• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.539-548
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• 2007

In the paper, a wireless charger with the function of auto-shutdown for fully implantale middle ear hearing devices (F-IMEHD) has been designed. The wireless charger can communicate with an implant module to be turned off automatically shutdown after an internal rechargeable battery has been fully-charged by electromagnetic coupling using two coils. For the communication with an implant module, the wireless charger uses the load shift keying (LSK) method. But, the variation of the mutual inductance due to the different distance between two coils can cause the communication error in receiving the fully-charged signal from an implant module. To solve the problem, the implemented wireless charger has a variable reference generator for LSK communication. The wireless charger generates proper level of the reference voltage for a comparator using an ADC (analog-to-digital converter) and a DAC (digital-to-analog converter). Through the result of experiment, it has been confirmed that the presented wireless charger can detect signals from implantable module. And wireless charger can stop generating electromagnetic flux after an implanted battery has been fully charged in spite of variable coil distance according to different skin thickness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.640-646
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• 2017

A GEN TEST of the auxiliary power unit of a T-50 series aircraft is performed as part of the operational test of its emergency power system on the ground before flight. At this time, the auxiliary power unit should be automatically turned off via the response signal of the ECU when power is not normally supplied to the emergency power system. If the correct operation of the emergency power system cannot be confirmed on the ground, it is not possible to proceed with the flight. This kind of defect is a major factor causing the operation rate of the aircraft to be decreased. The defect code identified by the ECU was confirmed as a defect in the inverter. However, the same defect was found after replacing the inverter. This report presents an improved method of identifying the cause of the defect by analyzing the response characteristics of the ECU and emergency power system and allows the ECU to be recognized as the cause of the defect if the inverter does not generate a voltage within a certain time. Also, the application of the improved method confirmed that it can satisfy the output request time of the emergency power system and effectively prevent the auto-shutdown of the auxiliary power unit.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.