• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.187-194
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• 2012

Vehicular communication networking is one of the most important building blocks of Intelligent Transportation System(ITS). The vehicular communication network is a wireless communication system enabling vehicles to communicate with each other as well as with roadside base stations. Especially, Wi-Fi based vehicle-to-infrastructure(V2I) communication is an emerging solution to improve the safety, traffic efficiency, and comfort of passengers. In this paper, we proposed a new communication hub platform for vehicles, and explained vehicle communication technology in short. Through car simulation results, we show thar our proposed system reduces signaling interference.

• Proceedings of the ESK Conference
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• 1996.10a
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• pp.185-190
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• 1996

Navigation system is regarded as the interface border line between the Intelligent Transportation Systems (ITS) and the driver as the prospective information provider of the ATIS (Advanced Traveler Information System). Following theory, if the navigation system appropriately designed and utilized, that can maximize the transport efficiency, contribute to improvements of the environments and road safety. To accomplish these dinds of objectives of the navigation system use, human factors plays an important roles specially focused on the driver's safety, performance and system usability. Because the effectiveness of the system depends on the acceptance of the system, and the extent to which the system conforms to driver physical and cognitive limitations and capabilities. Therefore, the ergonomic design vaniables must be seriously selected and reflected in early design step for more effective and appreciate product design. As the first step of this aim, this study selected and categorized the human factors design variables of the navigation system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.10 s.101
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• pp.1020-1027
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• 2005

In this paper, economic miniature RF transmitter module for intelligent transportation system(ITS) is described. This module which consists of ASK modulator, frequency synthesizer, power amplifier is operating at 5.8 GHz frequency band and implemented by using LTCC process technique. Thus, ultra small size of 0.8 CC and improved electrical performances has been obtained. From the test results, transmitting characteristics of 10 dBm ouput power and -46 dBc interchannel interference with 1.024 Mbps ASK modulated have been shown. Frequency synthesizer as a transmitting signal source reveals very short locking time of 26 usec and outstanding phase noise of -115 dBc/Hz at 1 MHz offset from 5.8 GHz center frequency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• Proceedings of the KWS Conference
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• 2010.05a
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• pp.32-38
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• 2010

Hybrid material(ceramic+metal) processes were developed for micro filter using ceramics coating at metal filter surface by thermal spray method, micro hole drilling at ceramic coated filter surface by femtosecond laser, and fiber laser direct welding of ceramic and metal (SUS304, SM45C) by capillary effect. Thermal spray process was used for ceramic powders and metal filters. The used ceramic powders were $Al_2O_3+40TiO_2$(Metco 131VF) powder of maximum particle size $5{\mu}m$ and ${Al_2O_3}99+$(Metco 54NS) power of maximum particle size 45m. Ceramic coated filters using thermal spray method had a great influence on powder material, particle size and coating thickness but had a fine performance as a micro filter. CW fiber laser was used to drill the top ceramic layer and melt the bottom metal layer for joining applications.

• Annual Conference of KIPS
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• 2005.11a
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• pp.51-54
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• 2005

최근 도로 네트워크 환경에서 날로 증가하는 교통 수요를 충족시키고 각종 교통 문제를 해결하기 위해서 지능형교통시스템(ITS, Intelligent Transportation System)을 적용하고 있다. 특히, 첨단교통정보 시스템(ATIS, Advanced Traveler Information System)은 개별 차량의 주행을 최적화시키는 시스템으로서 운전자에게 출발지에서 목적지까지 빠르고 쾌적한 주행경로를 제공하는 차량 경로계획 수립을 제공한다. 하지만 이러한 시스템은 도로 구간의 비용으로 정적인 값을 이용하므로 동적으로 변화하는 구간 비용을 가지고 도로 네트워크에서 최단 경로를 제공하기는 어렵다. 따라서, 이 논문에서는 교통 혼잡을 고려한 최단 경로 탐색 연산자를 제안한다. 제안된 연산자는 현재 시간 비용과 과거의 시간 비용 변화 량을 더하여 출발지에서 목적지까지 경로를 탐색하는데 이용한다. 이러한 방법은 시간에 따라 변화하는 도로의 상황을 반영하며 출발지에서 목적지까지의 최단 경로뿐만 아니라 예상 도착 시간을 추정할 수 있다. 또한 제안된 연산자는 효율적인 도로 이용, 물류비용 감소, 응급 상황 대체, 연료 절약 및 환경 오염 감소 등의 장점을 가지며 첨단교통정보시스템에서 응용 될 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.480-497
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• 2012

As a new business concept, machine-to-machine (M2M) communications are born from original telemetry technology with the intrinsic features of automatic data transmissions and measurement from remote sources typically by cable or radio. M2M includes a number of technologies that need to be combined in a compatible manner to enable its deployment over a broad market of consumer electronics. In order to provide better understanding for this emerging concept, the correlations among M2M, wireless sensor networks, cyber-physical systems (CPS), and internet of things are first analyzed in this paper. Then, the basic M2M architecture is introduced and the key elements of the architecture are presented. Furthermore, the progress of global M2M standardization is reviewed, and some representative applications (i.e., smart home, smart grid and health care) are given to show that the M2M technologies are gradually utilized to benefit people's life. Finally, a novel M2M system integrating intelligent road with unmanned vehicle is proposed in the form of CPS, and an example of cyber-transportation systems for improving road safety and efficiency are introduced.