• 한국자동차공학회논문집
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• 제12권4호
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• pp.110-118
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• 2004

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of ‘NARMAX’ form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

• 한국자동차공학회논문집
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• 제21권5호
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• pp.113-120
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• 2013

This paper presents a mathematical model to determine the optimal length of static messages that can achieve more efficient use of a FlexRay network. In order to determine the optimal length of static message, the proposed model evaluates the given set of messages with respect to a network utilization index, which is defined in this work. The efficient use of a FlexRay network is achieved by reallocating any static message whose length is equal or greater than the resulting value to the dynamic segment. The effectiveness of the proposed method is investigated by applying to the SAE benchmark data.

• 한국추진공학회지
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• 제22권3호
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• pp.72-80
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• 2018

연소불안정 현상은 연소기 내부에서 열발생 섭동과 음향 압력 섭동 사이의 피드백 관계로부터 도출된다. 특히 항공용 엔진에 대한 배출 가스 규제가 강화되면서, 환형 연소기에서의 연소불안정 연구에 대한 관심이 크게 증가하고 있다. 본 연구에서는 환형연소기에서 다양한 음향 모드를 계산할 수 있는 열음향 네트워크 모델을 개발 및 사용하였고, 이때 연소 모델은 화염전달함수를 적용하였다. 이와 같은 네트워크 모델을 사용하여 벤치마킹한 환형연소기의 실험데이터와 비교 분석하여 연소불안정 해석을 진행하였다.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

• 한국레이저가공학회지
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• 제5권3호
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• pp.21-30
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• 2002

In automotive industry, the studies about light weight vehicle and improving the productivity have been accomplished. For that, TRIP steel was developed and research for the laser welding process have been performed. In this study, the monitoring system using photodiode was developed for laser welding process of TRIP steel. With measuring light, neural network model for estimating bead width and tensile strength was made and weld quality classification algorithm was formulated with fuzzy inference method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.972-975
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• 2013

This work developed a code analysis & extraction tool named Function Network Analyzer (FNA) to reduce the cost of software safety analysis. FNA analyzes functions and variables which a given function depends on, and extracts subset of code that can be compiled of automotive operating system, final resulting a well-ordered code sequence that can be compiled for model checking technique. And the experimental result illustrates that FNA can get 100% accurate rate and over 96% reduction rate by testing API functions from trampoline system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.5098-5115
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• 2017

A number of automotive electronics-safety, driver assistance, and infotainment devices-have been deployed in recent vehicles. This raises new challenges regarding in-vehicular network arbitration. A performance analysis of non-destructive arbitration has revealed a fairness issue. The arbitration prioritizes without collisions, despite multiple simultaneous transmissions; however, the performances of the highest priority node and the lowest priority node are very different. In this paper, an ID-rotation arbitration method to solve the arbitration-fairness problem is proposed. The proposed algorithm was applied to several engine control units (ECUs), including a controller area network (CAN) controller. Experimental results showed that the algorithm improved the fairness as well as the total throughput within a specific performance constraint.

• 한국자동차공학회논문집
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• 제9권1호
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• pp.156-162
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• 2001

An effective and practical method is presented for solving the forward kinematics of a 6-DOF Stewart Platform, using neural network algorithm together with Newton-Raphson method. An approximated solution is obtained from trained neural network, then it is used as an initial estimate for Newton-Raphson method. A series of accurate solutions are calculated with reasonable speed for the entire workspace of the platform. The solution procedure can be used for driving a real-time simulation platform.

• Journal of Welding and Joining
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• 제32권2호
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• 한국자동차공학회논문집
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• 제10권5호
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• pp.29-34
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• 2002

This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.