• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1059-1065
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• 2014

Wind turbine system can obtain the maximum wind energy using torque control under the rated wind speed, and wind turbine power is controlled as the rated power using pitch control over the rated wind speed. In this paper, we present a method for wind turbine pitch controller using neural networks. The purpose of the pitch control is to control generator speed and power in the above rated wind speed. To improve the neural network pitch controller, the difference between a rated and current speed of generator has been used for another input of neural networks as well as wind speed. Error back-propagation algorithm is used for training the neural network pitch controller and simulation and Matlab/Simulink is used for verifying that this system is controlled well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2587-2593
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• 2014

Ceramic Injection Molding (CIM) is one of wide used processes in industry field and the applications are gradually being expanded to parts of medical and electric devices. In this study, the CIM process were analyzed with FEM and process parameters were studied and analyzed the effect on product quality. The shape of simple flat plate was compared to the shapes with the hole, with the round corner portion or with the side wall portion. If there are holes then the hole around the uneven density distribution and the defects such as weld lines could be occurred. The Large radius of the corners of the product give good formability and fluidity. Not only the shape parameters of product but also the process parameters during CIM are studied. The simulation results showed that the process parameters of temperature, initial fractions and velocity are important design parameters to improve the quality of products.

• The Korean Journal of Emergency Medical Services
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• v.19 no.2
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• pp.7-17
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• 2015

Purpose: This study aimed to compare the effects of rescue ventilation maneuvers on the quality of two-rescuer cardiopulmonary resuscitation (CPR). Methods: We implemented mouth to mouth (MMV), mouth to pocket mask (MPV) and bag-valve mask ventilation (BMV) maneuvers. Each team of two-nurses was randomized to perform three consecutive sessions of two-rescuer CPR by using three artificial ventilation maneuvers. Results: The subjects were 26 teams of nurses (female: 96.2%, male: 3.8%, age: 26.6 years). Failed ventilation was more frequent in BMV ($2.23{\pm}2.21$, p <.001) than MMV ($0.31{\pm}0.74$) and MPV ($0.38{\pm}0.64$). BMV had more compressions per minute ($93.7{\pm}5.7$) than MMV ($87.0{\pm}7.2$, p = .001) and shorter total compression pause time ($46.1{\pm}5.8sec$) and compression pause fraction ($23.3{\pm}2.2%$) than MMV ($54.8{\pm}10.3sec$, p = .001, $25.5{\pm}3.5%$, p = .001, respectively) and MPV ($53.1{\pm}7.1sec$, p =. 006 and $25.8{\pm}2.6%$, p = .006, respectively). Conclusion: In our simulation study, BMV reduced the compression pause time and increased the number of compressions per minute, thus indicating CPR provided to patients was effective. However, considering the high rate of ventilation failure, we recommend periodic training.

• Korean Medical Education Review
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• v.19 no.1
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• pp.18-24
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• 2017

This study examined the perception and readiness of nursing educators regarding interprofessional education (IPE), and discussed the validity and application of IPE in nursing. From December 2016 to January 2017, 239 nursing professors and nurses completed a structured questionnaire consisting of general characteristics, the Interdisciplinary Education Perception Scale, the Readiness for Interprofessional Learning Survey (RIPLS), and an IPE action plan. The collected data were analyzed by descriptive statistics and t-test using the IBM SPSS ver. 23.0 program (IBM Corp., Armonk, NY, USA). The analysis revealed that 91.6% of the participants had not experienced IPE, and only 11.7% knew about IPE. However, approximately 80.0% answered that IPE is necessary. The results of this study showed that the score of the perceived need for cooperation was higher in nurses than it was in professors, while the score on competency and autonomy was higher in professors than it was in nurses. With reference to the scores on the RIPLS, those of professors were high on the sub-scales of teamwork and collaboration, professional identity, and roles and responsibility. The results revealed that participants considered the upper-grade undergraduate years as the ideal time for imparting IPE, and it was deemed suitable to include communication, simulation, and clinical practice in IPE. Doctors, pharmacists, and physiotherapists were thought to require cooperation for IPE the most. Despite the presence of several barriers to IPE, the participants thought that IPE can achieve learning outcomes such as interprofessional communication and cooperation, conflict resolution, and teamwork. It is necessary to cooperate with professionals in the complex clinical environment as professional areas are specialized and subdivided. Therefore, it is necessary to examine the application of IPE in undergraduate education and in on-the-job training.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Journal of Engineering Education Research
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• v.24 no.5
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• pp.38-45
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• 2021

The CanSat was designed as an educational satellite simulation program that implements the overall system of the satellite such as the command processing unit, the communication unit, and the power unit in a structure of the size of a can. In particular, the training effect is very excellent because the trainee can learn a process similar to the actual satellite development process by designing, manufacturing, testing, and launching. Republic of Korea Air Force Academy has been using the CanSat production kit used by the domestic can satellite contest experience department for education, but since it was produced based on PCB, it was impossible to show creativity and operation was restricted even with small mistakes. In this paper, we analyze the existing CanSat kit and propose a new educational CanSat kit that can be used in creative engineering design and practice subjects that will be reorganized into a regular course from 2021, and a lesson plan. In conclusion, by using the proposed CanSat kit for lectures, it is possible to achieve educational purposes and effects, improve lecture satisfaction, and provide stable instruction.

• Journal of Navigation and Port Research
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• v.43 no.4
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• pp.237-242
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• 2019

Behavioral errors of the seafarers are one of the major causes of collisions and are usually corrected through education and training. To correct this behavioral error, the structure in which the behavioral error occurs needs to be identified and analyzed. For this purpose, behavior observation data were obtained through ship maneuvering simulation for collision encounters. The 9-state behavior classification frame proposed by Reason was used for the behavior observation and 50 university students were involved in the experiment. Behavioral analysis used the behavioral model of collision avoidance success and failure, which was developed from the 9-state Left-to-Right Hidden Markov modeling technique. As a result of the experiment, the difference between behaviors of success and failure of collision avoidance was clearly identified, and the linkage between 9-state behaviors, required to prevent collision, was derived.

• Journal of Trauma and Injury
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• v.32 no.1
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• pp.8-16
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• 2019

Purpose: The Essential Surgical Procedures in Trauma (ESPIT) course was developed as a model to teach necessary surgical procedures to trauma physicians. Its goals are to improve knowledge, self-confidence, and technical competence. Methods: The ESPIT course consisted of five lectures and a porcine lab operative experience. The ESPIT course has been run seven times between February 2014 and April 2016. ESPIT participants completed a questionnaire to assess self-efficacy regarding essential surgical procedures in trauma before and immediately after taking the ESPIT course. Sixty-three participants who completed both pre- and post-course questionnaires on self-efficacy were enrolled in this study. Results: The overall post-ESPIT mean self-efficacy score was higher than the pre-ESPIT mean self-efficacy score ($8.3{\pm}1.30$ and $4.5{\pm}2.13$, respectively) (p<0.001). Self-efficacy was significantly improved after the ESPIT course in general surgeons (p<0.001), thoracic and cardiovascular surgeons (p<0.001), emergency medicine doctors, and others (neurosurgeons, orthopedic surgeons) (p<0.001). The differences in self-efficacy score according to career stage (<1 year, 1-3 years, 3-5 years, and >5 years) were also statistically significant (p<0.001). Conclusions: The data of the ESPIT participants indicated that they felt that the ESPIT course improved their self-efficacy with regard to essential surgical procedures in trauma. The ESPIT course may be an effective strategy for teaching surgical procedures, thus promoting better management of traumatic injuries.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.863-869
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• 2018

Due to recent changes in the maritime traffic environment, naval warship accidents are constantly occurring. Especially in 2017, serious loss of life was caused by a US navy destroyer accident. The purpose of this study is to analyze the characteristics of naval warship accident cases and construct an accident scenario by using naval training materials, adjudication of naval warship accidents and US navy destroyer accident reports. Based on the surveyed data, the status of accidents was identified and cases were analyzed. We reproduced 17 accident cases in accordance with accident reproduction procedure and constructed naval warship accident scenarios. As a result of analyzing the CPA, TCPA and PARK model for risk, reproducing 17 naval ship accident cases, collision risk increased on average 5-6 minutes before an accident. The result of this study represents basic data for naval and simulation education materials, contributing to the prevention of marine accidents.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.326-332
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• 2019

Recently, the deep learning technology has gained lots of attention which leads to its application to various fields. Especially, there are recent attempts to overcome the limit of wireless communications systems through the use of the deep learning. In this paper, we have verified the performance of deep learning based transmit power control scheme. Unlike previous transmit power control schemes where the optimal transmit power is derived by solving the optimization problem explicitly, in the deep learning based transmit power control, the general solver for the optimization problem is derived through the deep neural network (DNN). Especially, by using the spectral efficiency as the loss function of DNN, the training can be performed without needing labels. Through simulation based on Tensorflow, we confirm that the transmit power control based on deep learning can achieve the optimal performance while reducing the computational complexity by 1/200.