• 대한전기학회논문지:시스템및제어부문D
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• 제50권8호
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• pp.367-375
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over modeling error, parameter variation and disturbance. Second, it needs accurate performance which have small overshoot phenomenon and steady state error to avoid colliding with ground surface or obstacles. Third, it needs continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, it needs interpolation method which can sole the speed dependency problem of controller parameters. To solve these problems, we propose a depth control method using Fuzzy Sliding Mode Controller with feedforward control-plane bias term and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• 한국자동차공학회논문집
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• 제7권5호
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• pp.178-185
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• 1999

Recently, Collision Warning System is developed to improve vehicle safety. This system chiefly uses radar. But the detected vehicle from radar must be decide whether it is the vehicle in the same lane of my vehicle or not. Therefore, Vision System is needed to detect traffic lane. As a preparative step, this study presents the development of algorithm to recognize traffic lane curve direction. That is, the Neural Network that can recognize traffic lane curve direction is constructed by using the information of short distance, middle distance, and decline of traffic lane. For this procedure, the relation between used information and traffic lane curve direction must be analyzed. As the result of application to sampled 2,000 frames, the rate of success is over 90%.t text here.

• 한국항해항만학회지
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• 제33권2호
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• pp.143-151
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• 2009

The field of vehicle routing is currently growing rapidly because of many actual applications in truckload and less than truckload trucking, courier services, door to door services, and many other problems that generally hinder the optimization of transportation costs in a logistics network. The rapidly increasing number of customers in such a network has caused problems such as difficulty in cost optimization in terms of getting a global optimum solution in an acceptable time. Fast algorithms are needed to find sufficient solutions in a limited time that can be used for real time scheduling. In this paper, the nearest L-method (NLNM) is proposed to obtain a vehicle routing solution. String neighbors of different lengths were chosen, tested and compared. The applied de crossing procedure is meant to solve the routes by NLNM by giving a better solution and shorter computation time than that of NLNM with long string neighbors.

• 한국지능시스템학회논문지
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• 제20권2호
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• pp.262-266
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• 2010

Recognizing a license plate of a vehicle has widely been issued. In this thesis, firstly, mean shift algorithm is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. We then present an approach to recognize a vehicle's license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. We show that the proposed system is well to recognize the license plate and shows some compute simulations.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.636-639
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• 2003

In this paper, we present a robust method for detecting other vehicles from n forward-looking CCD camera in a moving vehicle. This system uses edge and shape information to detect other vehicles. The algorithm consists of three steps: lane detection, ehicle candidate generation, and vehicle verification. First after detecting a lane from the template matching method, we divide the road into three parts: left lane, front lane, and right lane. Second, we set the region of interest (ROI) using the lane position information and extract a vehicle candidate from the ROI. Third, we use local orientation coding (LOC) edge image of the vehicle candidate as input to a pretrained neural network for vehicle recognition. Experimental results from highway scenes show the robustness and effectiveness of this method.

• 전자공학회논문지C
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• 제34C권8호
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• pp.88-100
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• 1997

This paper proposes a new concept for an optimal traffic signal cycle method which will reduce the average vehicle waiting time and improve average vehicle speed. Electro sensitive traffic system can extend the traffic cycle when there ar emany vehicles in the road or it can reduce the traffic consider vehicle length, so it can cause oveflow and reduce average vechicel waiting time at the intersection, we propose on optimal traffic cycle with fuzzy ruels and neural network. Computer simulation results prove that reducing the average vehicle waiting time which proposed considering passing vehicle's length for the optimal traffic cycle better than fixe dsignal method dosen't consider vehicle length.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.526-532
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• 2003

This paper describes a recognition method of a road direction for an autonomous vehicle based on magnetic sensors. Using the sensors mounted on a vehicle and the magnetic markers embedded along the center of road, the autonomous vehicle can recognize a road direction and control a steering angle. Using the front lateral deviation of a vehicle and the rear one, the road direction is calculated. The analysis of magnetic field, the acquisition technique of training data, the training method of neural network and the computer simulation are presented. According to the computer simulation, the proposed method is simulated, and its performance is verified. Also, the experimental test is confirmed its reliability.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.167-178
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• 2019

In this paper, we study the multi-objectives vehicle and drone routing problem with time windows, MOVDRPTW for short, which is defined in an urban delivery network. We consider the dual modal delivery system consisting of drones and vehicles. Drones are used as a complement to the vehicle and operate in a point to point manner between the depot and the customer. Customers make various requests. They prefer to receive delivery services within the predetermined time range and some customers require fast delivery. The purpose of this paper is to investigate the effectiveness of the delivery strategy of using drones and vehicles together with a multi-objective measures. As experiment datasets, we use the instances generated based on actual courier delivery data. We propose a hybrid multi-objective evolutionary algorithm for solving MOVDRPTW. Our results confirm that the vehicle-drone mixed strategy has 30% cost advantage over vehicle only strategy.

• 전자공학회논문지CI
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• 제49권4호
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• pp.82-88
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• 2012

VANET(Vehicular Ad Hoc Network)은 MANET(Mobile Ad Hoc Network)의 일종으로 기지국과 같은 기반시설의 도움 없이 차량 간의 무선 통신을 통해 구성되는 임시적인 네트워크이다. VANET은 차량들의 고속 이동성이나 차량 간 밀도 변화로 인해 빈번한 링크 단절 및 네트워크 토폴로지 변화 등을 야기한다. 이러한 VANET의 특성으로 인해 기존의 MANET에서 사용되는 AODV와 DSR과 같은 경로기반 라우팅 프로토콜보다는 주변 노드의 정보만을 이용하는 GPSR(Greedy Perimeter Stateless Routing)과 같은 지리기반 라우팅 프로토콜이 매우 적합하다. 그러나 GPSR은 차량 노드의 밀도가 낮은 환경에서는 잦은 링크 단절과 반복적인 로컬 맥시멈으로 인해 전송지연 및 데이터 손실이 발생할 수 있다. 따라서 본 논문에서는 차량의 밀도가 낮은 VANET 환경에서 효율적인 라우팅을 수행하기 위해 2-hop 이웃 노드의 존재가 없는 경우에 DTN 기반의 라우팅을 수행하는 DTVR(Delay Tolerant Vehicular Routing) 알고리즘을 제안한다. ns-2를 이용한 성능분석 결과 제안된 DTVR 프로토콜이 차량 밀도가 낮은 환경에서 기존 라우팅 프로토콜보다 성능이 우수함을 입증하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.407-410
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• 2012

최근, 무선 네트워크의 서비스가 널리 사용됨에 따라, 무선 네트워크 모듈의 개발 기술 및 그 활용도가 점차 확대되고 있으며, 그에 따라서 IT 융합 산업들이 많이 나타나고 있는 추세이다. 본 연구는 자동차 정보를 가져오기 위해 OBD-II 통신을 이용하고, 외부 서버로 데이터를 전송하여 다른 외부 장치에서도 고속 주행 중인 차량의 상태 정보를 확인 할 수 있는 시스템을 개발하였다. 차량 내부의 각종 센서들로부터 OBD-II 커넥터를 이용하여 정보를 읽고 사용자가 보기 쉽게 변환한 뒤, 무선 네트워크 모듈을 이용하여 외부 서버로 전송을 하는 고속 차량 상태 확인 시스템을 구현하였다. 개발한 시스템의 성능 테스트를 위하여 실제 써킷에서 고속 주행 중인 경주용 차량을 이용했다. 고속 주행 중인 차량에서 발생된 데이터는 OBD-II 스캐너를 통하여 전송되었으며, 고속 차량 상태 확인 시스템은 이 데이터가 정상적으로 수신 되는 것을 확인하였다. 수신된 데이터는 무선 네트워크를 이용하여 외부 서버로 전송을 하였을 때 똑같은 데이터가 에러 없이 송 수신되는 것을 확인하였다. 향후 이런 기술은 새로운 자동차 IT 융합의 새로운 연구 분야로써 성장하게 될 것이다.