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

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.4
• /
• pp.95-105
• /
• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.64-69
• /
• 2022

Commercialization of public technology is the transfer of government-led scientific and technological innovation and R&D results to the private sector, and is recognized as a key achievement driving economic growth. Therefore, in order to activate technology transfer, various machine learning methods are being studied to identify success factors or to match public technology with high commercialization potential and demanding companies. However, public technology commercialization data is in the form of a table and has a problem that machine learning performance is not high because it is in an imbalanced state with a large difference in success-failure ratio. In this paper, we present a method of utilizing CTGAN to resolve imbalances in public technology data in tabular form. In addition, to verify the effectiveness of the proposed method, a comparative experiment with SMOTE, a statistical approach, was performed using actual public technology commercialization data. In many experimental cases, it was confirmed that CTGAN reliably predicts public technology commercialization success cases.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.6
• /
• pp.842-849
• /
• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.19-26
• /
• 2022

With the recent development of intelligent transportation systems, car license plate recognition systems are being used in various fields. Such systems need to guarantee real-time performance to recognize the license plate of a driving car. Also, they should keep a high recognition rate even in problematic situations such as small license plates in low-resolution and unclear image due to distortion. In this paper, we propose a real-time car license plate recognition system that improved processing speed using object detection algorithm based on anchor-free method and text recognition algorithm based on Convolutional Neural Network(CNN). In addition, we used Spatial Transformer Network to increase the recognition rate on the low resolution or distorted images. We confirm that the proposed system is faster than previously existing car license plate recognition systems and maintains a high recognition rate in a variety of environment and quality images because the proposed system's recognition rate is 93.769% and the processing speed per image is about 0.006 seconds.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.4
• /
• pp.171-178
• /
• 2021

Caravans are easily affected by external physical factors and often cause dangerous situations for passengers. Therefore, in order to secure the stability of the passenger, there is a need to develop a sway reduction device capable of preventing the sway phenomenon in advance. This paper aims to minimize the hitch angle between the tow vehicle and the caravan. Specifically, the initial instability of the caravan is detected through an IMU sensor mounted on each of the tow vehicle and the caravan, and a control value is calculated to reduce errors from the Hitch angle and Hitch yaw rate using a PID controller. Different braking torques are generated, distributed, and controlled on the left and right brakes of the caravan according to the calculated control value. It could be verified through the driving experiment that the hitch angle was decreased compared to the case where the performance of the sway reduction device was not controlled, and the transverse stability improvement rate was improved by 94.49% compared to before control.

• Journal of Aerospace System Engineering
• /
• v.16 no.3
• /
• pp.93-98
• /
• 2022

The purpose of this paper was to propose a method for improving the performance of the open-loop control of single-phase permanent magnet synchronous motor (SP-PMSM), based on a square wave voltage injection. Generally, the SP-PMSM driving systems cmprise a full-bridge inverter and asymmetric air-gap structure of magnetic circuit, because a zero torque occurs on the symmetrical air-gap. As a result, it cannot be started at a specific rotor position. Thus, it is possible to cause the start-up failure at an open-loop control for sensorless operation of SP-PMSM. In this paper, the method with square wave voltage injection considering the nonlinearity of the inverter is presented to resolve the problem. The effectiveness of the proposed algorithm is verified through several experiments.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.488-493
• /
• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.

• Journal of Korea Port Economic Association
• /
• v.37 no.3
• /
• pp.75-91
• /
• 2021

Due to increased competition in supply side to reduce operational costs, port professionals have experienced extreme pressure, which demanded academicians to develop the model for efficient port operations from the industry perspective. Among many ports in the world, U.S. ports are our primary interest to analyze in our study for its high volume of cargoes transacted in the U.S. ports. We primarily employed DEA (Data Envelopment Analysis) technique to research the productivity of U.S. ports and applied the algorithm of double bootstrapped DEA proposed by Simar & Wilson (2007) to further investigate the driving forces of the performance of U.S. port operations. The external variables employed in our study comprise onDock Rail, Channel Depth, Location, Area, Acres, ForeignCargoRatio, and TEUChange, out of which onDock Rail, Acres, ForeignCargoRatio, and TEUChange were significant. In order to evaluate the effects of methodology selection, we conducted the same analysis applying the Censored model (Tobit) and contrasted the outcomes drawn from the two different techniques. Based on the findings from this work we proposed managerial implications and concluded.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.6
• /
• pp.423-438
• /
• 2021

The blade of motor grader is used for scattering and leveling the aggregates on the foundation of road construction site. The paper performed a design improvement research of the blade part to enhance the working efficiency of motor graders. The scattering works of aggregates by blade driving were simulated by DEM (discrete element method) of a dynamic code. The four design parameters were selected and a specific leveling scenario for the simulation was determined. The nine blade models were numerically experimented, and the sensitivity of each factors was analyzed. Next, the design factors that influence a blade performance have been selected by ANOVA, and these key design factors were applied to the progressive quadratic response surface method (PQRSM). The optimum set of design factors of the blade was finally proposed.