The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to de...The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to detect running vehicle position for synchronized current generation. To maintain the stable levitating condition during vehicle running, the irregularity of guideway surface should be monitored by sensors measuring the displacement and acceleration between vehicle and guideway. In this study, the application methods of these sensors in the high speed maglev are investigated and through the experiments by using the small-scale test bed, the validity of examined methods is confirmed.展开更多
Currently, most researches use signals, such as the coil current or voltage of solenoid, to identify parameters; typically, parameter identification method based on variation rate of coil current is applied for positi...Currently, most researches use signals, such as the coil current or voltage of solenoid, to identify parameters; typically, parameter identification method based on variation rate of coil current is applied for position estimation. The problem exists in these researches that the detected signals are prone to interference and difficult to obtain. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which adds a new group of secondary winding to the coil of the ordinary switching electromagnet. On the basis of electromagnetic coupling theory analysis and simulation research of the magnetic field regarding the primary and secondary winding coils, and in accordance with the fact that under PWM control mode varying core position and operating current of windings produce different characteristic of flux increment of the secondary winding. The flux increment of the electromagnet winding can be obtained by conducting time domain integration for the induced voltage signal of the extracted secondary winding, and the core position from the two-dimensional fitting curve of the operating winding current and flux-linkage characteristic quantity of solenoid are calculated. The detecting and testing system of solenoid core position is developed based on the theoretical research. The testing results show that the flux characteristic quantity of switching electromagnet magnetic circuit is able to effectively show the core position and thus to accomplish the non-displacement transducer detection of the said core position of the switching electromagnet. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which provides a new theory and method for switch solenoid to control the proportional valve.展开更多
A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor a...A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.展开更多
Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorit...Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.展开更多
文摘The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to detect running vehicle position for synchronized current generation. To maintain the stable levitating condition during vehicle running, the irregularity of guideway surface should be monitored by sensors measuring the displacement and acceleration between vehicle and guideway. In this study, the application methods of these sensors in the high speed maglev are investigated and through the experiments by using the small-scale test bed, the validity of examined methods is confirmed.
基金supported by National Natural Science Foundation of China(Grant No.51175362)
文摘Currently, most researches use signals, such as the coil current or voltage of solenoid, to identify parameters; typically, parameter identification method based on variation rate of coil current is applied for position estimation. The problem exists in these researches that the detected signals are prone to interference and difficult to obtain. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which adds a new group of secondary winding to the coil of the ordinary switching electromagnet. On the basis of electromagnetic coupling theory analysis and simulation research of the magnetic field regarding the primary and secondary winding coils, and in accordance with the fact that under PWM control mode varying core position and operating current of windings produce different characteristic of flux increment of the secondary winding. The flux increment of the electromagnet winding can be obtained by conducting time domain integration for the induced voltage signal of the extracted secondary winding, and the core position from the two-dimensional fitting curve of the operating winding current and flux-linkage characteristic quantity of solenoid are calculated. The detecting and testing system of solenoid core position is developed based on the theoretical research. The testing results show that the flux characteristic quantity of switching electromagnet magnetic circuit is able to effectively show the core position and thus to accomplish the non-displacement transducer detection of the said core position of the switching electromagnet. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which provides a new theory and method for switch solenoid to control the proportional valve.
基金Project(50925727) supported by the National Fund for Distinguish Young Scholars of ChinaProject(60876022) supported by the National Natural Science Foundation of China+1 种基金Project(2010FJ4141) supported by Hunan Provincial Science and Technology Foundation,ChinaProject supported by the Fund of the Key Construction Academic Subject (Optics) of Hunan Province,China
文摘A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.
文摘Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.
