more effective tunnel safety diagnosis and maintenance method through field application of the proposed prediction models. For this study, the Seoul Metro's Line 1 through Line 4 were selected as target structures because they were built more than 30 years ago and have accumulated numerous diagnosis and maintenance data for about 15 years. As a result of the analysis of compressive strength and carbonation, we were able to draw prediction models with accuracy of more than 80% and confirmed the prediction model's reliability by comparing it with the existing models. We've also confirmed field suitability of the prediction models by applying field, the average error of an estimate on compressive strength and carbonation depth was about 20%, which showed an accuracy of more than 80%. We developed a more effective maintenance method using durability prediction Map before field inspection. With the durability prediction Map, diagnostic engineers and structure managers can easily detect the vulnerable points, which might have failed to reach the standard of designed strength or have a high probability of corrosion due to carbonation, therefore, it is expected to make it possible for them to diagnose and maintain tunnels more effectively and efficiently.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.8
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• pp.1787-1798
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• 1995

Monitoring and diagnosis of the operating machine mean evaluating the condition of a machine such as the detection of the defects and the prediction of the time to failure in the machine elements, while it is running. In this study, a technique of automatic diagnosis using probability concept is studied and the analyses of the pattern comparison are introduced. An expert system, which is able to analyze the automatic identification of the multiple defects in the ball bearings, is also developed. Finally, to confirm the effectiveness of the programmed algorithms, some tests were made with specimens of the ball bearings involving the multiple defects. The proposed system reasonably predicts the defects.

• Clinical and Experimental Pediatrics
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• v.56 no.2
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• pp.45-51
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• 2013

Because nonalcoholic steatohepatitis can progress towards cirrhosis even in children, early detection of hepatic fibrosis and accurate diagnosis of nonalcoholic fatty liver disease (NAFLD) are important. Although liver biopsy is regarded as the gold standard of diagnosis, its clinical application is somewhat limited in children due to its invasiveness. Noninvasive diagnostic methods, including imaging studies, biomarkers of inflammation, oxidative stress, hepatic apoptosis, hepatic fibrosis, and noninvasive hepatic fibrosis scores have recently been developed for diagnosing the spectrum of NAFLD, particularly the severity of hepatic fibrosis. Although data and validation are still lacking for these noninvasive modalities in the pediatric population, these methods may be applicable for pediatric NAFLD. Therefore, noninvasive imaging studies, biomarkers, and hepatic fibrosis scoring systems may be useful in the detection of hepatic steatosis and the prediction of hepatic fibrosis, even in children with NAFLD.

• Journal of Mechanical Science and Technology
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• v.19 no.8
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• pp.1670-1681
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• 2005

In-situ diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the in-situ diagnosis is to predict the performance of a target chiller. Many models based on thermodynamics have been proposed for the purpose. However, they have to be modified from chiller to chiller and require profound knowledge of thermodynamics and heat transfer. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). The effect of sample data distribution on training the ANFIS is investigated. It is found that the data sampling over 10 days during summer results in a reliable ANFIS whose performance prediction error is within measurement errors. The reliable ANFIS makes it possible to prepare an energy audit and suggest an energy saving plan based on the diagnosed chilled water supply system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.1
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• pp.93-101
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• 2002

In this study, the stator form-winding sample coils based on silicone resin and polyimide were made for fault prediction and reliability estimation on the C-Class(200$\^{C}$ ) insulation system of traction motors. The complex accelerative degradation was periodically performed during 10 cycles, which was composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, the condition diagnosis test such as insulation resistance '||'&'||' polarization index, capacitance '||'&'||' dielectric loss and partial discharge properties were investigated in the temperature range of 20 ∼ 160$\^{C}$. Relationship among condition diagnosis tests was analyzed to find a dominative degradation factor and an insulation state at end-life point.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.23-40
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• 2021

In pancreatic cancer, imaging plays an essential role in surveillance, diagnosis, resectability evaluation, and treatment response evaluation. Pancreatic cancer surveillance in high-risk individuals has been attempted using endoscopic ultrasound (EUS) or magnetic resonance imaging (MRI). Imaging diagnosis and resectability evaluation are the most important factors influencing treatment decisions, where computed tomography (CT) is the preferred modality. EUS, MRI, and positron emission tomography play a complementary role to CT. Treatment response evaluation is of increasing clinical importance, especially in patients undergoing neoadjuvant therapy. This review aimed to comprehensively review the role of imaging in relation to the current treatment strategy for pancreatic cancer, including surveillance, diagnosis, evaluation of resectability and treatment response, and prediction of prognosis.