Electromagnetic Field Equation and Lorentz Gauge in Rindler Space-time

In this paper, we derived electromagnetic field transformations and electromagnetic field equations of Maxwell in Rindler space-time in the context of general theory of relativity. We then treat the Lorentz gauge transformation and the Lorentz gauge fixing condition in Rindler space-time and obtained the transformation of differential operation, the electromagnetic 4-vector potential and the field. In addition, charge density and the electric current density in Rindler spacetimeare derived. To view the invariance of the gauge transformation, gauge theory is applied to Maxwell equations in Rindler space-time. In Appendix A, we show that the electromagnetic wave function cannot exist in Rindler space-time. An important point we assert in this article is the uniqueness of the accelerated frame. It is because, in the accelerated frame, one can treat electromagnetic field equations.

A Deep Learning Based Level Set Model for Pancreas Segmentation

A deep learning based active contour framework is proposed for pancreas segmentation. Data extension and fractional differential operation are firstly applied for pre-processing. Second, deep learning method is designed to acquire the initial contour of pancreas. Subsequently, an intensity constrained term is designed to stop the contours at the edges. The intensity constrained term is integrated into a variational active contour model with three terms. The accurate pancreas segmentation is obtained by the evolution of the active contour model. Our approach reaches high detection dice similarity coefficient (DSC) of 83% and sensitivity of 85% in a dataset containing 40 abdominal CT scans. Comparisons with other level set models provide evidence that the proposed method offers desirable performances.

A Deep Learning Based Level Set Model for Pancreas Segmentation

A deep learning based active contour framework is proposed for pancreas segmentation. Data extension and fractional differential operation are firstly applied for pre-processing. Second, deep learning method is designed to acquire the initial contour of pancreas. Subsequently, an intensity constrained term is designed to stop the contours at the edges. The intensity constrained term is integrated into a variational active contour model with three terms. The accurate pancreas segmentation is obtained by the evolution of the active contour model. Our approach reaches high detection dice similarity coefficient (DSC) of 83% and sensitivity of 85% in a dataset containing 40 abdominal CT scans. Comparisons with other level set models provide evidence that the proposed method offers desirable performances.

A New Index of Static Actuation Force Sensitivity of Mechanisms Based on Unified Forward Jacobian Matrix

This paper proposes a novel performance index, which is called static actuation force sensitivity (SAFS), to investigate the response of the actuation forces when the amplitude of the suffered load of the end-effector has a change. Smaller SAFS can protect the actuations, and the load is mainly suffered by the structural constraints. This work starts with the construction of the unified forward Jacobian matrix of both serial and parallel mechanisms by screw theory. Then, with the forward Jacobian matrix, the inverse static equation is established. SAFS is thus introduced by the “partial differential” operation on the inverse static equation. SAFS is only related to the position of the whole mechanism and the direction of the suffered load, but not related to the detailed value of the amplitude of the load and the detailed value of the actuation forces; thus, SAFS can reveal the essence of static force capacities of the mechanisms. The example mechanism (namely, the 3revolute-prismatic-spherical (RPS) parallel mechanism) is used to illustrate the distribution of SAFS both over the workspace and at a certain pose. The analysis method of SAFS and the proposed index are expected to be applied to the pose optimization in the motion planning of the mechanisms to protect the actuations.

Algorithm for measuring fiber length distributions of raw cotton and combed wool using dual-beard image method

The dual-beard image method, which has been developed in recent years as a fast and economical method for fiber length measurement, consists of dual-beard specimen preparation, image processing, fibrogram extraction, and length parameter calculations. However, one of the shortcomings of this method is that it can only produce extremely limited length parameters such as mean length, coefficient of variation, modal length, and quality length (UHML, upper half mean length). This study introduces a new algorithm for converting the dual-beard fibrogram into a length distribution histogram which can be used to calculate most of the current length parameters. The algorithm is based on the short fiber content formulae but modified by theoretical analysis and experimental comparison. The length distributions of 24 cotton samples and 12 wool samples are measured by dual-beard image method with the new algorithm, and Advanced Fiber Information System (AFIS) and Almeter are employed for comparison. Comparative analysis shows that the peaks and ranges of the distribution histograms using the dual-beard method are similar to those from the reference methods, and the shapes of histograms from difference methods match well with one another. In addition, five length parameters calculated from the dual-beard distributions are verified to be consistent with those measured by AFIS and Almeter. The new algorithm employed in the dual-beard image method avoids the differential operation which amplifies the curve error, giving the dual-beard image method the ability to output more comprehensive length information.