• Journal of the military operations research society of Korea
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• v.12 no.2
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• pp.37-55
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• 1986

Vehicle fleet planning problem is generic name given to a whole class of practical decision making problems which find the vehicle routes and schedules to accomplish the reqired service to customers using vehicles. In this paper the various problems are classified into the three groups according to their characteristics: (1) vehicle routing problems, (2) vehicle scheduling problems, and (3) vehicle routing and scheduling problems. The State of the art of each group is described and the future research directions are presented.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.33-38
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• 2018

In this study, we try to detect new attacks for vehicle by learning only one class. We use Car-Hacking dataset, an intrusion detection dataset, which is used to evaluate classification performance. The dataset are created by logging CAN (Controller Area Network) traffic through OBD-II port from a real vehicle. The dataset have four attack types. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve high efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data, which are new attacks. In this study, we use one class classifier to detect new attacks that are difficult to detect using signature-based rules on network intrusion detection system. The proposed method suggests a combination of parameters that detect all new attacks and show efficient classification performance for normal dataset.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.50-61
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• 1994

A 300 meter class ROV(CROV300) is composed of three parts : a surface unit, a tether cable and an underwater vehicle. The vehicle controller is based on two processors : an Intel 8097-16-bit one chip micro-processor and a Texas Instruments TMS320E25 digital signal processor. In this paper, the surface controller, the vehicle controller and peripheral devices interfaced with the processors are described. These controllers transmit/receive measured status data and control commands through RS422 serial communication. Depth, heading, trimming, camera tilting, and leakage signals are acquired through the embedded AD converters of the 8097. On the other hand, altitude of ROV and lbstacle avoidance signals are processed by the DSP processor and periodically fetched by the 8097. The processor is interfaced with a 4-channel 12-bit D/A converter to generate control signals for DC motors an dseveral transistors to handle the relays for on/off switching of external devices.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.131-132
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• 2016

우리는 많은 소형 무인비행체들이 운행될 곧 다가올 미래에 대비하여 무인비행체들 간의 충돌 예방을 할 수 있는 방안에 대하여 연구를 진행하고 있다. 본 논문에서는 높은 밀집도를 가지는 소형 무인 비행체들 간의 충돌을 회피하기 위하여 무인비행체의 성능에 따라 클래스를 부여하고 비행 고도를 지정해주고 지정된 고도 내에서는 수평 비행을 하고 클래스간 이동을 위해서는 몇개의 정해진 지점에서만 수직 비행이 가능하도록 하는 제어 방안을 논하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1160-1166
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• 2006

The information of vehicle is very important for maintaining traffic order under the present complex traffic environments. This paper proposes a new vehicle plate recognition mechanism that is essential to know the information of vehicle. The proposed method uses SVM which is excellent object classification compare to other methods. Two-class SVM is used to find the location of vehicle plate and multi-class SVM is used to recognize the characters in the plate. As a real-time processing system using multi-step image processing and recognition process this method recognizes several different vehicle plates. Through the experimental results of real environmental image and recognition using the proposed method, the performance is proven.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.566-572
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• 2011

In recent semiconductor and FPD (Flat Panel Display) manufacturing processes, high clean-class delivery operation is required more and more for short working time and better product quality. Traditionally SLIM (Single-sided Linear Induction Motor) is widely used in the liner drive applications because of its simplicity in the rail structure. A magnetically levitated (Maglev) unmanned vehicle with SLIM traction, which is powered by a CPS (Contactless Power Supply) can be a high precision delivery solution for this industry. In this paper unmanned FPD-carrying vehicle, which can levitate without contacting the rail structure, is suggested for high clean-class FPD delivery applications. It can be more acceptable for the complex facilities composed with many processes which require longer rails, because of simple rail structure. The test setup consists of a test vehicle and a rounded rail, in which the vehicle can load and unload products at arbitrary position commanded through wireless communications of host computer. The experimental results show that the suggested vehicle and rail have reasonable traction servo and robust electromagnetic suspensions without any contact. The resolution of point servo errors in the SLIM traction system is accomplished under 1mm. The maximum gap error is ${\pm}0.25mm$ with nominal air gap length of 4.0mm in the electromagnetic suspensions. This type of automated delivery vehicle is expected to have significant role in the clean delivery like FPD glass delivery.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.47-52
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• 2011

Vehicle classification data are considerably used in the almost all fields of transportation planning and engineering. Highway agencies use a large number of vehicle classification schemes. Vehicles on the national highway are classified by 12-Category classification system, using number of axles, distances between axles, vehicle length, overhang, and other factors. In the case of using existing axle-sensor-based classification counters (that is, 12-category classification system), two-axle vehicles(Class 1 to 4) can be erroneously classified because a passenger vehicle becomes larger and similar with class 3 and 4. In this reason, this study proposes the vehicle classification scheme based on using vehicle height profiles obtained by a laser sensors. Also, the accuracy of the proposed method are tested through a field study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.96-106
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• 2020

To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.

• International Journal of Automotive Technology
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• v.7 no.7
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• pp.841-846
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• 2006

This paper presents a numerical approach to optimizing vehicle fuel economy in a higway bus. The method described is based on using a commercial software vehicle simulation to identify the relative efficiency of each of the vehicle systems, such as the engine hardware, engine software calibration, transmission, cooling system and ancillary drives. The simulation-based approach offers a detailed understanding of which vehicle systems are underperforming and by how much the vehicle fuel economy can be improved if those systems are brought up to best-in-class performance. In this way, the optimum vehicle fuel economy can be provided to the vehicle customer. A further benefit is that the simulation requires only a minimum number of vehicle testing for initial validation, with all subsequent field test cycles performed in software, thereby reducing development time and cost for the manufacturer.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.181-185
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• 2006

A study to get better vehicle fuel economy is described based on an express bus. The approach is based on using a commercial software vehicle simulation to identify the relative efficiency of each of the vehicle systems, such as the engine hardware, engine software calibration, transmission, cooling system and ancillary drives. The simulation-based approach offers a detailed understanding of which vehicle systems are underperforming and by how much the vehicle fuel economy can be improved if those systems are brought up to best-in-class performance. In this way, the optimum vehicle fuel economy can be provided to the vehicle customer. A further benefit is that the simulation requires only a minimum of vehicle testing for initial validation, with all subsequent field test cycles performed in software, thereby reducing development time and cost for the manufacturer.