Pointer meters are frequently applied to industrial production for they are directly readable. They should be calibrated regularly to ensure the precision of the readings. Currently the method of manual calibration is most frequently adopted to accomplish the verification of the pointer meter, and professional skills and subjective judgment may lead to big measurement errors and poor reliability and low efficiency, etc. In the past decades, with the development of computer technology, the skills of machine vision and digital image processing have been applied to recognize the reading of the dial instrument. In terms of the existing recognition methods, all the parameters of dial instruments are supposed to be the same, which is not the case in practice. In this work, recognition of pointer meter reading is regarded as an issue of pattern recognition. We obtain the features of a small area around the detected point, make those features as a pattern, divide those certified images based on Gradient Pyramid Algorithm, train a classifier with the support vector machine (SVM) and complete the pattern matching of the divided mages. Then we get the reading of the pointer meter precisely under the theory of dynamic three points make a line (DTPML), which eliminates the error caused by tiny differences of the panels. Eventually, the result of the experiment proves that the proposed method in this work is superior to state-of-the-art works.
Nongyro inertial measurement units (NGIMUs) use only microelectromechanical system or microaccelerometers replacing gyroscopes to compute the motion of a moving body. The angular information of NGIMU can be acquired from linear acceleration information. In this work, a new nine-accelerometer configuration of NGIMU is presented with a built experiment system. To improve the precision of the estimated angular rate, a compensation algorithm is used to get the correct accelerometer output. It is provided under the existence of the accelerometer mounting error, which seriously affects NGIMU performance. The simulation study of the proposed methods for estimating the angular rate is investigated based on the present NGIMU configuration. Additionally, the experiment verification using the two-axis turntable to measure the angular rate is also performed. The simulation and experiment results show the feasibility of the configuration and methods. In the experiment, the relative error of this nine-accelerometer configuration is around 1% when the measured angular rate ranges from 0 to 200 deg/s.
When a Non-gyro inertial measurement unit (NGIMU) is working, an inevitable static coupling error and dynamic
coupling error occur. The coupling error is defined as a situation that the output signal of a single-axis accelerometer
includes the additional value, which affected by the accelerometer input in other direction. Obviously, the coupling error
will decrease the system measurement precision seriously. Basing on a nine-accelerometer configuration of NGIMU and
the definition of the coupling error, a new static and dynamic united decoupling method is applied to NGIMU. The
method overcome the complexity which is aroused by using the static decoupling method and dynamic coupling method
respectively, and simplifies the following processing system. Finally, a simulation case for estimating the error of the
angular rate in three axes is investigated. The simulation results show that after the static and dynamic decoupling, the
navigation precision is improved effectively.
Non-gyro inertial measurement unit (NGIMU) uses only accelerometers replacing gyroscopes to compute the motion of a moving body. Use the traditional accelerometer configurations for reference, a novel nine-accelerometer configuration of NGIMU is proposed with its mathematic model constructed. This configuration can acquire the expressions of the angular accelerations directly and avoid calculating the differential poly-equation. To confirm the effectiveness of the design, the experiment system is set up and the DSP data processing circuit is also done. In addition, the experiments of the angle estimating are performed and the results show that the design can reflect the trend of the angle changing with high precision to some extend.
This paper proposes a method using exploring agent for the noise elimination and crack recognition in binary images which originate from the objective gray level images. A mean filtering method is introduced to correct non-uniform background illumination and obtain the dynamic thresholds, which are used to convert the original 255 scales gray level image into binary images. The pavement crack figures in the binary image have been contaminated by randomly distributed noisy dots, and in most cases, the crack shape and orientation can't be represented by specific functions. The exploring agent method using sense-compute-act loop, presented in this paper, can be employed to determine the crack and eliminate the random noise. The exploring agent and the Least Square Fit (LSF) method separately have unique characteristics in recognizing the crack intersection and orientation, and automatically running along the crack. The traces of the exploring agent are the skeleton of the pavement crack, and the number of steps can be used to calculate the length of the crack. The sense, compute, and act ability of the exploring agent iterate to guarantee the effect in processing randomly distributed features of image during the actual processing.