Research on image processing algorithm of immune colloidal gold test paper detection

In order to better solve the problem of automatic identification of quality control line and detection line in the detection of gold standard test strip, this paper proposes to collect the image information of gold standard test strip after color rendering through CMOS sensor, preprocess the obtained information, transform RGB image into gray image, build cloud model in the CIELAB/HSV/HSL space, and apply the improved AdaBoost algorithm to determine the position of detection line and quality control line Place. Compared with the traditional template matching method, it improves the accuracy and accuracy of recognition

A sample-driven classification and identification method with KPCA and multi-SVM

It is difficult to develop an accurate fault diagnosis model for aero-engines due to their complex structure. In present paper, a new method for classifying and identifying fault modes according to the effective information extracted from history fault samples was proposed. This method includes three steps: firstly, find out independent source vibration signals through diagnosing and separating vibration signals. Secondly, find out the features that have great contribution to fault analysis using kernel principal component analysis (KPCA), and extract the eigenvector that is sensitive to status. Finally, classify the eigenvectors characterizing fault nature using support vector machine (SVM), meanwhile, select and analyze the parameters affecting classification effect. Two typical fault samples of aero-engine were used for verifying the feasibility of this method. Results show that for fault classification, this method has high identification accuracy, fast diagnosis speed, and is applicable to solving the classification and identification of small and nonlinear faults

Likelihood ratio tests for covariance matrices of high-dimensional normal distribution

a b s t r a c t For a random sample of size n obtained from a p-variate normal population, the likelihood ratio test (LRT) for the covariance matrix equal to a given matrix is considered. By using the Selberg integral, we prove that the LRT statistic converges to a normal distribution under the assumption p=n-y 2 ð0; 1. The result for y¼ 1 is much different from the case for y 2 ð0; 1Þ. Another test is studied: given two sets of random observations of sample size n 1 and n 2 from two p-variate normal distributions, we study the LRT for testing the two normal distributions having equal covariance matrices. It is shown through a corollary of the Selberg integral that the LRT statistic has an asymptotic normal distribution under the assumption p=n 1 -y 1 2 ð0; 1 and p=n 2 -y 2 2 ð0; 1. The case for maxfy 1 ,y 2 g ¼ 1 is much different from the case max fy 1 ,y 2 g o 1

Research on curve smoothing algorithm for diesel indicator diagram

The algorithm of curve smoothing for diesel indicator diagram is introduced, and the concrete implementation process is expounded in detail. First, the multi cyclic mean value operation of indicator diagram data is performed. By probable error of calculating indicator diagram data, if probable error is greater than a specific value, and the predicted value is used instead of the singular point data. The average operation of the indicator diagram data is processed again. Finally, the five-point cubic smoothing method is used to smooth the indicator curve, and the smooth, continuous and gradual indicator diagram curve is obtained. The experiments show that the algorithm described in the paper can effectively filter the noise of cylinder pressure signal, and obtain a smooth indicator diagram curve, which verifies the effectiveness of the algorithm described in the paper

Vision-force-fused curriculum learning for robotic contact-rich assembly tasks

Contact-rich robotic manipulation tasks such as assembly are widely studied due to their close relevance with social and manufacturing industries. Although the task is highly related to vision and force, current methods lack a unified mechanism to effectively fuse the two sensors. We consider coordinating multimodality from perception to control and propose a vision-force curriculum policy learning scheme to effectively fuse the features and generate policy. Experiments in simulations indicate the priorities of our method, which could insert pegs with 0.1 mm clearance. Furthermore, the system is generalizable to various initial configurations and unseen shapes, and it can be robustly transferred from simulation to reality without fine-tuning, showing the effectiveness and generalization of our proposed method. The experiment videos and code will be available at https://sites.google.com/view/vf-assembly

Tunable topological phase transition in soft Rayleigh beam system with imperfect interfaces

Acoustic metamaterials, particularly the topological insulators, exhibit exceptional wave characteristics that have sparked considerable research interest. The study of imperfect interfaces affect is of significant importance for the modeling of wave propagation behavior in topological insulators. This paper models a soft Rayleigh beam system with imperfect interfaces, and investigates its topological phase transition process tuned by mechanical loadings. The model reveals that the topological phase transition process can be observed by modifying the distance between imperfect interfaces in the system. When a uniaxial stretch is applied, the topological phase transition points for longitudinal waves decrease within a limited frequency range, while they increase within a larger frequency scope for transverse waves. Enhancing the rigidity of the imperfect interfaces also enables shifting of the topological phase transition point within a broader frequency range for longitudinal waves and a confined range for transverse waves. The transition of topologically protected interface modes in the transmission performance of a twenty-cell system is verified, which include altering frequencies, switching from interface mode to edge mode. Overall, this study provides a new approach and guideline for controlling topological phase transition in composite and soft phononic crystal systems.Comment: 39 pages,8 figure

Central Limit Theorems for Classical Likelihood Ratio Tests for High-Dimensional Normal Distributions

For random samples of size n obtained from p-variate normal distributions, we consider the classical likelihood ratio tests (LRT) for their means and covariance matrices in the high-dimensional setting. These test statistics have been extensively studied in multivariate analysis and their limiting distributions under the null hypothesis were proved to be chi-square distributions as n goes to infinity and p remains fixed. In this paper, we consider the high-dimensional case where both p and n go to infinity with p/n → y ∈ (0, 1]. We prove that the likelihood ratio test statistics under this assumption will converge in distribution to normal distributions with explicit means and variances. We perform the simulation study to show that the likelihood ratio tests using our central limit theorems outperform those using the traditional chi-square approximations for analyzing high-dimensional data