• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.313-319
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• 2007

In this paper, we propose a novel method to extract an area of car licence plate and codes of vehicle number from a photographed car image using features on vertical edges and a new Fuzzy neural network algorithm to recognize extracted codes. Prewitt mask is used in searching for vertical edges for detection of an area of vehicle number plate and feature information of vehicle number palate is used to eliminate image noises and extract the plate area and individual codes of vehicle number. Finally, for recognition of extracted codes, we use the proposed Fuzzy neural network algorithm, in which FCM is used as the learning structure between input and middle layers and Max_Min neural network is used as the learning structure within inhibition and output layers. Through a variety of experiments using real 150 images of vehicle, we showed that the proposed method is more efficient than others.

• Journal of Drive and Control
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• v.12 no.3
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• pp.18-24
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• 2015

The plug-in hybrid electric vehicle has a high fuel economy and can be driven long distances. Its different modes include the electric vehicle, hybrid electric vehicle, and only engine operating mode. A power management strategy is important to determine which mode should be selected. The strategy makes the vehicle more efficient using appropriate power sources for driving. However, the strategy usually needs a driving speed profile which is future driving cycle. If the profile is known, the strategy easily determines which mode is driven efficiently. However, it is difficult to estimate the speed profile for a real system. To address this problem, this paper proposes a new power distribution strategy using a neural network. The average speed and driving range are used as input parameters to train the neural network system. The strategy determines a limit for the use of the battery and the desired power is distributed between the engine and the motor simultaneously. Its fuel economy can increase by improving the basic strategy.

• ETRI Journal
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• v.42 no.5
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• pp.700-711
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• 2020

Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned aerial vehicle (UAV)-based 6G mobile networks using free space optical (FSO) links have been recently proposed. Considering the advancements made in UAVs, big data processing, and artificial intelligence precision control technologies, the formation of an additional wireless network based on UAV aerial platforms to assist the existing fixed base stations of the mobile radio access network is considered a highly viable option in the near future. In this paper, a combined time bound optimization scheme is proposed that can adaptively satisfy the control and communication time constraints as well as the processing time constraints in FSO-based 6G UAV aerial networks. The proposed scheme controls the relation between the number of data flows, input data rate, number of worker nodes considering the time bounds, and the errors that occur during communication and data processing. The simulation results show that the proposed scheme is very effective in satisfying the time constraints for UAV control and radio access network services, even when errors in communication and data processing may occur.

• Journal of Veterinary Clinics
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• v.33 no.5
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• pp.278-285
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• 2016

Epidemiological studies have shown the association between transportation of live animals and the potential transmission of infectious disease between premises. This finding was also observed in the 2014-2015 foot-and-mouth disease (FMD) outbreak in Korea. Furthermore, slaughterhouses played a key role in the global spread of the FMD virus during the epidemic. In this context, in-depth knowledge of the structure of direct and indirect contact between slaughterhouses is paramount for understanding the dynamics of FMD transmission. But the social network structure of vehicle movements to slaughterhouses in Korea remains unclear. Hence, the aim of this study was to configure a social network topology of vehicle movements between slaughterhouses for a better understanding of how they are potentially connected, and to explore whether FMD outbreaks can be explained by the network properties constructed in the study. We created five monthly directed networks based on the frequency and chronology of on- and off-slaughterhouse vehicle movements. For the monthly network, a node represented a slaughterhouse, and an edge (or link) denoted vehicle movement between two slaughterhouses. Movement data were retrieved from the national Korean Animal Health Integrated System (KAHIS) database, which tracks the routes of individual vehicle movements using a global positioning system (GPS). Electronic registration of livestock movements has been a mandatory requirement since 2013 to ensure traceability of such movements. For each of the five studied networks, the network structures were characterized by small-world properties, with a short mean distance, a high clustering coefficient, and a short diameter. In addition, a strongly connected component was observed in each of the created networks, and this giant component included 94.4% to 100% of all network nodes. The characteristic hub-and-spoke type of structure was not identified. Such a structural vulnerability in the network suggests that once an infectious disease (such as FMD) is introduced in a random slaughterhouse within the cohesive component, it can spread to every other slaughterhouse in the component. From an epidemiological perspective, for disease management, empirically derived small-world networks could inform decision-makers on the higher potential for a large FMD epidemic within the livestock industry, and could provide insights into the rapid-transmission dynamics of the disease across long distances, despite a standstill of animal movements during the epidemic, given a single incursion of infection in any slaughterhouse in the country.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.527-537
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• 2012

In this paper, we propose unmanned vehicle based tracking network (UVTN) architecture and algorithms which employ multiple autonomous unmanned ground vehicles (AUGV) to efficiently follow targets in a group. The goal of UVTN is to maximize the service coverage while tracking target nodes for monitoring or providing the network access. In order to achieve this goal, UVTN performs periodic expansion and contraction which results in optimized redistribution of AUGV's in the network. Also, enhanced algorithms such as fast contraction and longest first are also discussed to improve the performance of UVTN in terms of the average coverage ratio and traveled distance. Simulation results show that the proposed UVTN and enhanced algorithms can effectively track the moving target and provide the consistent coverage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.488-489
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• 2009

In this paper, a FlexRay module and gateway is developed for high speed in-vehicle network. Speed of FlexRay is 10 times higher than that of CAN. In this paper, FlexRay module is implemented using MC9S12XF512 micro-controller and gateway converting CAN message to FlexRay message.

• Journal of IKEEE
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• v.25 no.4
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• pp.766-769
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• 2021

In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.98-101
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• 2009

Recently, there is use of the vehicle network for vehicle diagnostic method and Increased use of the vehicle protocol such as (CAN(Controller Area Network), MOST, LIN, FlexRay), Distributed control and data about the vehicle are being sought methods for real-time observation and monitoring and trend tends to have gone into this. In this case of automotive diagnostic module in today, there is Primarily to use DLC(Data Link Connector)Connector called self-check terminal. Generally, vehicle Diagnoses to use DLC Connector such as OBD2(On Board Diagnostics) Through Diagnostic Module(scanner). But there limit diagnostic as engine and powertrain part, and not consider user's perspective In this paper, By designing Vehicle diagnostic monitoring system using Ethernet Port, transmit and Receives CAN protocol vehicle data, and implement Easily monitoring system that provide and Diagnoses to provide vehicle's state and information to use PC.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.65-70
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• 2017

This paper presents the implementation for the master changing algorithm between nodes in a general electric vehicle. The packet processing method based on the unique network method of an electric vehicle is that the method of processing a communication packet has the priority from the node of a vehicle installed at both ends. An important factor in deciding master or slave in a train is that the request data, the status data, and transmits or control codes of sub-devices are controlled from the node which master becomes. If the request data or the status data is transmitted from the non- master side, it is very important that only one of the devices of both stages be master since the data of the request data may collide with each other. This paper proposes an algorithm to select master or slave depending on which vehicle is started first, which node is master or slave, and whether the vehicle key is operation. Finally experimental results show the stable performance and effectiveness of the proposed algorithm.