• 전자공학회논문지T
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• 제35T권3호
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• pp.46-52
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• 1998

본 논문에서는 차량에 부착된 번호 판을 컴퓨터에 입력한 후 이를 색 분해법과 역전파 신경망을 이용하여 자동차 번호를 고속으로 추출할 수 있는 방법을 제시하였다. 칼라 비디오 카메라에 의해 컴퓨터에 입력되는 자동차의 동화상을 R, G, B 신호로 분리한 후 승용차의 번호판 색상을 이용하여 R, G ,B의 각 농도에 맞는 임계치를 설정하여 2치화 시켜 번호판 영역을 추출한 후에 2 치화된 이 화상 신호를 프레임 버퍼에 기록하여 컴퓨터의 화상 데이터로 입력시켰다. 그리고 문자 인식 알고리즘을 적용한 후 문자 인식을 개선시키기 위해 역전파 신경 회로망을 적용하여 차랑 번호판 인식 시스템을 구현하였다. 또한 주변의 유사 색상의 존재로 인한 흔돈을 극소화시키기 위해 차량 번호판의 직사각형 구조를 이용하여 수평.수직선 추출 알고리즘을 사용하였으며 실험 결과 고속으로 차량 번호판 추출 및 인식이 가능함을 보였다.

• 스마트미디어저널
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• 제3권3호
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• pp.46-50
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• 2014

모바일 기기는 우리 삶에 많은 변화를 만들고 있다. 현재 가장 널리 쓰이는 모바일 운영체제로는 구글의 안드로이드와 애플사의 iOS, Microsoft사의 Windows Mobile이 있다. 그중에서도 안드로이드는 스마트폰과 태블릿 PC뿐만 아니라 자동차 분야까지 다양한 전자분야에서 연구되고 있다. MOST(Meclia Oriented Systems Transport)는 자동차산업 분야에서 고속 멀티미디어 통신에 사용되는 표준이다. 본 논문에서 안드로이드를 기반으로 MOST 네트워크를 이용해 차량용 인포테인먼트 소프트웨어를 설계한 결과를 제시한다.

• 전기학회논문지P
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• 제62권1호
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• pp.23-29
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• 2013

TCN(train communication network) standard was approved in 1999 by the IEC (IEC 61375-1) and IEEE (IEEE 1473-T) organizations to warrant a reliable train and equipment interoperability. TCN defines the set of communication vehicle buses and train buses. The MVB(multifunction vehicle bus) defines the data communication interface of equipment located in a vehicle and the WTB(wire train bus) defines the data communication interface between vehicles. The WTB and each MVB will be connected over a node acting as gateway. Also, to support applications demanding a high reliability, the standard defines a redundancy scheme in which the bus may be double-line and redundant-node implemented. In this paper we have presented protocol analysis platform for the WTB redundancy which is part of TCN system, to verify communication state of high-speed trains. As a confirmation of its validity, the technology described in this paper has been successfully applied to state monitoring and protocol verification of redundancy WTB based on TCN.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• 한국산학기술학회논문지
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• 제12권11호
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• pp.5272-5277
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• 2011

이동통신 기술의 발전과 자동차 산업의 발전은 현재 텔레매틱스 서비스라는 또 하나의 지능형 통신기반 자동차 서비스 산업을 일으키고 있다. 각국은 텔레매틱스 서비스의 무한한 산업적 시장성을 기대하며 본격적으로 연구 및 표준화 활동을 벌이고 있다. 텔레매틱스 서비스 제공을 위하여 차량안전통신 기술이 향후 가장 중요한 서비스 위치를 차지할 것으로 예상되며, 최근 이에 대한 연구가 기대되고 있다. 따라서 본 논문에서는 ad-hoc 네트워크를 이용하여 차량 간 통신을 통한 차량 사고에 대한 위험인지와 회피 방안에 대한 설계를 제안하고자 한다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.124-131
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• 2000

In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.

• 한국정밀공학회지
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• 제16권7호
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• pp.109-118
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• 1999

A graphic software is one of the most important components of the vehicle simulator. To increase a visual reality of the simulator, the graphic software should require several technologies such as three-dimensional graphics, graphic modeling of the vehicle and the environment, drivers biomechanical models, and real-time data processing. This study presents a real time graphic driving simulator of a construction vehicle. The graphic simulator contains the three models of the construction vehicle, the human, and the environment, and employes a neural network approach to decrease an on-line dynamic computation. An excavator model is represented using an object-oriented paradigm and contains the detailed information about a real-size vehicle. The human model is introduced for objective visual evaluations of the developed excavator model. Since the environment model plays an important role in a real-time simulator, a block-based approach is implemented and a text format is utilized for easier construction of environment. The simulation results are illustrated in order to demonstrate the applicability of developed models and the neural network approach.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.33-40
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• 2017

Internet of Vehicles (IoV) is a subset of Internet of Things (IoT) and it is an infrastructure for vehicles. Therefore, IoV consists of three main network including inter-vehicle network, intra-vehicle network, and vehicular mobile internet. IoV mainly used in urban traffic environment to provide network access for drivers, passengers and traffic management. Accordingly, many research works have focused on network technology. But, recent concerted efforts in academia and industry point to paradigm shift in IoV system. In this paper, we proposed a knowledge base for intelligence service in IoV. A detailed design and implementation of the proposed knowledged base is illustrated. We hope this work will show power of IoV as a disruptive technology.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 춘계학술대회 논문집
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• pp.68-75
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• 1997

This paper presents a new approach to the design of neural-fuzzy controller for the speed and azimuth control of a track vehicle. The proposed control scheme uses a Gaussian function as a unit function in the frzzy-neural network, and back propagaton algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a track vehicle driven by two independent wheels.

• 한국통신학회논문지
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• 제22권11호
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• pp.2432-2438
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• 1997

본 논문은 CCD 카메라로부터 얻어진 차량 영상에서 번호판 영역이 일정한 패턴의 광강도를 지니는 특징을 이용하여 번호판 영역을 추출학 문자인식을 개선하기 위하여 단일 역전파 신경망 대신 다중 역전파 신경망으로 차량 번호판 인식 시스템을 구현하였다. 본 논문의 실험 결과, 효율적인 문자 영역의 추출이 가능하고, 기존의 단일 역전파 방법보다 학습 시간이 단축되고 인식율이 향상됨을 보인다.