• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.179-184
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• 2019

Recently, various problems have arisen due to the popularization and aging of urban railway transit, which is the key transportation of large cities. In this study, the vibrational accelerations for the Daegu Metropolitan City Urban Railway(Line 1) were measured and evaluated using the smartphone built-in acceleration sensor and the approved application. For this purpose, the three axes running accelerations were measured according to the domestic standard (KS R 9160), and the acceleration data along the 32 stations (3 directions) were analyzed and compared. In addition, the increasing of acceleration values caused by the change of vibrational environment was monitored along the main stations between the time in 1997 and 2017. It was found that there are considerable increase of lateral and vertical directional accelerations due to the aging of railway facility environment for the last 20 years. The results of this study have valuable means for evaluating the ride quality of urban railway and the vibration influence on surrounding structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.290-296
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• 2019

Head-up displays for vehicles play an important role in displaying various information about the safety and convenience of driving on the windshield of the vehicle. In this study, ultra-precision machining was performed and evaluated as a method for machining a large-area aspheric free-form mirror that is applicable to augmented reality technology. Precision diamond cutting is highly accurate and suitable for the production of advanced parts with excellent surface integrity, low surface roughness, and low residual stress. By using an aspheric free-form mold, it is possible to improve the optical transfer function, reduce the distortion path, and realize a special image field curvature. To make such a mold, the diamond cutting method was used, and the result was evaluated using an aspherical shape-measuring machine. As a result, it was possible to the mold with shape accuracy (PV) below $1{\mu}m$ and surface roughness (Ra) below $0.02{\mu}m$.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of IKEEE
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• v.22 no.4
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• pp.970-977
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• 2018

The autonomous vehicle is based on an advanced driver assistance system (ADAS) consisting of a sensor that collects information about the surrounding environment and a control module that determines the measured data. As interest in autonomous navigation technology grows recently, an easy development framework for ADAS beginners and learners is needed. However, existing development and verification methods are based on high performance vehicle simulator, which has drawbacks such as complexity of verification method and high cost. Also, most of the schemes do not provide the sensing data required by the ADAS directly from the simulator, which limits verification reliability. In this paper, we present an interactive ADAS development and verification framework using a 3D vehicle simulator that overcomes the problems of existing methods. ADAS with image recognition based artificial intelligence was implemented as a virtual sensor in a 3D car simulator, and autonomous driving verification was performed in real scenarios.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.375-380
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• 2018

This paper proposes a classification method using the Convolutional Neural Network(CNN) which can obtain the type of trucks from the input image without the feature extraction step. To automatically classify vehicle images according to the type of truck cargo box, the top view images of the vehicle are used as input image and we design the structure of the CNN suitable for the input images. Learning images and correct output results is generated and the weights of neural network are obtained through the learning process. The actual image is input to the CNN and the output of the CNN is calculated. The classification performance is evaluated through comparison CNN output with actual vehicle types. Experimental results show that vehicle images could be classified with more than 90 percent accuracy according to the type of cargo box and this method can be used for pre-classification for inspecting loading defect.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.94-99
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• 2019

Friction stir welding was first reported by TWI(The Welding Institute) in 1991, and this welding method has been rapidly used in various industrial areas such railway, automobile, aerospace and shipbuilding industry. Here, we study core characteristics of friction stir welding (FSW) applied to Al 6005-T6 extruded sheets, which is the typical alloy used for railway car bodies. With the fixed welding speed of 500 mm/min, the rotating tool speed was varied from 600 to 1800 RPM. The results of hardness measurement revealed that the hardness of nugget area is ~70% with respect to the parent material, and for the selected range of rotation speed, no clear dependence was observed and the hardness values close to the parent materials were achieved for the area located 5 mm away from the welding interface. The tension test shows that yield strength and tensile strength were slightly decreased with increasing RPM, with no observed difference for the elongation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.63-68
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• 2019

Emerging applications such as Smart factory, in-car network, wide area power network require strict bounds on the end-to-end network delays. Flow-based scheduler in traditional Integrated Services (IntServ) architecture could be possible solution, yet its complexity prohibits practical implementation. Sub-optimal class-based scheduler cannot provide guaranteed delay since the burst increases rapidly as nodes are passed by. Therefore a leaky-bucket type regulator placed next to the scheduler is being considered widely. This paper proposes a simple server that achieves both fair scheduling and traffic regulation at the same time. The performance of the proposed server is investigated, and it is shown that a few msec delay bound can be achieved even in large scale networks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.521-526
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• 2020

In this paper, we propose a design method and process for hybrid V2X communication platform that combines WAVE communication and LTE-V2X communication which are C-ITS communication protocols for vehicle environments and Legacy LTE communication which is a commercial mobile communication for evaluating the autonomous platooning platform of commercial vehicles. For a safe and efficient autonomous platooning platform, an low-latency communication function based on C-ITS communication is required, and to control it, commercial communication functions such as Legacy LTE, which can be connected at all times, are required. In order to evaluate such a system, the evaluation equipment must have the same level of communication performance or higher. The main design contents presented in this paper will be applied to the implementation of hybrid V2X terminals for functional evaluation.