Effect of Arc-anode Attachment on Thermal Plasma Jet
An interaction of plasma flow with an arc-anode attachment in a dc arc plasma torch was investigated. Statistical processing of the photographic images of plasma jet was used for analysis of an effect of the anode attachment on structure and stability of plasma flow. Deflection, flattening and flutter of the plasma jet was found to be caused by disturbing effect of the arc-anode attachment.
Properties of Hybrid Water/Gas DC Arc Plasma Torch
A new type of plasma torch with combined stabilization of electric arc by water vortex and gas flow was investigated. This hybrid water/gas stabilization offers the possibility of adjusting plasma jet parameters within a wide range from high-enthalpy low-density plasmas typical for liquid stabilized torches to lower enthalpy higher density plasmas generated in gas stabilized torches. The torch was operated at an arc power from 22 to 130 kW with an exit centerline plasma velocity from 2 to 6.5 km/s and a plasma temperature from 14 000 K to 22 000 K. Moreover, gas flow in the cathode part protects a cathode tip and thus a consumable carbon cathode used in water torches could be replaced by a fixed tungsten cathode. The characteristics of the electric arc with combined gas/water stabilization were measured and the effect of gas properties and flow rate on plasma properties and gas-dynamic flow characteristics of the plasma jet were studied for argon, and mixtures of argon with hydrogen and nitrogen
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.