• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.586-591
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• 2012

The solution of maintenance decision support system for the gas turbine engine, which is currently operating in GUNSAN combined cycle power plant, was developed and is consist of online monitoring module, periodic performance trending module, optimal compressor washing interval analysis module and hot component management module. Also, GUI platform was applied to this solution for the user to monitoring the analyzed result of engine performance condition and then to make a decision of the consequent maintenance action. In online condition monitoring module, the performance degradation of engine is provided by the analysis of difference between the real time measurement data compared to exist engine performance. The optimal compressor washing interval module produced the washing interval of maximum net profit value by researching the maintenance expense and the loss profit value corresponds to the performance degradation with economic assessment algorithm. Thus, this solution support the user to enable the optimal maintenance and operation of gas turbine engine with overall analysis of engine condition and main information.

• Journal of Veterinary Science
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• v.22 no.5
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• pp.71.1-71.12
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• 2021

Background: African swine fever (ASF) is a hemorrhagic fever occurring in wild boars (Sus scrofa) and domestic pigs. The epidemic situation of ASF in South Korean wild boars has increased the risk of ASF in domestic pig farms. Although basic reproduction number (R₀) can be applied for control policies, it is challenging to estimate the R₀ for ASF in wild boars due to surveillance bias, lack of wild boar population data, and the effect of ASF-positive wild boar carcass on disease dynamics. Objectives: This study was undertaken to estimate the R₀ of ASF in wild boars in South Korea, and subsequently analyze the spatiotemporal heterogeneity. Methods: We detected the local transmission clusters using the spatiotemporal clustering algorithm, which was modified to incorporate the effect of ASF-positive wild boar carcass. With the assumption of exponential growth, R₀ was estimated for each cluster. The temporal change of the estimates and its association with the habitat suitability of wild boar were analyzed. Results: Totally, 22 local transmission clusters were detected, showing seasonal patterns occurring in winter and spring. Mean value of R₀ of each cluster was 1.54. The estimates showed a temporal increasing trend and positive association with habitat suitability of wild boar. Conclusions: The disease dynamics among wild boars seems to have worsened over time. Thus, in areas with a high elevation and suitable for wild boars, practical methods need to be contrived to ratify the control policies for wild boars.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.632-637
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• 2019

Recent researches on autonomous driving of vehicles are becoming very active, and it is a trend to assist safe driving and improve driver's convenience. Autonomous vehicles are required to combine artificial intelligence, image recognition capability, and Internet communication between objects. Because mobile telecommunication networks have limitations in their processing, they can be easily implemented and scale using an easily expandable Wi-Fi network. We propose a wireless design method to construct such a vehicle control network. We propose the arrangement of AP and the software configuration method to minimize loss of data transmission / reception of mobile terminal. Through the design of the proposed network system, the communication performance of the moving vehicle can be dramatically increased. We also verify the packet structure of GPS, video, voice, and data communication that can be used for the vehicle through experiments on the movement of various terminal devices. This wireless design technology can be extended to various general purpose wireless networks such as 2.4GHz, 5GHz and 10GHz Wi-Fi. It is also possible to link wireless intelligent road network with autonomous driving.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.45-50
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• 2021

With the recent development of IT technology, research and interest in various bio-signal measuring devices are increasing. But studies related to ECG(electrocardiogram), which is one of the most representative bio-signals, particularly arrhythmic signal detection, are incomplete. Since arrhythmia has various causes and has a poor prognosis after onset, preventive treatment through early diagnosis is best. However, the 24-hour Holter electrocardiogram, a tool for diagnosing arrhythmia, has disadvantages in the limitation of use time, difficulty in analyzing motion artifact due to daily life, and the user's real-time alarm function in danger. In this study, an ECG and pulse monitoring device capable of continuous measurement for a long time, a real-time monitoring app, and software for analysis were developed, and the trend of the measured values was confirmed. In future studies, research on derivation of quantitative results of ECG signal measurement analysis is required, and further research on the development of an arrhythmic signal detection algorithm based on this is required.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.13-20
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• 2021

A series of numerical experiments with measurements observed at continuously operating reference stations (CORS) of the international GNSS services (IGS) and the national geographical information institute of Korea (NGII) have been intensively carried out to evaluate the quality of pseudo-ranges and carrier-phases of GPS L2C signal obtained by various receiver types, benign and harsh operational environment. In this analysis, some quality measures, such as signal-to-noise ratio (SNR), the magnitude of multipath, and the number of cycle slips, the pseudo-range and carrier phase obtaining rate were computed and compared. The SNR analysis revealed an impressive result that the trend in the SNR of C/A and the L2C comparably depends upon type of receivers. The result of multipath analysis also showed clearly different tendency depending on the receiver types. The reason for this inconsistent tendency was seemed to be that the different multipath mitigation algorithm built-in each receiver. The number of L2C cycle slip was less than P2(Y), and L2C measurements obtaining rate was higher than that of P2(Y) in three receiver types. In the harsh observational environment, L2C quality was not only superior to P2(Y) in all aspects such as SNR, multipath magnitude, the number of cycle slips, and measurement obtaining rate, but also it could maintain a level of quality equivalent to C/A. According to the results of this analysis, it's expected that improved positioning performance like accuracy and continuity can be got through the use of L2C instead of existing P2(Y).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.225-230
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• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• Journal of ILASS-Korea
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• v.27 no.2
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• pp.57-65
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• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.31-42
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• 2021

The purpose of this study was to explore research topics and examine the trend in COVID19 related research papers. We identified eight topics using latent Dirichlet allocation and found acceptable validity in comparison with the structural topic model. The subtopics have been extracted using k-means clustering and plotted in PCA space. Additionally, we discovered the topics bearing negative tones and warning signs by sentiment analysis. The results flagged up the issues of the topics, Biomedical Related, International Dynamics and Psychological Impact. The findings could serve as a guideline for researchers who explore new research directions and policymakers who need to make decisions about which research projects to support.

• The Research Journal of the Costume Culture
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• v.30 no.4
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• pp.608-630
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• 2022

The purpose of this study is to examine the relationships between citations and the research trends of The Research Journal of the Costume Culture (RJCC) using bibliometric and network analyses. The results are as follows. First, the RJCC has been cited by a greater number of journals and high-reputation journals today. The RJCC has been mentioned in global academic journals in various fields, and it has been noted the most in environmental science. Second, because of examining the articles published in the RJCC over the past three years (2019 - 2021), it was found that the number of topics was evenly distributed in various subfields of the clothing and textiles sector. The RJCC principally deals with traditional clothing, ethics and sustainability, and technology, which means that the RJCC reflects the past, present, and future. As a result of conducting a cluster analysis using the Wakita-Tsurumi algorithm, the subjects of ethical fashion and sustainability were derived from the subdivisions of the RJCC. This suggests that the RJCC is a journal specialized in ethical fashion and sustainability sectors such as environmental, animal, and labor ethics. This study outlined the current status and future direction of academic journals in the field of clothing through an analysis of the RJCC's influence change and the relationship between citations. In addition, it is academically significant because it identifies research trends and knowledge-structure changes in the apparel science field by identifying changes in research keywords and significant research topics by sector.