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20th International Conference on Control, Automation and Systems (ICCAS)

作者： Heo, Hyeon Uk Kim, Dae Jung Chung, Chung Choo Hanyang Univ Dept Elect Engn Seoul 04763 South Korea Hanyang Univ Dept Elect & Biomed Engn Seoul 04763 South Korea

ISBN: (纸本)9788993215199

In this paper, we propose an estimation of the accurate vehicle position using Interacting Multiple Model Extended kalman filter (IMM EKF) when road surface varies. Since the vehicle has different cornering stiffness as the road surface varies, it is difficult to accurately estimate the position of the vehicle. To resolve this problem, we present the IMM EKF considering each model of different roads to improve the estimation performance. From the numerical simulation using MATLAB/CARSIM, we observed that the performance of the proposed algorithm improves vehicle positioning performance.

关键词： Autonomous Driving Interacting Multiple Model kalman filter algorithm Vehicle Positioning

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Front Moving Vehicle Detection and Tracking with kalman filter 4

Front Moving Vehicle Detection and Tracking with Kalman Filt...

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Rin, Vannat Nuthong, Chaiwat King Mongkuts Inst Technol Ladkrabang Int Coll Bangkok Thailand

ISBN: (纸本)9781728113227

Immature behavior, fatigue, and inattentional driving have been mainly identified as major issues in safety. Such human behaviors are considered to be the core problem for the occurrences of traffic accidents. As a result, Intelligent Transportation System including Safety Driver Assistance Systems plays an important role in traffic safety. This system becomes the focused research topics recently. This paper aims to make an investigation of front moving vehicles detection and tracking for the forward collision warning system. In the proposed system, there are two major parts, i.e. vehicle detection and vehicle tracking for position prediction. For the detection procedure, this paper makes use of computer vision techniques with shifting three frame difference method on video sequence and combines with edge detection. For object's boundary selection, this work applies the blob-detection to extract the center of mass and the bounding box features of the detected object. After having selected those features of the front vehicle the tracking process is carried on by utilizing kalman filter algorithm in order to improve the accuracy of object detection and position prediction. Three videos obtained on the urban structured road in Thailand are tested. Experimental results show that the detection rate of front moving vehicle performs better when kalman filter is applied for tracking.

关键词： front object detection object tracking kalman filter algorithm

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Real-time Vehicle Detection and Tracking Based on YOLOv3 Pruning Model

Real-time Vehicle Detection and Tracking Based on YOLOv3 Pru...

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第33届中国控制与决策会议

作者： Mingxiu Lin Jiayi Li Jiaxin Zhang Xinghui Li Shiyao Ji Yunli He College of Information Science and Engineering Northeastern University

Real-time detection and tracking of vehicles is very important in the field of automatic *** this paper,a YOLOv3 network based on pruning algorithm is proposed to solve the problem of real-time vehicle *** reducing the number of channels and layers in the backbone network,the computation of the model is *** thus,the rate of detection is greatly *** the basis of vehicle detection,the real-time tracking of multiple vehicle targets is completed by using kalman filter algorithm for prediction and Hungarian algorithm for data *** experimental results show that compared with the original YOLOv3 network,the model size is compressed by 95% to11.25 MB and the detection rate is doubled to 128.1 frames/s while the average accuracy is basically *** detection and tracking frame rate of the whole algorithm is 18 fps,and the recall is 98.5%.The algorithm also has strong robustness on complex traffic roads,and can basically realize real-time detection and tracking of road vehicles.

关键词： Real-time detection and tracking pruning algorithm kalman filter algorithm Hungarian algorithm

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Sound field measurement and evaluation research for radiated acoustic fields in amplitude-variable sonochemical systems

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MEASUREMENT & CONTROL 2019年第9-10期52卷 1499-1507页

作者： Kong, Yaguang Tao, Xuyang Chen, Zhangpin Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China

The content of this study is based on the background of sonochemistry processes. First, the hydrophone is used to measure the sound pressure in the reactor (the experimental ultrasonic frequency range is 20 +/- 1 kHz). The sound pressure signals are processed by threshold method, kalman filter algorithm, and five-point three-time smoothing method. These methods eliminate singular items in the sound pressure signal, filter noise, smooth burrs, and so on. In addition, the sound intensity value is calculated by dual hydrophone method. In this paper, the average intensity of ultrasonic cavitation is studied. The radial distribution and longitudinal distribution of cavitation intensity of the same tool head are studied, and the effects of different power and different tool heads on cavitation intensity are also discussed in this paper. The experimental results show that the instrument designed in this paper can effectively measure the distribution of ultrasonic sound field and evaluate the performance of the tool head. We can infer from the experiments that the performance of the diamond tool head is better than that of the nine-section whip and the dumbbell tool head.

关键词： Ultrasonic cavitation acoustic field distribution kalman filter algorithm hydrophone

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KF-based estimation of diode turn-off power loss using datasheet information

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ELECTRONICS LETTERS 2019年第20期55卷 1082-+页

作者： Naseri, F. Farjah, E. Ghanbari, T. Shiraz Univ Sch Elect & Comp Engn Adv Power Elect & Elect Vehicle Lab Shiraz Iran Shiraz Univ Sch Adv Technol Shiraz Iran

In this Letter, an effective method for estimation of turn-off power losses of power diodes using datasheet information is proposed. An effective regression method with non-linear least-squares algorithm is used to obtain approximate representations of the reverse-recovery charge and softness factor of the diode using datasheet information. kalman filter (KF) algorithm is then used to accurately acquire the value of diode forward current at the turn-off instant. Finally, the obtained information is used to estimate the turn-off power losses with simple calculations, based on approximated reverse-recovery equations. Despite the simplicity of the proposed technique, the results demonstrate that the estimation error does not exceed 4% in the worst case.

关键词： least squares approximations kalman filters regression analysis power semiconductor diodes reverse-recovery charge datasheet information kalman filter algorithm turn-off instant turn-off power losses approximated reverse-recovery equations estimation error KF-based estimation diode turn-off power loss power diodes effective regression method approximate representations nonlinear least-squares algorithm

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Structural time series model for energy demand in Iran's transportation sector

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CASE STUDIES ON TRANSPORT POLICY 2019年第2期7卷 423-432页

作者： Mousavi, Mir Hossein Ghavidel, Saleh Alzahra Univ Dept Econ Tehran Iran Islamic Azad Univ Dept Econ Firoozkooh Branch Firoozkooh Iran

Since the first oil crisis in the 1970s, demand for energy has undergone significant transformations. This paper intends to model the demand by the transportation sector for the petroleum products in Iran. We propose a structural time-series model with an underlying trend. After transforming the model to state-space form, we apply the kalman filter algorithm to estimate the parameters of the model for the period between 1980 and 2016. Results indicate that the underlying trend for gasoline and diesel demand by the transportation sector is non-linear. This trend can be used as proxy applicable for studying those variables which could enable us to explain technological improvements and changes in consumer preferences for gasoline and diesel over time. Results also show that technological improvements and changes in consumers' behavior led to a decrease in the consumption of diesel in recent years while it has no decreasing effect on gasoline. Demand functions show that gasoline and diesel are inelastic. Gasoline price elasticity of demand ranges between - 0.17 and - 0.24. The price elasticity of demand for diesel is very low and statistically insignificant at the 5% level. The income elasticity of demand for gasoline and diesel ranges between 0.34 and 0.48, and between 0.33 and 0.52 respectively, being strongly statistically significant. Finally, this paper proposes that the price of fuel should be determined by market mechanism to reduce fuel consumption.

关键词： Transportation Petroleum product's demand Random (Stochastic) processes Structural time series kalman filter algorithm State-space models

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kalman filter-based centralized controller design for non-square multi-input multi-output processes

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2018年 132卷 187-198页

作者： Fasih, Hamid Tavakoli, Saeed Sadeghi, Jafar Torabi, Hamed Univ Sistan & Baluchestan Dept Elect Engn Zahedan Iran Univ Sistan & Baluchestan Dept Chem Engn Zahedan Iran

In this paper, a simple and effective centralized controller is presented to control non-square multi-input multi-output industrial processes with heavy interactions and significant time delays. The control objective is to achieve a good load disturbance rejection and set point tracking. The controller and process model equations are represented in a stochastic state space. Also, the state and measurement equations respectively use general random walk model and finite impulse response model of the plant. To design the controller, a quadratic cost function including the control error vector as well as the input vector and its changes is introduced. A kalman filter algorithm is used to solve the discrete algebraic Riccati equation and to estimate the state vector of the controller recursively. This novel use of kalman filter combines the observer and controller in a block. No square down/up, pairing and pseudo-inversing, is required for the proposed control structure. This leads to the simplicity of controller design. To evaluate the performance of the proposed control strategy, it is applied to a simulation example and an industrial case study with heavy interactions and significant time delays, an atmospheric distillation column with 7 inputs and 4 outputs. Simulation results show that the proposed controller has a good performance in both load disturbance rejection and setpoint tracking. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Atmospheric distillation column Centralized controller General random walk models kalman filter algorithm Non-square multi-input multi-output process

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Unmanned aerial vehicle positioning based on multi-sensor information fusion

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Geo-Spatial Information Science 2018年第4期21卷 302-310页

作者： Wenjun Li Zhaoyu Fu The 710th Research Institute of China Shipbuilding Industry Corporation YichangChina

Unmanned aerial vehicle(UAV)positioning is one of the key techniques in the field of UAV *** the high positioning precision of UAV can be achieved through global positioning system(GPS),the frequency of updating signal in GPS is low and the energy consumption of GPS module is huge,which does not satisfy the real-time demand of UAV *** this paper,a multi-sensor information fusion method based on GPS,inertial navigation system(INS),and the visible light sensors is proposed for UAV *** kalman filter combining with simulated annealing algorithm is used to estimate the position error between GPS or INS and the visible light sensors,and then the motion trajectory is corrected according to this position error ***,the positioning accuracy of UAV can be improved in case of only INS being *** results demonstrate that the proposed method can remarkably improve the positioning accuracy and greatly reduce the energy consumption.

关键词： kalman filter algorithm simulated annealing algorithm target tracking integrated positioning system

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Remaining lifetime prediction for nonlinear degradation device with random effect

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Journal of Systems Engineering and Electronics 2018年第5期29卷 1101-1110页

作者： CAI Zhongyi CHEN Yunxiang GUO Jiansheng ZHANG Qiang XIANG Huachun Equipment Management and Unmanned Aerial Vehicle Engineering College Air Force Engineering University Defense Technology Academy China Aerospace Science and Industry Corporation Limited

For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function(PDF) of the first hitting time(FHT)is deduced. A parameter estimation method based on modified expectation maximum(EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model(SSM) and the kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.

关键词： remaining lifetime(RL) prediction nonlinear degradation model Wiener process random coefficient kalman filter algorithm

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Very Short-term Electric Load Forecasting for Real-time Power System Operation

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2018年第4期13卷 1419-1424页

作者： Jung, Hyun-Woo Song, Kyung-Bin Park, Jeong-Do Park, Rae-Jun Soongsil Univ Dept Elect Engn Seoul South Korea KEPCO Econ & Management Res Inst Busan South Korea Uiduk Univ Div Energy & Elect Engn Gyeongju South Korea

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the kalman filter algorithm is proposed. In order to apply the kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.

关键词： Very short-term electric load forecasting kalman filter algorithm

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