Magnetic Leakage Fields as Indicators of Eddy Current Testing
With eddy current testing in a homogenous field it is often spoken about hindrances that are represented by cracks to eddy currents that cannot flow so as in non-defective material. Such homogenous condition can be met with the magnetizing in a very long coil. If however the coil is short the field is not homogenous any more and especially with testing of ferromagnetic materials the magnetic leakage field may not be neglected. The demagnetizing effect of eddy currents can be seen also in magnetic leakage field. By proper arrangement of primary and secondary coil it is possible to get more information. It is given in the article how the longitudinal and the transverse cracks can be detected in the proposed arrangement of primary and secondary coils. If the arrangement of coils with the eddy currents testing is not a standard one it is very important to understand the physics of the new arrangement to be able to interpret the results properly. A very useful tool is the mathematical treatment of field equations. The numerical methods are often applied, since Maxwell equations are rather complex partial differential equations and there are usually real and imaginary components of the field to be taken into account. There are main ideas given how to solve more complicated problems. The actual concrete results will be compared with practical experiments.
Magnetic leakage fields calculated by the method of finite differences
Abstract Is the signal indicating magnetic leakage fields proportional to the depth of the crack? This questions needs to be answered to show the effectiveness of the testing method. However, contradictory reports have appeared in the literature in the past few years. The results given in this paper obtained by the method of finite differences illustrate a new aspect of the problem throwing a new light on previous results obtained by the method of finite elements.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.