31,451 research outputs found
Industry-level Total-factor Energy Efficiency in Developed Countries
This study computes and analyzes the total-factor energy efficiency (TFEE) of 11 industries in 14 developed countries during the period of 1995-2005 using the data envelopment analysis (DEA) approach. There are four inputs: labor, capital stock, intermediate inputs other than energy, and energy. The value added is the only output. The most inefficient industry is the metal industry, which has an average TFEE of 40.6%. Australia is the most inefficient country, with the lowest weighted TFEE in every year except for 1996 and 1998. The most efficient countries are the United States from 1995 to 1998, Denmark from 1999 to 2002, and Netherlands from 2003 to 2005. Given that the number of efficient industries decreases over time, it is clear that most industries have room to improve their energy efficiency as time goes by. Moreover, based on the total-factor framework, this study finds no support for the convergence of energy efficiency levels.Data envelopment analysis (DEA); Total-factor energy efficiency; Industry-level analysis
Representation Learning with Fine-grained Patterns
With the development of computational power and techniques for data
collection, deep learning demonstrates a superior performance over most of
existing algorithms on benchmark data sets. Many efforts have been devoted to
studying the mechanism of deep learning. One important observation is that deep
learning can learn the discriminative patterns from raw materials directly in a
task-dependent manner. Therefore, the representations obtained by deep learning
outperform hand-crafted features significantly. However, those patterns are
often learned from super-class labels due to a limited availability of
fine-grained labels, while fine-grained patterns are desired in many real-world
applications such as visual search in online shopping. To mitigate the
challenge, we propose an algorithm to learn the fine-grained patterns
sufficiently when only super-class labels are available. The effectiveness of
our method can be guaranteed with the theoretical analysis. Extensive
experiments on real-world data sets demonstrate that the proposed method can
significantly improve the performance on target tasks corresponding to
fine-grained classes, when only super-class information is available for
training
Design method for quasi-isotropic transformation materials based on inverse Laplace's equation with sliding boundaries
The deformation method of transformation optics has been demonstrated to be a
useful tool, especially in designing arbitrary and nonsingular transformation
materials. Recently, there are emerging demands for isotropic material
parameters, arising from the broadband requirement of the designed devices. In
this work, the deformation method is further developed to design
quasi-isotropic/isotropic transformation materials. The variational functional
of the inverse Laplace's equation is investigated and found to involve the
smooth and quasi-conformal nature of coordinate transformation. Together with
the sliding boundary conditions, the inverse Laplace's equation can be utilized
to give transformations which are conformal or quasi-conformal, depending on
functionalities of interest. Examples of designing an arbitrary carpet cloak
and a waveguide with arbitrary cross sections are given to validate the
proposed idea. Compared with other quasi-conformal methods based on grid
generation tools, the proposed method unifies the design and validation of
transformation devices, and thus is much convenient.Comment: 8 pages, 4 figure
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