• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.3
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• pp.58-66
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• 2019

In this study, the start-up characteristics of a staged combustion engine were analyzed numerically based on relational equation modeling of the entire engine components. The start-up characteristics were extensively analyzed considering the transient period of the total engine system from the start-up sequence till the steady-state of the engine. The performance characteristics of the engine components such as RPM of engine power-pack, chamber pressure and O/F ratio of pre-burner, and mass flow of propellants in the start-up period were investigated. Furthermore, the calculated engine data were compared satisfactorily with the experimental data. Through the comparison of data, successful validation of present engine start-up analysis has been obtained.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.737-747
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• 2019

The purpose of this study was the development and effectiveness of the Emotional Leadership program by Enneagramr. A nonequivalent control group pretest-posttest design was used to measure the effects of the program on the Emotional Leadership. Participants in a validation therapy program were 30 Leaders who attended 6 weekly sessions of approximately 60 minutes each. Effexts were evaluated through pre and post tests that included measurement of Emotional Leadership, Organizational Performance, and the Self Efficacy. The study variables were analyzed using ${\chi}^2$-test, Fisher's exact test, t-test with SPSS statistical package. For the Leaders, a statistically significand increaded in Emotional Leadership, Organizational Performance. Therefore, it is recommended to utilize the Emotional Leadership Program for Nurse's Leaders by improving the Emotional Leadership and Organizational Performance.

• Convergence Security Journal
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• v.17 no.5
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• pp.3-10
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• 2017

Standard Protocol Optimized for Resource-Constrained IoT Environment Constrained Application Protocol (CoAP) supports web-based communication between a sensor node in the IoT environment and a client on the Internet. The CoAP is a Request / Response model that responds to the client's CoAP Request message by responding with a CoAP Response message from the server. CoAP recommends the use of CoAP-DTLS for message protection. However, validation of the use of DTLS in the IoT environment is underway. We analyze CoAP and DTLS security mode, evaluate performance of secure channel creation time, security channel creation step time, and RAM / ROM consumption through Cooja simulator and evaluate the possibility of real environment application.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.23-29
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• 2019

In this paper, the mood of images was classified into eight categories through a deep convolutional neural network and video was automatically generated using proper background music. Based on the collected image data, the classification model is learned using a multilayer perceptron (MLP). Using the MLP, a video is generated by using multi-class classification to predict image mood to be used for video generation, and by matching pre-classified music. As a result of 10-fold cross-validation and result of experiments on actual images, each 72.4% of accuracy and 64% of confusion matrix accuracy was achieved. In the case of misclassification, by classifying video into a similar mood, it was confirmed that the music from the video had no great mismatch with images.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.49.3-50
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• 2018

Korea has kept its records of astronomical phenomena since around 2,000 years ago. However, the contents and scope of relevant service have been limited for researchers who need those records due to lack of complete data collection. In this regard, it is necessary to establish efficient collection and management systems of Korean astronomical records by utilizing an environment that is easily accessible. This study is intended to complete the development of a testbed system that allows researchers to systematically input and validate, in a Web environment, multiple astronomical records among the historical documents until Modern Joseon after the Three Kingdoms Period. Recognition of the pre-translated data and tables in advance is followed by its storage in the database built on the Web. Then, data validation is implemented by providing a retrieval service according to a specific form to only a finite number of researchers who have access authority. This study is targeted at a testbed system that takes around three months to be completely developed. The completed testbed system is expected to allow internal and external researchers of an organization to easily access the service on the Web. This will ensure that the accuracy of the data can be verified mutually and help identify areas of service improvement. The opinions collected regarding service improvement will be reflected in the future system. Eventually, domestic astronomical records will subsequently be able to be utilized internationally through the multilingual service.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• Journal of Veterinary Science
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• v.21 no.1
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• pp.16.1-16.10
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• 2020

Cancer is a major cause of death in dogs worldwide, and the incidence of cancer in dogs is increasing. The attenuated total reflection Fourier transform infrared spectroscopic (ATR-FTIR) technique is a powerful tool for the diagnosis of several diseases. This method enables samples to be examined directly without pre-preparation. In this study, we evaluated the diagnostic value of ATR-FTIR for the detection of cancer in dogs. Cancer-bearing dogs (n = 30) diagnosed by pathologists and clinically healthy dogs (n = 40) were enrolled in this study. Peripheral blood was collected for clinicopathological diagnosis. ATR-FTIR spectra were acquired, and principal component analysis was performed on the full wave number spectra (4,000-650 cm^-1). The leave-one-out cross validation technique and partial least squares regression analysis were used to predict normal and cancer spectra. Red blood cell counts, hemoglobin levels and white blood cell counts were significantly lower in cancer-bearing dogs than in clinically healthy dogs (p < 0.01, p < 0.01 and p = 0.03, respectively). ATR-FTIR spectra showed significant differences between the clinically healthy and cancer-bearing groups. This finding demonstrates that ATR-FTIR can be applied as a screening technique to distinguish between cancer-bearing dogs and healthy dogs.

• Current Photovoltaic Research
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• v.10 no.3
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• pp.77-83
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• 2022

Renewable energy is receiving attention again as a way to realize carbon neutrality to overcome the climate change crisis. Among renewable energy sources, the installation of Photovoltaic is continuously increasing, and as of 2020, the global cumulative installation amount is about 590 GW and the domestic cumulative installation amount is about 17 GW. Accordingly, O&M technology that can analyze the power generation and fault diagnose about PV plants the is required. In this paper, a study was conducted to diagnose fault using I-V curves of PV strings and deep learning. In order to collect the fault I-V curves for learning in the deep learning, faults were simulated. It is partial shade and voltage mismatch, and I-V curves were measured on a sunny day. A two-step data pre-processing technique was applied to minimize variations depending on PV string capacity, irradiance, and PV module temperature, and this was used for learning and validation of deep learning. From the results of the study, it was confirmed that the PV fault diagnosis using I-V curves and deep learning is possible.

• Tribology and Lubricants
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• v.37 no.6
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• pp.253-260
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• 2021

Metal pipes are used in a wide range of applications, from plumbing systems of large construction sites to small devices such as medical tools. When a liquid is enforced to flow through a metal pipe, a higher flow rate is beneficial for higher efficiency. Using high pressures can enhance the flow rate yet can be harmful for medical applications. Thus, we consider an optimal geometrical design to increase the flow rate in medical devices. In this study, we focus on cannulas, which are widely used small metal pipes for surgical procedures, such as liposuction. We characterize the internal flow rate driven by a negative pressure and explore its dependence on the key design parameters. We quantitatively analyze the suction characteristics for each design variable by conducting computational fluid dynamics simulations. In addition, we build a suction performance measurement system which enables the translational motion of cannulas with pre-programmed velocity for experimental validation. The inner diameter, section geometry, and hole configuration are the design factors to be evaluated. The effect of the inner diameter dominates over that of section geometry and hole configuration. In addition, the circular tube shape provides the maximum flow rate among the elliptical geometries. Once the flow rate exceeds a critical value, the rate becomes independent of the number and width of the suction holes. Finally, we introduce a slippery liquid-infused nanoporous surface (SLIPS) coating using nanoparticles and hydrophobic lubricants that effectively improves the flow rate and antifouling property of cannulas without altering the geometrical design parameter.