Least Square Support Vector Machine (LS-SVM) is an important machine of Support Vector Machine (SVM). But this method can not be used for online identification, and maybe lead to calculation inflation. A gradient recursive method of LS-SVM is presented by combining the LS-SVM method with the gradient method. This method can overcome the influence of bad data to the parameter estimation, has a stronger robustness, and improves the calculation speed of LS-SVM. The presented method is applied to the modeling of chaotic series. The simulation example validates the validity of the presented method.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Single phase Ba 2 Co 2−1.6 x (Mn 0.2 Fe 0.2 Zn 1.2 ) x Fe 12 O 22 hexaferrites with a self-aligned sheet stacked, highly c -axis oriented and multi-domain structure were fabricated to widen their electromagnetic wave absorption band without an applied magnetic field.
Road assets condition has a critical impact on road safety and efficiency. Accurate and efficient monitoring and management of road assets is a challenge. This research is focused on developing a cost efficient mobile surveying system to tackle this challenge. The system is equipped with LADARs (LAser Detection And Ranging) and a camera as exteroceptive sensors, and other sensors including Inertial Measurement Units (IMU), odometer and GPS (Global Positioning System).This system can acquire road assets information expeditiously in highly dynamic environments, where data collection has previously been inefficient, laborious and even dangerous.
Continuous Position, Velocity and Attitude (PVA) information is obtained by the integration of IMU, GPS, camera and odometer. Then PVA information is fused with range and remission data from LADARs to achieve multiple functions for road assets surveying and management. The functions include road clearance surveying, road surface profiling, 3D structure modelling, road boundary detection and road roughness measurement. The processing results are presented in a user-friendly graphical interface and can be saved as videos for convenient data management.
Two sets of GUI (graphical user interface) have been developed for data acquisition from all the sensors and data processing for the system functions. A Data Acquisition GUI is used for sensors control, data acquisition and pre-processing. It has multiple functions, including configuring LADARs scan frequency and resolution, displaying and recording data and exporting data with the required format. The Data Processing GUI includes various algorithms to perform all the data processing and management functions.
The camera in the proposed system provides not only a vision reference, but also visual odometry for improving PVA estimation when GPS is unreliable. In order to obtain a robust and accurate visual odometry, a new algorithm named PURSAC (PURposive SAmple Consensus) has been purposed for model fitting, which purposely selects sample sets according to the sensitivity analysis of a model against sampling noise and other information. This in turn can improve the accuracy and robustness of fundamental matrix estimation, resulting in a more precise and efficient visual odometry.
A prototype system designed for online data processing has been developed and four road tests have been successfully completed. Experimental results on a variety of roads have demonstrated the effectiveness of the proposed mobile surveying system.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Road assets' condition has a critical impact on road safety and efficiency. It is a big challenge to accurately and efficiently monitor and manage road assets. This paper proposes a mobile surveying system that is cost efficient and robust to acquire and manage road assets information in highly dynamic environments like highways and urban streets, where the data collection has previously been laborious and even dangerous for the staff performing the survey. Equipped with laser measurement systems, camera, proprioceptive sensors and novel sensor fusion algorithms; the proposed system can survey and manage road assets expeditiously. Laser sensors measure surroundings with range and remission data. Range data is processed to build up 3D model of surveyed objectives with position and attitude information from proprioceptive sensors. Remission data is used for extracting traffic lanes and signs on the roads. Each traffic lane's clearance of structures, like bridges and tunnels, is calculated and marked on the 3D model, and compared with the signs captured by the camera. Road surface condition is measured by both inertial and laser sensors. Any abnormal circumstance detected is reported automatically. The surveying results are presented in a user friendly interface and saved as videos for convenient data management. Experimental results of a prototype system demonstrate its performance for road assets monitoring and management.
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