Thermal Analysis of Cable Sealing Ends
A general analytical method for thermal analysis of high-voltate oil-filled cable sealing ends is presented. Oil flow within the conductor oil duct, longitudinal heat flow through the conductor and conductor losses variation with temperature are taken into consideration. The method is successfully applied for a sealing end of a 300 kV, 2000 mm2 integral sheath cooled oil-filled cable. A companion paper where cable joints are analyzed, is referenced.
Computerized Thermal Analysis of High Voltage Bushings
Having a reliably operating electric power system requires the detailed knowledge of the electrical, mechanical, and thermal performances of the system components. The thermal performance of high voltage bushings should be devoid of hot spots during the full load and overload conditions. An analytical method is developed to predict the conductor temperature distribution inside a high voltage bushing. The measurements of temperataure is not required. The conductor losses and the dielectric losses have been included in the model. Self contained evaporative cooling method is suggested for increasing the loading of the bushing. The model is capable of predicting the conductor temrperature profile with and without cooling. The imodel is applied to an experinental bushing with and without cooling and good agreement is obtained with the measured temperatures.
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.