• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.299-306
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• 2020

Image recognition using neural networks is widely used in various fields. In this study, we investigated licensed / unlicensed vehicle recognition systems necessary for vehicle number recognition and control when entering and exiting a specific area. This system is equipped with the function of recognizing the image, so it checks all the information on the vehicle number and adds the function to accurately recognize the vehicle number plate. In addition, it is possible to check the vehicle number more quickly using a neural network.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.157-161
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• 2011

This paper explains an error process for multimedia collaboration works with session management running on vehicle network environment. This system consists of an FDA and FRA. FDA is an agent that detects an error by hooking techniques for multimedia system based on vehicle network environment with session management. FRA is a system that is suitable for recovering software error for multimedia system with session management based on vehicle network environment. This paper describes only FRA. When multiple local sessions is opened, each local session manager sends information of participant to global session manager and take current information about session of processing in network.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1023-1030
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• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.29-39
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• 1997

A discrete model reference control scheme for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of discrete time nonlinar dynamics. The schmen employs a neural network to identify the plan systems, wher the neural network estimates the nonlinear dynamics of the plant. The algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed. Whith thd resulting identification model which contains the neural networks, the parameters of controller are adjusted. The proposed scheme is applied to the vehicle active four wheel system and shows the validity and effectiveness through simulation. The three-degree-of freedom vehicle handling model is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the yaw rate overshoot reduction of a typical mid-size car is improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response andl smaller side slip angle than the 2WS case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.899-903
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• 2017

In this paper, it is shown the in-vehicle network architecture and implementation for multimedia service which supports Human machine interface of multi-passengers. For multi-passengers' vehicle, it has to be considered the factor of network traffic, simultaneously data transferring to multi users and accessibility to use variety of media contents for passengers compared to conventional in-vehicle network architecture system Therefore, it is proposed the change of network architecture compared with general MOST network, implementation of designed software module which can be interoperable between ethernet and MOST network and accessible interface that passenger can be plugged into MOST network platform using their device based on ethernet network system.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.2
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• pp.39-47
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• 2010

As vehicles become intelligent for convenience and safety of drivers, in-vehicle networking (IVN) systems are essential components of intelligent vehicles. Recently, the chassis networking system which require increased network capacity and real-time capability is being developed to expand the application area of IVN systems. Also, FlexRay has been developed for the chassis networking system. However, FlexRay needs a complex scheduling method of static segment, which is a barrier for implementing the chassis networking system. Especially, if we want to migrate from CAN network to FlexRay network using CAN message database that was well constructed for the chassis networking system by automotive vendors, a novel scheduling method is necessary to be able to reduce design complexity. This paper presents a node-based scheduling method for FlexRay network system. And, in order to demonstrate the method's feasibility, its performance is evaluated through an experimental testbed.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.51-58
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• 2002

Vehicle dynamics control systems are. complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3025-3047
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• 2021

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive Ethernet technology. The proposed system provides design and optimization algorithms for automotive networking technology related to AVB (Audio Video Bridge) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machine to Machine (RLMM) plays an outstanding role in reducing the latency among devices. RLMM's approach to real-world experimental cases indicates a reduction in latency of around 41.2%. The setup optimized for the automotive network environment is expected to significantly reduce the time in the development and design process. The results obtained in the study of image transmission latency are trustworthy because average values were collected over a long period of time. It is necessary to analyze a latency between multimedia devices within limited time which will be of considerable benefit to the industry. Furthermore, the proposed reliable camera and video streaming through optimized AVB device settings would provide a high level of support in the real-time comprehension and analysis of images with AI (Artificial Intelligence) algorithms in autonomous driving.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.401-407
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• 2007

This paper proposes the ethernet-based in-vehicle networking method for "body" and "multimedia" domains. The ethernet-based in-vehicle networking method should modify the topology and the layer 2 for traffic shaping. In this paper, we simulate the two ring networking systems, the Media Oriented Systems Transport (MOST) and the proposed system with the shaping by the network simulator 2 and evaluate each performance. In addition, we demonstrate the proposed networking system to exchange two kinds of traffic, i.e., QoS data and best-effort data, on the ring network constituting of three nodes. Finally this paper expects to substitute the ethernet-based in-vehicle network for the MOST in advance.