• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05b
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• pp.12-15
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• 2002

In this paper, we fabricated a electro-chemical discharge machine for drilling microscopic glass hole. In this research, we used a glass plate and NaOH solution. From the experimental result, we knew that the change of voltage wave was caused by drilling microscopic hole of glass using electro-chemical discharge method. So, we can give a function of the power auto stop to electro-chemical discharge machine with the change of voltage wave.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.401-402
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• 2014

State of Charge (SOC) and State of Health (SOH) are the key issues for the application of Absorbent Glass Mat (AGM) type battery in Idle Start Stop (ISS) system which is popularly integrated in Electric Vehicles (EVs). However, battery parameters strongly depend on SOC, current rate and temperature and significantly change over the battery life cycles. In this research, a novel method for SOC, SOH estimation which combines the Auto Regressive with external input (ARX) method using for online parameters prediction and Dual Extended Kalman Filter (DEKF) algorithm considering hysteresis is proposed. The validity of the proposed algorithm is verified by the simulation and experiments.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.419-427
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• 2020

This study was undertaken to address the difficulties and inconveniences of an electric wheelchair. We focused on improving usability of initially completed products by augmenting the prototypes designed in the previous study. For evaluation of usability, 10 elderly women aged over 65 years, capable of movements and physical activities in daily life, were enrolled as subjects. The experimental method included a subjective satisfaction questionnaire evaluation of the elderly women using the target product, and the observation evaluation was achieved using video recording data, etc. Usability evaluation revealed that the elevating sector requires improvement of intuition through separation of the elevating control panel and the driving control panel. Improvements in the driving sector include corrections of the front wheel mechanism or driving control algorithm, UI, and sudden stop system. Transferring section assessment revealed a necessity to secure structures and add structures that support power. We believe that based on the inconveniences and improvements presented in the usability evaluation, appending the existing prototype with complementary products will improve the quality of life of elderly women with limited mobility.