• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.147-158
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• 2005

Day after day the information of vehicle under the complex traffic environments is greatly required not only for traffic flow but also for vehicle disclosure of traffic violation, Vehicle information can be obtained from a recognition of vehicle license plate, This paper proposes a new vehicle plate recognition mechanism that uses moving style vehicle-loaded camera, The method is a real-time processing system using multi-step image processing and recognition process that recognizes general vehicles and special purpose vehicles, The experimental results of real environmental image and recognition using the proposed method are shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1152-1167
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• 2018

In this paper, I propose an application layer cooperative congestion control scheme for safety message broadcast in vehicular networks, called CMS, that adaptively controls a vehicle's safety message rate and transmit timing based on the channel congestion state. Motivated by the fact that all vehicles should transmit and receive an application layer safety message in a periodic manner, I directly exploit the message itself as a means of estimating the channel congestion state. In particular, vehicles can determine wider network conditions by appending their local channel estimation result onto safety message transmissions and sharing them with each other. In result CMS realizes cooperative congestion control without any modification of the existing MAC protocol. I present extensive NS-3 simulation results which show that CMS outperforms conventional congestion control schemes in terms of the packet collision rate and throughput, especially in a high-density traffic environment.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.57-61
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• 2002

We design Collision Avoidance System using model vehicle. The purpose of this system(Collision Avoidance System) is to maintain continuously constant distance between a forward running vehicle and a following automatic guided vehicle(AGV). For this system, we design modeling of vehicle and observe this through simulation. By sing super sonic sensors to measure the distance between vehicles and controller using 80c196kc for changing velocity of motor, we design Collision Avoidance System as maintaining continuously constant distance between vehicles.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.729-734
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• 2004

The e-Logistics means the virtual business activity and service architecture among the logistics companies based on the Internet technology. The management of vehicles' location in most conventional vehicle tracking system has some critical defects when it deals with data which are continuously changed. It means the conventional vehicle tracking system based on the conventional database is unable eventually to cope with the environment that should manage the frequently changed location of vehicles. The important things in the evaluation of the vehicle tracking system is to determine the threshold of cost of database ,update period and communication period between vehicles and the system. In other words, the difference between the reallocation of vehicle and the data in database can evaluate the overall performance of vehicle tracking systems. Most of the previous works considers only the information that is valid at the current time, and is hard to manage efficiently the past and future information. To overcome this problem, the efforts on moving objects management system(MOMS) and uncertainty processing have been started from a few years ago. In this paper, we propose an uncertainty processing model and system implementation of moving object that tracks the location of the vehicles. We adopted both linear-interpolation method and trigonometric function to chase up the location of vehicles for the past time as well as future time, respectively. We also explain the comprehensive examples of MOMS and uncertainty processing in parcel application that is one of major application of e-Logistics domain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.15-24
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• 2022

At present, there are few studies on signal control of pedestrian traffic flow and non-motor traffic flow at intersections. Research on the optimization scheme of mixed traffic flow signal control can coordinate and control the overall traffic flow of pedestrians, non-motor vehicles and motor vehicles, which is of great significance to improve the congestion at intersections. For the traffic optimization of intersections, this paper starts from two aspects: channelization optimization and phase design, and reduces the number of conflict points at intersections from spatial and temporal right-of-way allocation respectively. Taking the classical signal timing method as the theoretical basis, and aiming at ensuring the safety and time benefit of traffic travelers, a channelization optimization and signal control scheme of the intersection is proposed. The channelization and phase design methods of intersections with motor vehicles, non-motor vehicles and pedestrians as objects are discussed, and measures to improve the channelization optimization of intersections are proposed. A multi-objective optimization model of intersection signal control was established, and the model was solved based on NSGA-II algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.131-138
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• 2023

Globally, the eco-friendly industry is developing due to the climate crisis. Electric vehicles are an eco-friendly industry that is attracting attention as it is expected to reduce carbon emissions by 30~70% or more compared to internal combustion engine vehicles. As electric vehicles become more popular, charging stations have become an important factor for purchasing electric vehicles. Recent research is using artificial intelligence to identify local demand for charging stations and select locations that can maximize economic impact. In this study, in order to contribute to the improvement of the performance of the electric vehicle charging station demand prediction model, nationwide data that can be used in the artificial intelligence model was defined and a pre-processing technique was proposed. In addition, a preprocessor, artificial intelligence model, and service web were implemented for real charging station demand prediction, and the value of data as a location selection factor was verified.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.87-97
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• 2023

Charger infrastructure facilities are designed and installed based on a constant power supply. Initially designed charging facilities support charging of rapidly growing electric vehicles on a limited power supply basis. In addition, current commercial vehicles can only be fully charged, and are supported by the rapid equalization charging method. However, commercial vehicles operate according to a set schedule, so flexible charging is essential. In this paper, we propose a power operation method with more than 20% efficiency improvement by using a fixed schedule-based charging scheduling and power distribution technique of a commercial bus based on the same amount of power in accordance with the rapid growth and increase of electric vehicles.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.189-198
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• 2022

As the number of people using the Internet increases, a new application known as the Internet of Things (IoT) has been emerged. Internet of Things makes it easier for machines and objects to exchange, compute, and coordinate information autonomously without human interference. It is a tool for attaching intelligence to a variety of contemporary objects in houses, hospitals, buildings, vehicles, and even cities. As a new emerging technology, the focus in current IoT surveys does not shed the light on deep understanding for IoT fundamentals, architectures, challenges, and solutions. For this reason, the objective of this paper is to introduce specifications for IoT definitions, characteristics, functional blocks, and different architectures as a cement for better understanding. Additionally, we present current documented IoT challenges, with the existing available solution for each challenge.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.263-272
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• 2021

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.