• Proceedings of the KSME Conference
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• 2000.04a
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• pp.505-510
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• 2000

Vehicle performances such as fuel consumption and catalyst-out emissions are affected by a driving pattern, which is defined as a driving cycle with the grade in this study. We developed an algorithm to recognize a current driving pattern by using a neural network. And this algorithm can be used in adapting the driving control strategy to the recognized driving pattern. First, we classified the general driving patterns into 6 representative driving patterns, which are composed of 3 urban driving patterns, 2 suburban driving patterns and 1 expressway driving pattern. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, relative duration spent at stop, average acceleration and average grade are chosen to characterize the driving patterns. Second, we used a neural network (especially the Hamming network) to decide which representative driving pattern is closest to the current driving pattern by comparing the inner products between them. And before calculating inner product, each element of the current and representative driving patterns is transformed into 1 and -1 array as to 4 levels. In the end, we simulated the driving pattern recognition algorithm in a temporary pattern composed of 6 representative driving patterns and, verified the reliable recognition performance.

• Journal of the Korean Professional Engineers Association
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• v.26 no.3
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• pp.34-43
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• 1993

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.14-20
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• 2015

The Korea ministry of land, infrastructure and transport set the goal of cutting greenhouse gas emissions from the transport sector by 34.3% relative to the business as usual scenario by 2020. In order to achieve this goal, support is being given to education and information regarding eco-driving. As a practical measure, however, a vehicle control strategy for decreasing fuel consumptions and emissions is necessary. Therefore, this paper presents an optimized driving model in order to decrease fuel consumption. Scenarios were established by driving mode. The speed profile for each scenario applied to Comprehensive Modal Emission Model and then each fuel consumption was estimated. Scenarios and speed variation with the least fuel consumption were derived by comparing the fuel consumptions of scenarios. The optimized driving model was developed by the derived the results. The speed profiles of general driver were collected by field test. The speed profile of the developed model and the speed profile of general driver were compared and then fuel consumptions for each speed profile were analyzed. The fuel consumptions for optimized driving were decreased by an average of 11.8%.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.270-275
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• 2009

In a high performance motor driving system, PI controller which is simple implementation is frequently used. But, PI controller has various problems because of an integrator saturation. Therefore, the various methods of an anti-windup have been studied to solve such problems. But, conventional anti-windup methods have still problem with large overshoot and long settling time in the driving conditions. In this paper, to improve such problem, it is proposed a new anti-windup strategy. Using LPF and PI controller, it is improved an overshoot and reduced settling time. Simulation results have verified usefulness of the proposed method using MATLAB/Simulink.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.32-37
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• 2018

This paper proposes autonomous speed control strategy for an Electric Vehicle on urban road. SNU campus road is used to reperesent urban road situation. Motor efficiency of driving on campus circulation road can be improved by controlling velocity properly. Given information of campus road, especially slope of road, acceleration is selected from candidate, considering consumed power, human factor and driving time. To apply urban situation, preceding vehicle is also considered. With preceding vehicle, acceleration is defined according to clearance and relative velocity. Acceleration is bounded in normal range. Proposed acceleration control method is activated with proper velocity range for campus circulation road. With acceleration control, motor efficiency becomes better than driving with constant vehicle. To evaluate the performance of proposed acceleration controller, simulation study is conducted via MATLAB.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.642-646
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• 2008

Signaling system, for which ensuring safe train operation, must be a fail-safe system with higher reliability and safety. TCS has made significant improvements both on signaling system, from relay based ground signaling system to computer based on-board system, and on driving mode, form low speed and low density driving to TCS supporting high speed and high density driving. In addition, TCS has been applied to rolling stock with a wide variety of context according to the characteristics of rolling stock or railway infrastructure. In this paper, therefore, we confirmed the basic concept of ATC system and analyzed its development process in technology context via referencing international cases and ATC systems introduced and applied in Korea. Based on those analyses, we suggested the new TCS development strategy for its suitable application to high speed line, conventional line and metro, and we also provided technical considerations related to TCS application.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.555-561
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• 2010

In this paper, the constant current controller is designed to regulate the driving current of a power LED. The controller design model of the power LED including its driving circuit is proposed to design the constant current controller. A buck converter is also introduced to drive the power LED. The PI-based digital controller is implemented to validate the proposed strategy for the power LED driving.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.173-178
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• 1998

The strategy presented in this paper is based on modifying the past patterens and adjusting the content of the driving patterns by a new algorithm. Learning happens during the driving procedure of a mobile vehicle. The purpose of this paper is to solve the problem how to realize the hardware neurocomputer by back propagation (BP) neural network learning on-line.

• International Journal of Automotive Technology
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• v.7 no.5
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• pp.619-624
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• 2006

This paper presents design of a vehicle stop-and-go cruise control strategy based on analyzed results of the manual driving data. Human drivers driving characteristics have been investigated using vehicle driving data obtained from 100 participants on low speed urban traffic ways. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would feel comfortable to the human driver under low speed stop-and-go driving conditions. Vehicle following characteristics of the cruise controlled vehicle have been investigated using a validated vehicle simulator and real driving radar sensor data.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.2
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• pp.32-38
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• 2006

A vehicle adaptive cruise control strategy based on human drivers' driving characteristics has been investigated. Human drivers driving characteristics have been analyzed using vehicle test data obtained from 125 participants. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would reduce the workload of the human driver. Vehicle following characteristics of the cruise controlled vehicle have been compared to real-world driving radar sensor data of human drivers using a validated vehicle simulator. and compare nominal cruise control and adaptive cruise control.