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排序方式: 共有88条查询结果,搜索用时 15 毫秒
1.
ABSTRACTThe RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n+-n-p-p+) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 1010 Wm?2 and the Si-based counterpart has lowest power density of order 106 Wm?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances. 相似文献
2.
Harukazu Yoshino Zeynel Bayindir Joydeep Roy Ben Shaw Heon-ick Ha Andrei Lebed Michael J. Naughton 《Journal of Low Temperature Physics》2006,142(3-4):323-326
A new TTP donor, Me-DH-TTP (2-methyl-5-(1,3-dithiolan-2-yliden)-1,3,4,6-tetrathiapentalene), was designed to realize a system
with large on-site Coulomb repulsion as compared with the previously known bis-fused type TTP donors. Probably as a consequence,
(Me-DH-TTP)2AsF6 exhibits semiconducting behavior from room temperature to liquid helium temperature. By increasing pressure, metallic behavior
appears below 300 K, and with distinct metal-insulator (M-I) transition up to 2.2 GPa. This M-I transition suddenly disappears
beyond 2.5 GPa, and metallic state is stabilized down to 2.6 K. We discuss the possibility of quantum critical point around
2.4 GPa. 相似文献
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In this research work, electrical conductivity of a novel cast 6351 Al–Al4SiC4 composite has been studied for varying Al4SiC4 particle content (2–7 vol%). Analytical models were developed on the basis of free electron theory and local resistance approach in order to predict electrical conductivity of metal matrix composites. The electrical conductivity of cast 6351 Al–Al4SiC4 composite was found to decrease with increasing Al4SiC4 content. Overprediction of results and more % deviation of the predicted electrical conductivity from experimental value were observed for local-resistance-approach-based model. However, the model developed on the basis of electron theory considering scattering of electrons at particle–matrix interface accurately predicted the experimental electrical conductivity data. 相似文献
6.
Phase Transformation of Metastable ZnSnO3 Upon Thermal Decomposition by In‐Situ Temperature‐Dependent Raman Spectroscopy
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Tanujjal Bora Muna H. Al‐Hinai Ashraf T. Al‐Hinai Joydeep Dutta 《Journal of the American Ceramic Society》2015,98(12):4044-4049
Temperature‐dependent in‐situ Raman spectroscopy is used to investigate the phase transformation of zinc metastannate (ZnSnO3) to zinc orthostannate (Zn2SnO4) induced upon annealing in the ambient. ZnSnO3 microcubes (MCs) were synthesized at room temperature using a simple aqueous synthesis process, followed by characterization using electron microscopy, X‐ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and thermogravimetric analysis (TGA). Annealing of the ZnSnO3 MCs was carried out up to 1000°C, while recording the Raman spectra in‐situ at regular intervals. Phase transformation from metastannate to orthostannate was found to begin around 500°C with an activation energy of ~0.965 eV followed by the recrystallization into the inverse spinel orthostannate phase at ~750°C. Results from this study provide detailed understanding of the phase transformation behavior of perovskite ZnSnO3 to inverse spinel Zn2SnO4 upon thermal annealing. 相似文献
7.
Chatterjee Saikat Li Donghui Leung Jackie Sengupta Joydeep Chattopadhyay Kinnor 《Metallurgical and Materials Transactions B》2017,48(2):1035-1044
Metallurgical and Materials Transactions B - Inert gas shrouding in tundish can result in the formation of a tundish open eye (TOE) due to the presence of reversed flows on the upper surface of the... 相似文献
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Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches
to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning
to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield
better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint
groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor
that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm.
This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems
involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification
of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies
obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies
that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential
of using such a modular learning approach for discovering domain knowledge automatically from data.
Received: 21 November 2000, Received in revised form: 02 November 2001, Accepted: 13 December 2001 相似文献
10.
The problem of obtaining a single “consensus” clustering solution from a multitude or ensemble of clusterings of a set of objects, has attracted much interest recently because of its numerous practical applications. While a wide variety of approaches including graph partitioning, maximum likelihood, genetic algorithms, and voting-merging have been proposed so far to solve this problem, virtually all of them work on hard partitionings, i.e., where an object is a member of exactly one cluster in any individual solution. However, many clustering algorithms such as fuzzy c-means naturally output soft partitionings of data, and forcibly hardening these partitions before applying a consensus method potentially involves loss of valuable information. In this article we propose several consensus algorithms that can be applied directly to soft clusterings. Experimental results over a variety of real-life datasets are also provided to show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data. 相似文献