In this paper, based on the Lame function and Jacobi emptic function, the perturbation method is appliedto some nonlinear evolution equations to derive their multi-order solutions.
Lameness detection is a world-wide challenge to far-mers and veterinarians. Traditionally, one uses visual observation to make judgment on a cow's lameness or soundness. Visual observation heavily depends on the o...Lameness detection is a world-wide challenge to far-mers and veterinarians. Traditionally, one uses visual observation to make judgment on a cow's lameness or soundness. Visual observation heavily depends on the observer's experience, hence is subjective or ob-server-dependent. And even worse, it is inconsistent. It's reported that the agreement between veterinarians can be as low as 45% [1]. It is necessary and urgent to develop an objective detection method that can automatically detect lameness when it occurs. In this paper, we describe how statistical models can be used to develop such methods and how well the statistical models perform.展开更多
In this paper, we discuss the quadrilateral, finite element approximation to the two-dimensional linear elasticity problem associated with a homogeneous isotropic elastic material. The optimal convergence of the finit...In this paper, we discuss the quadrilateral, finite element approximation to the two-dimensional linear elasticity problem associated with a homogeneous isotropic elastic material. The optimal convergence of the finite element method is proved for both the L-2-norm and energy-norm, and in particular, the convergence is uniform with respect to the Lame constant lambda. Also the performance of the scheme does not deteriorate as the material becomes nearly incompressible, Numerical experiments are given which are consistent with our theory.展开更多
This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divi...This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.展开更多
基金supported by the National Natural Science Foundation of the People’s Republic of China“The research of finite element methods for eigenvalue problems in inverse scattering”(12261024)。
文摘In this paper, based on the Lame function and Jacobi emptic function, the perturbation method is appliedto some nonlinear evolution equations to derive their multi-order solutions.
文摘Lameness detection is a world-wide challenge to far-mers and veterinarians. Traditionally, one uses visual observation to make judgment on a cow's lameness or soundness. Visual observation heavily depends on the observer's experience, hence is subjective or ob-server-dependent. And even worse, it is inconsistent. It's reported that the agreement between veterinarians can be as low as 45% [1]. It is necessary and urgent to develop an objective detection method that can automatically detect lameness when it occurs. In this paper, we describe how statistical models can be used to develop such methods and how well the statistical models perform.
文摘In this paper, we discuss the quadrilateral, finite element approximation to the two-dimensional linear elasticity problem associated with a homogeneous isotropic elastic material. The optimal convergence of the finite element method is proved for both the L-2-norm and energy-norm, and in particular, the convergence is uniform with respect to the Lame constant lambda. Also the performance of the scheme does not deteriorate as the material becomes nearly incompressible, Numerical experiments are given which are consistent with our theory.
基金This work was supported by Shanxi Province Basic Research Program(Free Exploration)Project(No:202103021224149)Shanxi Province Postgraduate Education Teaching Reform Project(2021YJJG087)Shanxi Province Educational Science“14th Five-Year Plan”Education Evaluation Special Project(PJ-21001)funded.
文摘This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.
