Effect of UV treatment on the dielectric strength of BOPP capacitor film
The effect of UV treatment on dielectric breakdown strength of metallized biaxially-oriented polypropylene (BOPP) capacitor film was studied. Results from Weibull statistical analysis indicate that an optimal UV exposure level of ~3.4 J/cm2 (200-400 nm) increases the breakdown strength at 1% probability by ~20%. UV spectroscopic analysis indicates that the antioxidant in BOPP was consumed almost entirely. The results suggest that antioxidant remaining in the BOPP after processing may have an unfavorable effect on the breakdown strength of the film
ANTIOXIDANT ACTION IN IRRADIATED POLYPROPYLENE STUDIED BY ULTRAVIOLET SPECTROSCOPY
Abstract Ultraviolet spectrum of 0.2 mm thick film of polypropylene containing 0.5% ORGANOX 1010 showed that in the sample prepared by slow cooling about 15% of the antioxidant reacted during the preparation process. The difference in turbidity between the samples obtained in the slow and the fast cooling process is attributed to the degree of crystallinity, which is in agreement with the DSC data. Very pronounced effects of the oxygen concentration and the degree of crystallinity on antioxidant uptake in irradiated polypropylene films were observed and discussed. It was also shown that a Febetron 707 pulsed electron accelerator is capable of producing both the single pulse dose (50 kGy) and the dose rate (2.5 TGy/s) large enough to enable a comparison of dose rate effects and LET effects in the study of the antioxidant reactions in polypropylene.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.