• Journal of IKEEE
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• 제25권3호
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• pp.485-492
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• 2021

In this paper, we propose the development of a defect inspection system for polygonal containers. Embedded board consists of main part, communication part, input/output part, etc. The main unit is a main arithmetic unit, and the operating system that drives the embedded board is ported to control input/output for external communication, sensors and control. The input/output unit converts the electrical signals of the sensors installed in the field into digital and transmits them to the main module and plays the role of controlling the external stepper motor. The communication unit performs a role of setting an image capturing camera trigger and driving setting of the control device. The input/output unit converts the electrical signals of the control switches and sensors into digital and transmits them to the main module. In the input circuit for receiving the pulse input related to the operation mode, etc., a photocoupler is designed for each input port in order to minimize the interference of external noise. In order to objectively evaluate the accuracy of the development of the proposed polygonal container defect inspection system, comparison with other machine vision inspection systems is required, but it is impossible because there is currently no machine vision inspection system for polygonal containers. Therefore, by measuring the operation timing with an oscilloscope, it was confirmed that waveforms such as Test Time, One Angle Pulse Value, One Pulse Time, Camera Trigger Pulse, and BLU brightness control were accurately output.

• Journal of the Computational Structural Engineering Institute of Korea
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• 제34권1호
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of the Korea Society for Simulation
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• 제32권3호
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• pp.33-41
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• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.

• Journal of the Korean Society of Radiology
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• 제18권2호
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• pp.127-133
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• 2024

The elbow joint is made up of three different bones. X-rays or other radiological exams are commonly used to diagnose elbow injuries or disorders caused by physical activity and external forces. Previous research on the elbow joint reported a new examination method that meets the imaging evaluation criteria in the tilt position by Z-axis elevation of the forearm. Therefore, this study aims to design an optimized instrument and develop an aid applicable to other upper extremity exams. After completing the 2D drawing and 3D modeling design, the final design divided into four parts was fabricated with a 3D printer using ABS plastic and assembled. The developed examination aid consists of a four-stage Z-axis elevation tilt angle function (0°, 5°, 10°, and 15°) and can rotate and fixate 360° in 1-degree increments. It was designed to withstand a maximum equivalent stress of 56.107 Pa and a displacement of 1.6548e-5 mm through structural analysis to address loading issues caused by cumulative frequency of use and physical utilization. In addition to X-ray exams of the elbow joint, the developed aid can be used for shoulder function tests by rotating the humerus and also be applied to MRI and CT exams as it is made of non-metallic materials. It will contribute to the accuracy and efficiency of medical imaging diagnosis through clinical applications of various devices and medical imaging exams in the future.