• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.200-206
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• 2022

This study is to develop database by an experimental method for the development of condition based maintenance for auxiliary equipment in marine engine systems. Existing ships have been performing regular maintenance, so the actual measurement data development is very incomplete. Therefore, it is best to develop a database on land tests. In this paper, a database developed by an experimental method is presented. First, failure case analysis and reliability analysis were performed to select a failure mode. For the failure simulation test, a test bed for land testing was developed. The failure simulation test was performed based on the failure simulation scenario in which the failure simulation test plan was defined. A 1.5TB failure simulation database has been developed, and it is expected to serve as a basis for ship failure diagnosis and prediction algorithm model development.

• Smart Media Journal
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• v.10 no.4
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• pp.45-54
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• 2021

The skin is the body's first line of defense against external infection. When a skin disease strikes, the skin's protective role is compromised, necessitating quick diagnosis and treatment. Recently, as artificial intelligence has advanced, research for technical applications has been done in a variety of sectors, including dermatology, to reduce the rate of misdiagnosis and obtain quick treatment using artificial intelligence. Although previous studies have diagnosed skin diseases with low incidence, this paper proposes a method to classify common illnesses such as warts and corns using a convolutional neural network. The data set used consists of 3 classes and 2,515 images, but there is a problem of lack of training data and class imbalance. We analyzed the performance using a deep metric loss function and a cross-entropy loss function to train the model. When comparing that in terms of accuracy, recall, F1 score, and accuracy, the former performed better.

• Advances in nano research
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• v.10 no.5
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• pp.461-470
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• 2021

Infection is one of the major mortality causes throughout the globe. Nuclear medicine plays an important role in diagnosis of deep infections such as osteomyelitis, arthritis infection, heart valve and heart prosthesis infections. Techniques such as labeled leukocytes are sensitive and selective for tracking the inflammations but they are not suitable for differentiating infection from inflammation. Anionic linear-globular dendrimer-G₂ was synthesized then conjugation to gemifloxacin antibiotic. The structures were identified by FT-IR, ¹H-NMR, C-NMR, LC-MS and DLS. The toxicity of gemifloxacin and dendrimer-gemifloxacin complex was compared by MTT test. Dendrimer-G₂-gemifloxacin was labeled by Technetium-99m and its in-vitro stability and radiochemical purity were investigated. In-vivo biodistribution and SPECT imaging were studied in a rabbit model. Identify and verify the structure of the each object was confirmed by FT-IR, ¹H-NMR, C-NMR and LC-MS, also, the size and charge of this compound were 128 nm and -3/68 mv respectively. MTT test showed less toxicity of the dendrimer-G₂-gemifloxacin than free gemifluxacin (P < 0.001). Radiochemical yield was > %98. Human serum stability was 84% up to 24 h. Biodistribution study at 50 min, 24 and 48 h showed that the complex is significantly absorbed by the intestine and accumulation in the lungs and affects them, finally excreted through the kidneys, biodistribution results are consistent with results from full image means of SPECT/CT technique.

• The Journal of Internal Korean Medicine
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• v.42 no.2
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• pp.175-183
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• 2021

Diabetes mellitus, commonly known as diabetes, comprises a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. Diabetes is classified into type 1 diabetes and type 2 diabetes, and the incidence and prevalence of diabetes, mostly type 2, have increased remarkably in recent decades. A diagnosis of type 2 diabetes is greatly influenced by heredity, and it is important to prevent diabetes in people with a family history by improving lifestyle and environmental factors. Controlling overeating, obesity, lack of exercise, and stress is effective to prevent diabetes. The patient in this case report had impaired fasting glucose and mild hyperlipidemia. The patient experienced improvements in these sequelae after the administration of herbal medicine (Yangkyuksanwha-tang) for 12 weeks and the implementation of a plant-based diet. The complete blood count, XXXX, fasting blood sugar, HbA1c, insulin, and C-peptide levels were measured, and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and HOMA-β were calculated according to the FBS and fasting insulin levels. Total cholesterol, triglyceride, FBS, and HbA1c levels all decreased after 12 weeks compared with baseline measures. There was no change in the insulin secretory ability; the C-peptide level did not change as a result of β-cell function; and the HOMA-β level reflected an improved insulin secretory ability.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• BMB Reports
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• v.55 no.11
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• pp.547-552
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• 2022

Sorafenib, originally identified as an inhibitor of multiple oncogenic kinases, induces ferroptosis in hepatocellular carcinoma (HCC) cells. Several pathways that mitigate sorafenib-induced ferroptosis confer drug resistance; thus strategies that enhance ferroptosis increase sorafenib efficacy. Orphan nuclear receptor estrogen-related receptor γ (ERRγ) is upregulated in human HCC tissues and plays a role in cancer cell proliferation. The aim of this study was to determine whether inhibition of ERRγ with DN200434, an orally available inverse agonist, can overcome resistance to sorafenib through induction of ferroptosis. Sorafenib-resistant HCC cells were less sensitive to sorafenibinduced ferroptosis and showed significantly higher ERRγ levels than sorafenib-sensitive HCC cells. DN200434 induced lipid peroxidation and ferroptosis in sorafenib-resistant HCC cells. Mechanistically, DN200434 increased mitochondrial ROS generation by reducing glutathione/glutathione disulfide levels, which subsequently reduced mTOR activity and GPX4 levels. DN200434 induced amplification of the antitumor effects of sorafenib was confirmed in a tumor xenograft model. The present results indicate that DN200434 may be a novel therapeutic strategy to re-sensitize HCC cells to sorafenib.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.433-443
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• 2022

Purpose: In this paper, we propose a study of deep learning algorithms that estimate and predict sarcopenia by exploiting the high penetration rate of smart devices. Method: To utilize deep learning techniques, experimental data were collected by using the inertial sensor embedded in the smart device. We implemented a smart device application for data collection. The data are collected by labeling normal and abnormal gait and five states of running, falling and squat posture. Result: The accuracy was analyzed by comparative analysis of LSTM, CNN, and RNN models, and binary classification accuracy of 99.87% and multiple classification accuracy of 92.30% were obtained using the CNN-LSTM fusion algorithm. Conclusion: A study was conducted using a smart sensoring device, focusing on the fact that gait abnormalities occur for people with sarcopenia. It is expected that this study can contribute to strengthening the safety issues caused by sarcopenia.

• BMB Reports
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• v.55 no.6
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• pp.281-286
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• 2022

Hepatocellular carcinoma is a major health burden, and though various treatments through much research are available, difficulties in early diagnosis and drug resistance to chemotherapy-based treatments render several ineffective. Cancer stem cell model has been used to explain formation of heterogeneous cell population within tumor mass, which is one of the underlying causes of high recurrence rate and acquired chemoresistance, highlighting the importance of CSC identification and understanding the molecular mechanisms of CSC drivers. Extracellular CSC-markers such as CD133, CD90 and EpCAM have been used successfully in CSC isolation, but studies have indicated that increasingly complex combinations are required for accurate identification. Pseudogene-derived long non-coding RNAs are useful candidates as intracellular CSC markers - factors that regulate pluripotency and self-renewal - given their cancer-specific expression and versatile regulation across several levels. Here, we present the use of microarray data to identify stemness-associated factors in liver cancer, and selection of sole pseudogene-derived lncRNA ZNF204P for experimental validation. ZNF204P knockdown impairs cell proliferation and migration/invasion. As the cytosolic ZNF204P shares miRNA binding sites with OCT4 and SOX2, well-known drivers of pluripotency and self-renewal, we propose that ZNF204P promotes tumorigenesis through the miRNA-145-5p/OCT4, SOX2 axis.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.243-250
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• 2023

Chest X-ray is radiological examination for xeamining the lungs and haert, and is particularly widely used for diagnosing lung disease. Since the quality of these chest X-rays can affect the doctor's diagnosis, the process of evaluating the quality must necessarily go through. This process can involve the subjectivity of radiologists and is manual, so it takes a lot of time and csot. Therefore, in this paper, based on the chest X-ray quality assessment guidelines used in clinical settings, we propose a tool that automates the five quality assessments of artificial shadow, coverage, patient posture, inspiratory level, and permeability. The proposed tool reduces the time and cost required for quality judgment, and can be further utilized in the pre-processing process of selecting high-quality learning data for the development of a learning model for diagnosing chest lesions.