• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.83-89
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• 2021

To improve the performance of self-driving cars, the introduction of V2X, a communication technology that connects vehicles, infrastructure, and vehicles, is essential. Even if traffic information of the other vehicle is known, the structure of the intersection and a distance calculation algorithm are required for accurate calculations at roundabouts. This paper proposes a design algorithm for a rotating intersection and implemented in Matlab that complies with the national design rules and enables accurate calculations. Assuming the roundabout and the entrance/exit path to be a circle, a method for measuring the distance between vehicles at an arbitrary point was proposed using the horizontal shift of the entrance circle to the main circle. The algorithm could be used in fully autonomous vehicles by designing a roundabout suitable for the terrain by arbitrarily varying the angle between branches and the radius of curvature of the entrance and exit roads, and transmitting a warning signal when a collision between two driving vehicles is expected.

• ETRI Journal
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• v.39 no.2
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• pp.213-221
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• 2017

The advent of wireless access in vehicular environments (WAVE) technology has improved the intelligence of transportation systems and enabled generic traffic problems to be solved automatically. Based on the IEEE 802.11p standard for vehicle-to-anything (V2X) communications, WAVE provides wireless links with latencies less than 100 ms to vehicles operating at speeds up to 200 km/h. To date, most research has been based on field test results. In contrast, this paper presents a numerical analysis of the V2X broadcast throughput limit using a path loss model. First, the maximum throughput and minimum delay limit were obtained from the MAC frame format of IEEE 802.11p. Second, the packet error probability was derived for additive white Gaussian noise and fading channel conditions. Finally, the maximum throughput limit of the system was derived from the packet error rate using a two-ray path loss model for a typical highway topology. The throughput was analyzed for each data rate, which allowed the performance at the different data rates to be compared. The analysis method can be easily applied to different topologies by substituting an appropriate target path loss model.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1062-1070
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• 2022

In the present communication, pure and stable α-Bismuth Oxide (Bi₂O₃) nanoparticles (NPs) were synthesized by low temperature solution combustion method using urea as a fuel and calcined at 500℃. The synthesized sample was characterized by using powder X-ray Diffraction (PXRD), Scanning Electron Microscopy (SEM), Energy dispersive X-ray analysis (EDAX), Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FTIR) and UV-Visible absorption spectroscopy. The PXRD pattern confirms the formation of mono-clinic, stable and low temperature phase α-Bi₂O₃. The direct optical energy band gap was estimated by using Wood and Tauc's relation which was found to be 2.81 eV. The characterized sample was studied for X-ray/gamma ray shielding properties in the energy range 0.081-1.332 MeV using NaI (Tl) detector and multi channel analyzer (MCA). The measured shielding parameters agrees well with the theory, whereas, slight deviation up to 20% is observed below 356 keV. This deviation is mainly due to the influence of atomic size of the target medium. Furthermore an accurate theory is necessary to explain the interaction of X-ray/gamma ray with the NPs.The present work opens new window to use this facile, economical, efficient, low temperature method to synthesize nanomaterials for X-ray/gamma ray shielding purpose.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.59-62
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• 2017

In this paper, atmospheric pressure plasma treatment was proposed for high performance indium gallium zinc oxide thin film transistor (IGZO TFT). RF Ar plasma treatment is performed at room temperature under atmospheric pressure as a simple and cost effective channel surface treatment method. The experimental results show that field effect mobility can be enhanced by $2.51cm^2/V{\cdot}s$ from $1.69cm^2/V{\cdot}s$ to $4.20cm^2/V{\cdot}s$ compared with a conventional device without plasma treatment. From X-ray photoelectron spectroscopy (XPS) analysis, the increase of oxygen vacancies and decrease of metal-oxide bonding are observed, which suggests that the suggested atmospheric Ar plasma treatment is a cost-effective useful process method to control the IGZO TFT performance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.3
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• pp.226-232
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• 2023

As a potential alternative to flash memory, HfO₂/Al₂O₃ stacks appear to be a viable option as charge capture layers in charge trapping memories. The paper undertakes a review of HfO₂/Al₂O₃ stacks as charge trapping layers, with a focus on comparing the number, thickness, and post-deposition heat treatment and γ-ray and white x-ray treatment of such stacks. Compared to a single HfO₂ layer, the memory window of the 5-layered stack increased by 152.4% after O₂ annealing at ±12 V. The memory window enlarged with the increase in number of layers in the stack and the increase in the Al/Hf content in the stack. Furthermore, our comparison of the treatment of HfO₂/Al₂O₃ stacks with varying annealing temperatures revealed that an increased annealing temperature resulted in a wider storage window. The samples treated with O₂ and subjected to various γ radiation intensities displayed superior resistance. and the memory window increased to 12.6 V at ±16 V for 100 kGy radiation intensity compared to the untreated samples. It has also been established that increasing doses of white x-rays induced a greater number of deep defects. The optimization of stacking layers along with post-deposition treatment condition can play significant role in extending the memory window.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.891-894
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• 2024

The quest for reliability in Artificial Intelligence (AI) is progressively urgent, especially in the field of next generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge number of devices and will offer innovative services invested with AI and Machine Learning tools. Wireless communications, in general, and medium access control (MAC) techniques were among the fields that were heavily affected by this improvement. This study presents the applications and services of future communication networks. This study details the Medium Access Control (MAC) scheduler of Beyond-5G/6G from 3^rd Generation Partnership (3GPP) and highlights the current open research issues which are yet to be optimized. This study provides an overview of how AI plays an important role in improving next generation communication by solving MAC-layer issues such as resource scheduling and queueing. We will select C-V2X as our use case to implement our proposed MAC scheduling model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.240-257
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• 2022

Autonomous vehicles use various local sensors such as camera, radar, and lidar to perceive the surrounding environment. However, it is difficult to predict the movement of vulnerable road users using only local sensors that are subject to limits in cognitive range. This is true especially when these users are blocked from view by obstacles. Hence, this paper developed an algorithm for collision avoidance with VRU using V2X information. The main purpose of this collision avoidance system is to overcome the limitations of the local sensors. The algorithm first evaluates the risk of collision, based on the current driving condition and the V2X information of the VRU. Subsequently, the algorithm takes one of four evasive actions; steering, braking, steering after braking, and braking after steering. A simulation was performed under various conditions. The results of the simulation confirmed that the algorithm could significantly improve the performance of the collision avoidance system while securing vehicle stability during evasive maneuvers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.173-177
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• 2016

Korea, the traffic and transportation problems are significant because private cars are increasing constantly. Therefore, it is imperative to improve traffic condition so as to solve the problems such as traffic congestion and accidents which may occur due to the increase of vehicles in a limited area through the signal control. However, the current operating system for traffic control cannot provide car users the optimal signal but it generates a time delay of vehicles, traffic congestions etc. In this paper, we propose and implement the system based on V2X based automatic controller, which reduces the waste of time and the driver's psychological stress on the road intersection.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.45-54
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• 2022

Recently, the introduction of Cooperative Intelligent Transport Systems (C-ITS) has been attempted to solve the limitation of only the sensor of the vehicle itself. For example, vehicles traveling at intersections can drive more safely through C-ITS. By using V2X communication of WAVE and LTE, the driver can receive the status and time of traffic lights. However, LTE has a larger transmission delay time than WAVE, so timimg data may not match in real time. In this paper, using the SPaT message, it was confirmed that LTE has a larger C-ITS service transmission delay time than WAVE. Finally, it was confirmed that the timing data of SPaT provided by LTE corrected by the algorithm is similar to SPaT provided by WAVE. It was confirmed that safer intersection driving is possible based on real-time.

• Journal of IKEEE
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• v.27 no.4
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• pp.379-385
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• 2023

Distributed congestion control (DCC) is a technology that mitigates channel congestion and improves communication performance in high-density vehicular networks. Traditional DCC techniques operate to reduce channel congestion without considering quality of service (QoS) requirements. Such design of DCC algorithms can lead to excessive DCC actions, potentially degrading other aspects of QoS. To address this issue, we propose a deep reinforcement learning-based QoS-adaptive DCC algorithm. The simulation was conducted using a quasi-real environment simulator, generating dynamic vehicular densities for evaluation. The simulation results indicate that our proposed DCC algorithm achieves results closer to the targeted QoS compared to existing DCC algorithms.