Recently,fault or health condition prediction of complex systems becomes an interesting research topic. However,it is difficult to establish precise physical model for complex systems,and the time series properties are often necessary to be incorporated for the prediction in practice. Currently,the LS-SVR is widely adopted for prediction of systems with time series data. In this paper,in order to improve the prediction accuracy,accumulated generating operation( AGO) is carried out to improve the data quality and regularity of raw time series data based on grey system theory; then,the inverse accumulated generating operation( IAGO) is performed to obtain the prediction results. In addition,due to the reason that appropriate kernel function plays an important role in improving the accuracy of prediction through LS-SVR,a modified Gaussian radial basis function( RBF) is proposed. The requirements of distance functions-based kernel functions are satisfied,which ensure fast damping at the place adjacent to the test point and a moderate damping at infinity. The presented model is applied to the analysis of benchmarks. As indicated by the results,the proposed method is an effective prediction one with good precision.
In this paper,we study a STM32F4 as the core of the bionic design and implementation of embedded mobile robot. Bionic robot based on color sensor TCS3200 contacts surface color data sending instructions,by the PWM wave to control the robot body tricolor LED color to achieve the surrounding color. Proven by a lot of experimental data,the bionic robot designed in this paper possesses the advantages of high stability and high accuracy,and bionic robot has completely independent behavior.
In order to quickly and efficiently get the information of the bottom of the shoe pattern and spraying trajectory,the paper proposes a method based on binocular stereo vision. After acquiring target image,edge detection based on the canny algorithm,the paper begins stereo matching based on area and characteristics of algorithm. To eliminate false matching points,the paper uses the principle of polar geometry in computer vision.For the purpose of gaining the 3D point cloud of spraying curve,the paper adopts the principle of binocular stereo vision 3D measurement,and then carries on cubic spline curve fitting. By HALCON image processing software programming,it proves the feasibility and effectiveness of the method.
With the rise of intelligent residential housing project and the implement of intelligent meter reading system,the four become the typical representative at the same time,they also become the short board of the old residential intelligent direction and constraints. As one of the four meter is an important measurement tool to save water resources. In the process of the development of society and technology,different types of meter reading methods have been derived,but there are still many problems,such as difficulty,time consuming,error copy,misreading. With the current mature image processing technology, the Internet technology and the rapid development of handheld intelligent terminal,the paper develop a meter reading system base on the Android system. The system can reduce the work intensity and the cost of meter reading,and it can make up the blank which old district and the mechanical meter reading can not be intelligent.
The organic Rankine cycle( ORC) is an effective way to recycle low temperature exhaust heat but pump for the ORC has several disadvantages such as great difficulty in manufacturing,easily-invited cavitations,low efficiency and high cost. Gas-liquid two-phase injector is a device without moving parts,in which steam is used to drive cold liquid from a pressure lower than the primary steam to a pressure higher than the primary steam. In this paper,the mechanical circulation pump was replaced with a gas-liquid injector. The effect of the evaporate temperature for the system was studied with the organic fluid R123. While this novel ORC can not only improves the energy utilization,but also be suitable for some occasions without power.