• Journal of Powder Materials
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• v.29 no.2
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• pp.110-117
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• 2022

The grain growth behavior in the (1-x)K_0.5Na_0.5NbO₃-xCaZrO₃ (KNNCZ-x) system is studied as a function of the amount of CZ and grain shape. The (1-x)K_0.5Na_0.5NbO₃-xCaZrO₃ (KNNCZ-x) powders are synthesized using a conventional solid-state reaction method. A single orthorhombic phase is observed at x = 0 - 0.03. However, rhombohedral and orthorhombic phases are observed at x = 0.05. The grain growth behavior changes from abnormal grain growth to the suppression of grain growth as the amount of CaZrO₃ (CZ) increases. With increasing CZ content, grains become more faceted, and the step-free energy increases. Therefore, the critical growth driving force increases. The grain size distribution broadens with increasing sintering time in KNNCZ-0.05. As a result, some large grains with a driving force larger than the critical driving force for growth exhibit abnormal grain growth behavior during sintering. Therefore, CZ changes the grain growth behavior and microstructure of KNN. Grain growth at the faceted interface of the KNNCZ system occurs via two-dimensional nucleation and growth.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.36-37
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• 2013

This paper presents high voltage operation sensing boost converter with high side current. Proposed topology has three functions which are high voltage driving, high side current sensing and low voltage boost controller. High voltage gate driving block provides LED dimming function and switch function such as a load switch of LED driver. To protect abnormal fault and burn out of LED bar, it is applied high side current sensing method with high voltage driver. This proposed configuration of boost converter shows the effectiveness capability to LED driver through measurement results.

• Journal of Advanced Navigation Technology
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• v.17 no.2
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• pp.165-170
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• 2013

In this paper, we propose the information acquisition and analysis system for a vehicle driver in order to provide the safe driving environments. We first define the list of reckless driving behaviors and propose the recognition system, which recognizes the reckless behaviors, by using the acquired information. The collaboration among the information acquisition, the analysis, and the behavior comparison modules increases the accuracy of the recognition rate. Our system alarms to a vehicle driver in order to notify the potential to confront the dangerous situation due to the abnormal or reckless driving behaviors.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.1
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• pp.28-34
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• 2017

The vehicle stabilizer link is one of the suspension components that reduces the bumping and rolling during vehicle driving. However, this stabilizer link could be a source of the abnormal noises when its free gaps have higher than normal values. Therefore, the current study aims at investigating the quantitative relationships between the abnormal noises and free gaps of the vehicle stabilizer links, as well as the length of time that the vehicle stabilizer links could be used without generating abnormal noises. In this study, the abnormal noises were measured based on the magnitude of the stabilizer link vibration, while the free gaps were quantified through the force-displacement curves of the stabilizer links. Harsh durability tests were also conducted in order to quantify the operating cycles of the stabilizer links before generating the abnormal noises, along with the concomitant measurements of the free gaps. The current results showed that the abnormal noises of the stabilizer links were detected when its free gaps were larger than 0.12 mm. However, the free gaps of the stabilizer links, which are bigger than 0.1 mm, produced the abnormal noises at 1.5 million cycles under harsh durability test conditions. A parametric study in the future that would reflect the different shapes and sizes of the stabilizer links for diverse vehicles could determine more generalized relationships between the abnormal noises and free gaps of the vehicle stabilizer links.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.673-678
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• 2013

Driving gear units can be affected by various problems, including those associated with external or internal defects in the bearing, problems with the lubricant oil, high-loading of the railway, and frequent impacts generated by rail joints. Temperature monitoring is a basic method in diagnosing abnormal conditions in the reduction gear and other components. This paper describes a new wireless monitoring system for the temperature diagnosis of abnormal conditions of the reduction gear. Integrated circuit (IC)-type temperature sensors were installed in the reduction gear box of a high-speed railway car. The temperature data from the reduction gear were acquired and analyzed in situ during high-speed rail operation. Analysis of these data was used to alert the driver and/or maintenance personnel when problems occurred.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.66-72
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• 2016

As a method to improve the equipment reliability, early warning researches that can be detected fault symptom of an equipment at an early stage are being performed out among developed countries. In this paper, when abnormal signal is input to actual normal signal of a pump, early detection studies on pump's fault symptom were carried out with auto-associative kernel regression as an advanced pattern recognition algorithm. From analysis, correlations among power of motor driving pump, discharge flow of pump, power output of pump, and discharge pressure of pump are exited. When the abnormal signal is input to one of those normal signals, the other expected values are changed due to the influence of the abnormal signal. Therefore, the fault symptom of pump through the early-warning index is able to detect at an early stage.

• Journal of the Korean Society for Railway
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• v.18 no.5
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• pp.410-418
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• 2015

A slider of the pantograph undergoes uneven and abnormal wear due to sliding contact with the catenary. In the case of rain, the loss of lubricant increases the frictional resistance for the reciprocating motion between the catenary and the slider, accelerating local wear. The slider in the winter should have good wear resistance, which can be achieved through alloy design. Uneven and abnormal wear were not observed in the results of a driving test using a wear resistant slider. It was found that the increased density of the slider enhanced the corrosive effects of Fe-Ti, preventing the occurrence of abnormal wear by maintaining the wear and arc resistance in the rainy season. Also, mechanical and electrical wear did not affect the composition of the slider, and this improved the wear resistance. Inaddition, the slider was applied to an entire train and was tested during driving; ananalysis of the correlation of the catenary was performed, including during the rainy season and the winter season.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.302-313
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• 2023

This paper aims to develop a framework that can fully automate the quality management of training data used in large-scale Artificial Intelligence (AI) models built by the Ministry of Science and ICT (MSIT) in the 'AI Hub Data Dam' project, which has invested more than 1 trillion won since 2017. Autonomous driving technology using AI has achieved excellent performance through many studies, but it requires a large amount of high-quality data to train the model. Moreover, it is still difficult for humans to directly inspect the processed data and prove it is valid, and a model trained with erroneous data can cause fatal problems in real life. This paper presents a dataset reconstruction framework that removes abnormal data from the constructed dataset and introduces strategies to improve the performance of AI models by reconstructing them into a reliable dataset to increase the efficiency of model training. The framework's validity was verified through an experiment on the autonomous driving dataset published through the AI Hub of the National Information Society Agency (NIA). As a result, it was confirmed that it could be rebuilt as a reliable dataset from which abnormal data has been removed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.216-223
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• 2023

As the technology and level of autonomous vehicles advance and they drive in more diverse road environments, an intuitive and efficient interaction system is needed to resolve and respond to the situations the vehicle faces. The development of driving technology from the perspective of autonomous driving has the ultimate goal of responding to situations involving humans or more. In particular, in complex road environments where mutual concessions must be made, the role of a system that can respond flexibly through efficient communication methods to understand each other's situation between vehicles or between pedestrians and vehicles is important. In order to resolve the status of the vehicle or the situation being faced, the provision and method of information must be intuitive and the efficient operation of an autonomous vehicle through interaction with intention is required. In this paper, we explain the vehicle structure and functions that can display information about the situation in which the autonomous vehicle driving in a living lab can drive stably and efficiently in a diverse and complex environment.