Journal of the Korean Society for Precision Engineering

A Highway Secondary Accident Prevention System based on FFT Analysis of Vehicle Collision Sounds: Minki Jung, Young Shin Cho, Yongsik Ham, Joong Bae Kim; J. Korean Soc. Precis. Eng. 2025;42(9):749-756.
Published online September 1, 2025; DOI: https://doi.org/10.7736/JKSPE.025.037

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This study introduces a highway secondary accident prevention system that employs Fast Fourier Transform (FFT) analysis of vehicle collision sounds. The system is designed to identify abnormal acoustic patterns produced during collisions and skidding events, enabling faster and more accurate accident detection than traditional methods. When a crash is detected, visual warning signals are instantly sent to nearby vehicles using LED devices powered by a photovoltaic panel and an energy storage system (ESS). Experimental results showed 100% detection accuracy during independent playback of collision, skidding, and driving sounds, and 80% accuracy during simultaneous playback. These results confirm the system's ability to effectively differentiate accident-related sounds and deliver timely alerts. This research offers an innovative and environmentally sustainable approach to enhancing highway safety and reducing the societal and economic consequences of secondary accidents.

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Wear Estimation of an Intelligent Tire Using Machine Learning: Jun Young Han, Ji Hoon Kwon, Hyeong Jun Kim, Suk Lee; J. Korean Soc. Precis. Eng. 2023;40(2):113-121.
Published online February 1, 2023; DOI: https://doi.org/10.7736/JKSPE.022.107

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Tire-related crashes account for a large proportion of all types of car accidents. The causes of tire-related accidents are inappropriate tire temperature, pressure, and wear. Although temperature and pressure can be monitored easily with TPMS, there exists no system to monitor tire wear regularly. This paper proposes a system that can estimate tire wear using a 3-axis accelerometer attached to the tread inside the tire. This system utilizes axial acceleration, extracts feature from data acquired with the accelerometer and estimates tire wear by feature classification using machine learning. In particular, the proposed tire wear estimation method is designed to estimate tread depth in four types (7, 5.6, 4.2, and 1.4 mm) at speeds of 40, 50, and 60 kmph. Based on the data obtained during several runs on a test track, it has been found that this system can estimate the tread depth with reasonable accuracy.

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A Study on Wheel Member Condition Recognition Using 1D–CNN
Jin-Han Lee, Jun-Hee Lee, Chang-Jae Lee, Seung-Lok Lee, Jin-Pyung Kim, Jae-Hoon Jeong
Sensors.2023; 23(23): 9501. CrossRef

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Flexible Structured Illumination Microscope based on DMD and Focus-tunable Lens: Hyo Bin Jeong, Ki-Nam Joo; J. Korean Soc. Precis. Eng. 2021;38(6):397-403.
Published online June 1, 2021; DOI: https://doi.org/10.7736/JKSPE.021.010

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In this investigation, we propose a flexible structured illumination microscope (FSIM) to eliminate mechanical moving parts for the phase shifts in the spatial pattern and longitudinal scanning of the specimen. In order to prevent these mechanical motions, we adopt a focus-tunable lens and digital micromirror device (DMD) to replace the lateral motion of the pattern and the scanning of the specimen, respectively, which leads to the enhancement of rapid and precise measurement results for measuring the 3D surface profile of specimens. To realize the proposed system, two types of flexible structured illumination microscopes, Macro and micro types, were constructed and their performances were verified with a plane mirror and step height specimens.

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Tool Condition Monitoring Using Deep Learning in Machining Process: Byeonghui Park, Yoonjae Lee, Changwoo Lee; J. Korean Soc. Precis. Eng. 2020;37(6):415-420.
Published online June 1, 2020; DOI: https://doi.org/10.7736/JKSPE.020.040

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Tool condition monitoring is one of the key issues in mechanical machining for efficient manufacturing of the parts in several industries. In this study, a tool condition monitoring system for milling was developed using a tri-axial accelerometer, a data acquisition, and signal processing module, and an alexnet as deep learning. Milling experiments were conducted on an aluminum 6061 workpiece. A three-axis accelerometer was installed on a spindle to collect vibration signals in three directions during milling. The image using time-domain, CWT, STFT represented the change in tool wear of X, Y axis directions. Alexnet was modified to learn images of the two directional vibration signals, to predict the tool condition. From an analysis of the results of learning based on the experimental data, the performance of the monitoring system could be significantly improved by the suitable selection of the data image method.

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Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
Jaeseok Shim, Jeongseo Koo, Yongwoon Park, Jaehoon Kim
Applied Sciences.2022; 12(24): 12901. CrossRef
Comparative Analysis and Monitoring of Tool Wear in Carbon Fiber Reinforced Plastics Drilling
Kyeong Bin Kim, Jang Hoon Seo, Tae-Gon Kim, Byung-Guk Jun, Young Hun Jeong
Journal of the Korean Society for Precision Engineering.2020; 37(11): 813. CrossRef