• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1167-1174
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• 2022

Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. The main drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operating environment. Even though ultrasonic non-destructive testing has been conducted in major nuclear components and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonic signals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonic technique (UT) and machine learning (ML) models. Several different ML models, specifically the K-nearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models, were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identified that the ML models can predict the category of ultrasonic signals effectively according to the aging condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.37-47
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• 1995

In recent years, it has been variously developed for testing the accuracy of circular motion of NC machine tools, for example Telescoping Ball Bar Method by Bryan, Circular test Method by Knapp and $r^{-{\theta} }$ Method by Tsutsumi etc., but these methods are all 2-dimentional measuring methods on plane. These simple methods of circular motion accuracy test of NC machine tools have been studied by many reserchers as above, but it is not yet settled in the code of measuring methods of motion errors of NC machine tools, because of errors of measuring units and sensors, and also especially the difficulties of centering of measuring units and the spindle of machining center. In this paper, in use of 2 rotary encoders and 1 magnetic type linear scale with resolution of 0.5 .mu. m, it has become possible for measuring of 3 dimentional space motion accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1520-1542
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• 2020

Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.563-566
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• 2003

The conventional testing as IEC-60587 is widely used in suface aging measurement of outside insulator those testing can carry out very short time in Lab testing. Also IEC-60587 testing is able to offer the standard judgement of relative degradation level of out side HV machine. Therefore it is very useful method compare to previous conventional tracking testing method and effective Lab testing method, But surface discharges(SD) have very complex characteristics of discharge pattern so it is required estimation research to development of precise analysis method. In recent, the study of IRR Camera is carrying out discover of temperature of power equipment through condition diagnosis and system development of degradation diagnosis.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.477-485
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• 2005

This paper is concerned with the development of a micro tensile testing machine for optically functional materials such as single or poly crystalline silicon and nickel film. This micro tensile tester has been developed for testing various types of materials and dimensions. PZT type actuation is utilized for precise displacement control. The specifications of the PZT actuated micro tensile testers developed are as follows: the volumetric size of the tester is desktop type of 710mm' 200mm' 270mm; the maximum load capacity and the load resolution in this system are IKgf and 0.0152mgf respectively and; the full stroke and the stoke resolution of the PZT actuator are $1000{\mu}m$ and 10nm respectively. Special automatic specimen installing and setting equipment is applied in order to prevent unexpected deformation and misalignment of specimens during handling of specimens for testing. Nonlinearity of the PZT actuator is compensated to linear control input by an inverse compensation method that is proposed in this paper. The strain data is obtained by ISDG method that uses the laser interference phenomenon. To test the reliance of this micro tensile testing machine, a $200{\mu}m$ thickness nickel thin film and SCS (Single Crystalline Silicon) material that is made with the MEMS fabrication process are used.

• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.4
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• pp.428-432
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• 2014

Rotating machines are an integral part of large electrical power machinery in most of the industries. Any degradation or outages in the rotating electric machinery can result in significant losses in productivity. It is critical to monitor the equipment for any degradation's so that it can serve as an early warning for adequate maintenance activities and repair. Prior research and field studies have indicated that the rotating machines have a particular type of signal structure during the initial start-up transient. A machine performance can be studied based on the effect of degradation in signal parameters. In this paper a data-acquisition system and the FFT algorithm has been design and model using the MATLAB and Simulink. The implementation had been carried out on the TMS320 DSP Processor and various testing and verification of the machine performance had been carried out. The results show good agreement with expected results for both simulated and real-time data. The real-time data from AC water pumps which have rotating motors built-in were collected and analysed. The FFT algorithm provides frequency response and based on this frequency response performance of the machine had been measured.The FFT algorithm provides only approximation about the machine performances.

• Journal of the Optical Society of Korea
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• v.15 no.4
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• pp.394-397
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• 2011

For the purpose of fabricating off-axis aspheric optics, we propose an 8-axis-polishing machine combined with a testing tower whose height is up to 9 m. The proposed polishing machine was designed and analyzed by using a well-known finite element method. The eight axes of the machine have a synchronized motion generated by a computer, and each axis was calibrated by a heterodyne laser interferometer or an optical encoder. After calibration, the maximum positioning error of the machine was less than 2 ${\mu}m$ within a whole 2 m ${\times}$ 2 m area. A typical fabrication result of a ${\phi}1.5$ m concave mirror was also described in this manuscript.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.835-841
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• 1997

Microscopic examination of the morphology of wear debris is an accepted method for machine condition and fault diagnosis. However wear particle analysis has not been widely accepted in industry because it is dependent on expert interpretation of particle morphology and subjective assessment criteria. This paper was undertaken to analyze the morphology of wear debris for machine condition diagnosis of the lubricated moving surfaces by image processing and analysis. The lubricating wear test was performed under different sliding conditions using a wear test device made in our laboratory and wear testing specimen of the pin-on-disk-type was rubbed in paraffine series base oil. In order to describe characteristics of debris of various shape and size, four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) have been developed and outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus to overcome many of the difficulties in current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.