• International Journal of Automotive Technology
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• v.7 no.5
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• pp.637-644
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• 2006

The detection of engine misfire events is one of major concerns in engine control due to its negative effect on air pollution and engine performance. In this paper, a misfire detection system based on crankshaft angular speed fluctuation is developed. Synthetic variable method is adopted for the preprocessing of crankshaft angular speed. This method successfully estimates the work output of each cylinder by finding the effect of combustion energy on the crankshaft rotational speed or acceleration after virtually removing the effect of the internal inertia forces from the measured crankshaft speed signals. The detection system is developed using neural network with the revised synthetic angular acceleration as input which is derived from the preprocessing. Mathematical simulation is carried out for developing and verifying the misfire detection system. Finally, the reliability of the developed system is validated through an experiment.

• Journal of Power Electronics
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• v.14 no.5
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• pp.967-979
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• 2014

This paper focuses on speed and current sensor fault detection and isolation (FDI) for induction motor (IM) drives. A new, accurate and high-efficiency FDI approach is proposed so that a system can continue operating with good performance even in the presence of speed sensor faults, current sensor faults or both. The proposed three paralleled adaptive observers are capable of current sensor fault detection and localization. By using observers, the rotor flux and rotor speed can be estimated which allows the system to run under the speed sensorless vector control mode when a speed sensor fault occurs. In order to detect speed sensor faults, a threshold-based scheme is proposed. To verify the feasibility and effectiveness of the proposed FDI strategy, experiments are carried out under different conditions based on a dSPACE DS1104 induction motor drive platform.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.1-6
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• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1540-1551
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• 2015

The detection of position and speed in BLDC motors without using position sensors has meant many efforts for the last decades. The aim of this paper is to develop a sensorless technique for detecting the position and speed of BLDC motors, and to overcome the drawbacks of position sensor-based methods by improving the performance of traditional approaches oriented to motor phase voltage sensing. The position and speed information is obtained by computing the derivative of the terminal phase voltages regarding to a virtual neutral point. For starting-up the motor and implementing the algorithms of the detection technique, a FPGA board with a real-time processor is used. Also, a versatile hardware has been developed for driving BLDC motors through pulse width modulation (PWM) signals. Delta and wye winding motors have been considered for evaluating the performance of the designed hardware and software, and tests with and without load are performed. Experimental results for validating the detection technique were attained in the range 5-1500 rpm and 5-150 rpm under no-load and full-load conditions, respectively. Specifically, speed and position square errors lower than 3 rpm and between 10º-30º were obtained without load. In addition, the speed and position errors after full-load tests were around 1 rpm and between 10º-15º, respectively. These results provide the evidence that the developed technique allows to detect the position and speed of BLDC motors with low accuracy errors at starting-up and over a wide speed range, and reduce the influence of noise in position sensing, which suggest that it can be satisfactorily used as a reliable alternative to position sensors in precision applications.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.481-490
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• 2008

Reaction wheel is one of the actuators for spacecraft attitude control, which generates torque by changing an inertial rotor speed inside of the wheel. In order to generate required torque accurately and estimate an accurate angular momentum, wheel speed should be measured as close to the actual speed as possible. In this study, two conventional speed detection methods for high speed motor with digital tacho pulse (Elapsed-time method and Pulse-count method) and their resolutions are analyzed. For satellite attitude maneuvering and control, reaction wheel shall be operated in bi directional and low speed operation is sometimes needed for emergency case. Thus the bias error at low speed with constant acceleration (or deceleration) is also analysed. As a result, the speed detection error of elapsed-time method is largely influenced upon the high-speed clock frequency at high speed and largely effected on the number of tacho pulses used in elapsed time calculation at low speed, respectively.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.23-28
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• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.2
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• pp.23-31
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• 1996

The crankshaft speed fluctuation was measured every crank angle. In order to detect the misfire, the engine and the dynamometer were considered as a single- degree of freedom system. From this modeling, the detection criteria were derived and examined by the engine test. By this method the single misfire or multiple misfires can be detected. Even on the condition of low load and higher speed than 3000rpm, where it was difficult through the other methods, misfire detection was carried out steadily. From this results, the method proposed by this paper proved reasonable.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.182-187
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• 1997

In this paper, a instantaneous speed measurement method using a three phase sinusoidal encoder is described and it's simulations are developed. The proposed method can easily detect the AC motor speed by using that the encoder is propotion to the AC motor speed. The performance of proposed method is confirmed by computer simulation and experiment results. The high accuracy of the optimum control system, AC motor speed detection is designed and proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.13-22
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• 2009

Recently, DSRC technology is used in the various fields such as parking system, BIS, ETC, etc. This paper suggests a traffic information system using this DSRC technology. The traffic information processing based on point detection using existing vehicle detection equipment is the system in which a collection and a service are operated separately while the traffic information system based on the link detection using DSRC is able to collect and provide the traffic information through the communication between RSE and OBU. The speed of a traffic congestion is high on the process converted from a point passing speed to a link average speed because the vehicle detection equipment makes the link traffic information into the point information. When the condition of traffic is deteriorated, traffic speed of the vehicle detection equipment becomes higher than DSRC. Especially, in this system, deflection by data of the traffic speed of the traffic information system is much decreased, and the unexpected condition detection and traffic condition are provided promptly.