• Journal of Society of Preventive Korean Medicine
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• v.24 no.3
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• pp.57-67
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• 2020

Background : The Life cycle Health Promotion Programs using Traditional Korean Medicine (the Life cycle HaPPs-TKM) are the on-going 3rd stage projects that have centered on the development and dissemination of the standard life cycle HaPPs-TKM in the local community. The purpose of the study was to introduce the development background of the standard life cycle HaPPs-TKM and to suggest its activation plan. Methods : Academic and government research reports on the life cycle HaPPs-TKM were analyzed to introduce the development process, development backgrounds and the details of KM-HPP for each life cycle, such as infants and toddlers, adolescents, pregnant women, adults and the elderly. Results : We reviewed the development process of the standard life cycle HaPP-TKM consisted of a series of diagnosis on community members' health problems, establishment of project purpose, research on the involvement of KM intervention in a project, and final development of the project model. And we rediscovered that in the development backgrounds of KM-HPP, there were beneficial goals to manage and promote public health conditions for each life cycle. Conclusion : To activate life cycle HaPPs-TKM, we would recommend that activation plan should include six factors through systematic analysis of research reports. These factors consist of diversified goals for each life-cycle, competency reinforcement of local project manager, diversified Korean Medicinal modalities to enhance Sasang Constitution and Qigong, development of standard Outcome Index, periodical holding of performance contest, and improved guidance of government and associated entities through whole process of HaPP-TKM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Health Policy and Management
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• v.30 no.4
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• pp.433-437
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• 2020

Cancer is a disease which has the huge burden in worldwide, and cancer is the number one cause of death in Korea. At this point, the new framework for cancer monitoring index is required for regional cancer monitoring. Especially, cancer survivors are the important target which is rapidly increasing recently, also cancer survivor's quality of care should be considered in the cancer monitoring index framework. To develop the Multidimensional Cancer Monitoring Index considering cancer survivor's quality of care, we took into account cancer continuum which including prevention, detection, diagnosis, treatment, survivorship, assessment of quality of care and monitoring cancer patient, and end-of life care for stage. For target, components of health care delivery system such as patient, family, provider, payer, and policy maker are included. Also, Donabedian model which is a framework for examining health services and evaluating quality of health care such as structure, process, and outcome is applied to contents. This new cancer monitoring framework which includes multidimensional components could help to develop regional cancer monitoring index, and to make national cancer management and prevention policy in the future.

• Health Policy and Management
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• v.31 no.1
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• pp.91-99
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• 2021

Background: The Korean government introduced National Cancer Control Program and strengthening national health insurance coverage for cancer patients. Although many positive effects have been observed, there are also many concerns about cancer management such as patient concentration or time-to-treatment. Thus, we investigated the association between the time-to-treatment and survival of cancer patients, and compared regional differences by time trend. Methods: The data used in this study were national health insurance claims data that included patients diagnosed with lung cancer and received surgical treatment between 2005 and 2015. We conducted survival analysis with Cox proportional hazard model for the association between time-to-treatment and survival in lung cancer. Additionally, we compared the regional differences for time-to-treatment by time trend. Results: A total of 842 lung cancer patients were included, and 52.3% of lung cancer patients received surgical treatment within 30 days. Patients who received surgical treatment after 31 days had higher 5-year or 1-year mortality compared to treatment within 30 days (5-year: hazard ratio [HR], 1.566; 1-year: HR, 1.555; p<0.05). There were some regional differences for time-to-treatment, but it was generally reduced after 2010. Conclusion: Delayed surgical treatment after diagnosis can negatively affect patient outcomes in cancer treatment. To improve cancer control strategies, there are needed to analyze the healthcare delivery system for cancer care considering the severity and types of cancer.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.65-75
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• 2021

This study explores multiple variables of an OTT service for discovering hidden relationship between rating and the other variables of each successful and failed content, respectively. In order to extract key variables that are strongly correlated to the rating across the contents, this work analyzes 170 Netflix original dramas and 419 movies. These contents are classified as success and failure by using the rating site IMDb, respectively. The correlation between the contents, which are classified via rating, and variables such as violence, lewdness and running time are analyzed to determine whether a certain variable appears or not in each successful and failure content. This study employs a regression analysis to discover correlations across the variables as a main analysis method. Since the correlation between independent variables should be low, check multicollinearity and select the variable. Cook's distance is used to detect and remove outliers. To improve the accuracy of the model, a variable selection based on AIC(Akaike Information Criterion) is performed. Finally, the basic assumptions of regression analysis are identified by residual diagnosis and Dubin Watson test. According to the whole analysis process, it is concluded that the more director awards exist and the less immatatable tend to be successful in movies. On the contrary, lower fear tend to be failure in movies. In case of dramas, there are close correlations between failure dramas and lower violence, higher fear, higher drugs.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• 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.