• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.103-111
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• 2021

Among transport vehicles, Special Vehicles (SVs) are seriously exposed to energy and environmental problems. In particular, elevator cars used when moving objects in high-rise buildings increase the engine's rotational speed (radian per second: RPM). At this time, when the vehicle accelerates rapidly while idling, energy consumption increases explosively along with the engine speed, and a lot of soot is generated. The purpose of this paper is to develop a bi-directional DC-DC converter for control of vehicle power and secondary battery used in an elevated ladder vehicle (EC) used in the moving industry. As a result of this paper, the performance test of the converter was conducted. The charging/discharging state of the converter was simulated using DC power supply and DC electronic load, and a performance experiment was conducted to measure the input/output power of the converter through a power meter. Through this experimental result, it was confirmed that the efficiency was more than 92% in Buck mode and Boost mode at maximum 1.2kW output.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.542-558
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• 2016

Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1031-1046
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• 2018

Vehicular ad hoc network (VANET) is a dedicated network to connect vehicles without any centralized administration or infrastructure. The wireless access in vehicular environments (WAVE) protocol leveraging IEEE 1609/802.11p is widely implemented for VANETs. However, in congested traffic situation, the performance of the WAVE system degrades significantly due to serious collision, especially for safety related broadcast services on the control channel (CCH) interval due to the inherent drawback of its collision avoidance mechanisms called carrier sense multiple access with collision avoidance (CSMA/CA). In this paper, we propose a method that can decrease the number of frame collisions in CCH with a few modifications to the IEEE 802.11p protocol. In the paper, vehicles still employ CSMA/CA to compete for the channel access opportunity. However, by taking advantage of periodicity of synchronization interval, a two-state switching scheme introducing two new inter frame space (IFS) is proposed to reduce the number of competing vehicles substantially and as a result, the collision probability is significantly decreased. The simulation results demonstrate the superiority of the proposed method in packet collision rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3678-3698
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• 2017

With the rapid development of vehicular ad hoc Network (VANET), it has gained significant popularity and received increasing attentions from both academics and industry communities in aspects of security and efficiency. To address the security and efficiency issues, a self-authentication and deniable efficient group key agreement protocol is proposed in this paper. The scheme establishes a group between road side units (RSUs) and vehicles by using self-authentication without certification authority, and improves certification efficiency by using group key (GK) transmission method. At the same time, to avoid the attacker attacking the legal vehicle by RSUs, we adopt deniable group key agreement method to negotiation session key (sk) and use it to transmit GK between RSUs. In addition, vehicles not only broadcast messages to other vehicles, but also communicate with other members in the same group. Therefore, group communication is necessary in VANET. Finally, the performance analysis shows superiority of our scheme in security problems, meanwhile the verification delay, transmission overheard and message delay get significant improvement than other related schemes.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.256-262
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• 2021

In order for small electric vehicles to drive on hilly roads in Korea, methods to improve the climbing ability and power performance of vehicles should be taken. In order to improve the power performance of small electric vehicles, the performance of motors mounted on electric vehicles should be improved. However, if the performance of the motor is improved to improve the power performance of the electric vehicle, it is possible to lower the price competitiveness accordingly. In addition, the power consumption of the battery is rapidly increased to drive the high-performance motor, so in order to introduce the small electric vehicle into the domestic market, various problems must be overcome. In order to commercialize small electric vehicles that do not emit harmful exhaust gases to the human body in the hilly domestic terrain, it is effective to introduce a separate continuously variable transmission system that can improve the climbing ability and power transmission ability. In this study, we propose a proprietary model of continuously variable transmissions that can be applied to small electric vehicles. The proposed continuously variable transmission is equipped with a spring in the driving pulley and the driven pulley, and has the advantage of performing a shift that increases torque in a situation where the vehicle needs to increase torque when driving on a hill. In addition, the basic design for commercialization of the proposed continuously variable transmission was carried out, and the prototype manufactured and attached to the body of a small electric vehicle.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.162-170
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• 2022

Today, automobiles have become an indispensable element in people's lives, and the distribution of vehicles with various autonomous driving functions is expanding. Sensors such as cameras are used to recognize various situations on the road as an essential element for autonomous driving functions, but camera sensors have disadvantages that are vulnerable to bad weather. In this paper, we present a derivation process that defines external weather environment factors that negatively affect the performance of a camera for an autonomous vehicle. Through the proposed process, it is expected that it will contribute to securing the reliability of the camera and further improving the safety of autonomous vehicles.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.63-71
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• 2024

Deep reinforcement learning (RL) significantly influences autonomous vehicle development by optimizing decision-making and adaptation to complex driving environments through simulation-based training. In deep RL, an activation function is used, and various activation functions have been proposed, but their performance varies greatly depending on the application environment. Therefore, finding the optimal activation function according to the environment is important for effective learning. In this paper, we analyzed nine commonly used activation functions for RL to compare and evaluate which activation function is most effective when using deep RL for autonomous vehicles to learn highway merging. To do this, we built a performance evaluation environment and compared the average reward of each activation function. The results showed that the highest reward was achieved using Mish, and the lowest using SELU. The difference in reward between the two activation functions was 10.3%.

• The Journal of the Korea Contents Association
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• v.6 no.5
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• pp.14-19
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• 2006

In this paper, we describe an arrival time guidance system of circular vehicles using GPS, CDMA and TCP/IP technology. The on-board equipment consists of a GPS receiver and a PDA phone. The on-board equipment sends the current position data of the vehicle to the positioning server via CDMA and Internet. The server predicts the arrival time to the next bus-stop. Any user can lookup the current position and the predicted arrival time of the vehicle utilizing his mobile phone, PDA phone, or Web.