Dislocation Loop Formation and Growth under In Situ Laser and/or Electron Irradiation
Vacancies and interstitial atoms are primary lattice (point) defects that cause observable microstructural changes, such as the formation of dislocation loops and voids in crystalline solids. These defects' diffusion properties determine the phase stability and environmental resistibility of macroscopic materials under ambient conditions. Although in situ methods have been proposed for measuring the diffusion energy of point defects, direct measurement has been limited. In this study, we propose an alternative in situ method to measure the activation energy for vacancy migration under laser irradiation using a pulsed laser beam from a laser-equipped high-voltage electron microscope (laser-HVEM). We made in situ observations that revealed the formation and growth of vacancy dislocation loops in an austenitic stainless steel during laser irradiation. These loops continued to grow when thermal annealing was performed after laser irradiation at the same temperature. We anticipate that laser-HVEM will provide a new method for investigating lattice defects.
Dynamic studies of defect mobility using high voltage electron microscopy
Abstract The large variety of observed defect-structure development by electron irradiation in fcc and bcc metals is classified from the view point of point-defect mobilities. Vacancy mobility is obtained from the defect structure change caused by the annihilation of vacancies accumulated by irradiation, and their motion activation energy is obtained from the interstitial cluster growth at high temperatures. Mobility of interstitial atoms and their interaction with impurities are obtained from the variation of interstitial cluster formation. Various observed effects of electron radiation induced motion of point defects are explained by the displacement of atoms by the transfer of energy comparable with the migration activation energy of each point defect. The observed superficial temperature dependence of the induced diffusion of vacancies in fcc metals is attributed to divacancies created by the induced diffusion. An efficient method to obtain the self-diffusion energies is proposed with results for some metals.
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.