• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.725-729
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• 2016

The regular inspection of vehicles that are in service in domestic country, i.e., vehicle safety and emission gas inspection have been forced by the korea transportation safety authority and a private servicing enterprise according to the vehicle administration law (1995). In the safety inspection process, which is developed by a compatible inspection system and method, every effort is made to increase the reliability of vehicle safety and the service quality, and to protecting national health. In this study, the reliability of the braking force inspection method in the current regular vehicle inspection was studied statistically by comparing the dynamic braking force inspection method with the static braking force inspection method. Further, the problems and characteristics of the current braking force inspection methods were analyzed. Form the results for the dynamic braking force inspection equipment (H), a mean correction factor (y=18.61+2.18x) was obtained for the axle braking force ratio according to vehicle entering velocity.

• Journal of Auto-vehicle Safety Association
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• v.11 no.1
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• pp.36-39
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• 2019

The particulate matter that was emitted always come up by atmospheric environmental problem. Running on the road vehicles must have regular inspection at regular period and make sure the emissions of exhaust gases exceed the legal standards. Emission test for the atmospheric environment, but it is not free from the particulate matter. Currently, emission test of vehicle inspection is divided into regular inspection and close inspection. Regular inspection and close inspection differ not only the method of emission test, but also the facility standards that must have for this inspection. According to the "Regulations on the Implementation of Comprehensive vehicle Inspection, etc.", close inspection must have trapping device that is trap particulate matter by emission test to vehicle. However, regular inspection is different. Regular inspection do not specify any criteria for trapping facilities. Therefore, this study is confirm how to prevent the emission of particulate matter to the atmosphere during the year when mandatory trapping facilities are required to trapped particulate matter in the regular inspection.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.344-351
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• 2015

Braking force inspection of vehicles in service is certainly one of the most important characteristics that affect vehicle safety. Up to now, in domestic country, the regular safety inspection of vehicles in service has been tested with a roller type brake test (a static braking force inspection system). But, in EU and USA etc. in recent years, it has been tested with a plate type brake test (a dynamic braking force inspection system). In this study, to compare the characteristics of above two test systems, the correlations for the results of braking force are evaluated statistically. As the results, in the case of main braking force, the range of the $R^2$ of the deviation for the left and right side is 0.5386 ~ 0.6231 in the rear axle and 0.0032 ~ 0.0052 in the front axle respectively, then the $R^2$ in the front axle is lower than that in the rear axle and the total variation is unexplained by the least-squares regression line statistically. Also, the p-value for the deviation of the left and right in the front axle is 0.4839 ~ 0.5755, then it has nonsignificant in the front axle. Therefore, the static braking force inspection system can not reflect the inertia force that there is a load transfer from the rear axle to the front axle during braking. Accordingly, it is necessary to adopt the dynamic braking force inspection system which can reflect the inertia force on the regular vehicle safety inspection in domestic country.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.271-278
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• 2015

This paper develops an OBD Diagnostic Program (Program) using Visual Studio (C#), which was used to diagnosis malfunction information from OBD-II system vehicles. We accomplished this using the Program, Diagnostic tests, Board (STN1110), FTDI Basic Cable, Mini USB Cable, OBD Data Cable, and both hybrid and regular vehicles. The Program tests real-time data output, DTC output, sensor value output, engine RPM, waveform data, OBD type check, PID inspection, and whole monitoring. We found vehicles used in this research had 19 PIDs, which was within OBD-II regulations. We also gathered data on control and diagnostic code regulated by OBD-II system, such as, sensor output value, engine RPM, DTC output, each PID analytic value, OBD type, fuel mode, and whole monitoring result value. Using the data collected through the Program appropriately can lead to more effective diagnostic practices and contribute to education.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.74-81
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• 2019

Cars are inspected in the transport sector for their ability to achieve the greenhouse gas reduction targets. A vehicle (automobile) inspection broadly consists of regular and total checks, and both the safety level and the amount of exhaust gas are checked simultaneously during a vehicle inspection. This study deals with the efficiency of a soot probe to measure soot emissions from diesel vehicles. When the vehicle exhaust gas measurement is performed, there may be a difference between the exhaust gas temperature and the soot suction amount because of the different shape and angle of the exhaust port for each vehicle type. This may result in some incidents where the correct inspection nonconforming vehicle is not selected. Therefore, in this study, the shape of the probe was improved to increase the soot measurement efficiency under the condition of the exhaust pipe angle change.

• International Journal of Railway
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• v.1 no.1
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• pp.1-5
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• 2008

The railway system requires safety and reliability of service of all railway vehicles. Suitable technical systems and working methods adapted to it, which meet the requirements on safety and good order of traffic, should be maintained. For detection of defects, non-destructive testing methods-which should be quick, reliable and cost-effective - are most often used. Since failure in railway wheelset can cause a disaster, regular inspection of defects in wheels and axles are mandatory. Ultrasonic testing, acoustic emission and eddy current testing method and so on regularly check railway wheelset in service. However, it is difficult to detect a crack initiation clearly with ultrasonic testing due to noise echoes. It is necessary to develop a non-destructive technique that is superior to conventional NDT techniques in order to ensure the safety of railway wheelset. In the present paper, the new NDT technique is applied to the detection of surface defects for railway wheelset. To detect the defects for railway wheelset, the sensor for defect detection is optimized and the tests are carried out with respect to surface and internal defects each other. The results show that the surface crack depth of 1.5 mm in press fitted axle and internal crack in wheel could be detected by using the new method. The ICFPD method is useful to detect the defect that initiated in railway wheelset.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.28-33
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• 2016

Batteries are used for main power engine in the fields such as mobiles, electric vehicles and unmanned submarines, for starter and lamp driver in general automotive, for emergency electric source in ship. These days, lead-acid and the lithium ion batteries are increasingly used in the fields of the secondary battery, and the lead-acid battery has a low price and safety comparatively, The lithium ion battery has a high energy density, excellent output characteristics and long life, whereas it has the risk of explosion by reacting with moisture in the air. But Recently, due to the development of waterproof, fireproof, dustproof technology, lithium batteries are widely used, particularly, because their usages are getting wider enough to be used as a power source for hybrid ship and electric propulsion ship, it is necessary to manage more strictly. Hybrid ship has power supply units connected to the packets to produce more than 500kWh large power source, and therefore, A number of the communication modules and wires need to implement the wire inspection and monitor system(WIIMS) that allows monitoring server to transmit detecting voltage, current and temperature data, which is required for the management of the batteries. This paper implements a low price type wireless inspection and monitoring system(WILIMS) of the lithium ion battery for hybrid vessels using BLE wireless communication modules and power line modem( PLM), which have the advantages of low price, no electric lines compared to serial communication inspection systems(SCIS). There are state of charge(SOC), state of health(SOH) in inspection parts of batteries, and proposed system will be able to prevent safety accidents because it allows us to predict life time and make a preventive maintenance by checking them at regular intervals.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.549-558
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• 2021

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.