426 research outputs found
Recognizing License Plates in Real-Time
License plate detection and recognition (LPDR) is of growing importance for
enabling intelligent transportation and ensuring the security and safety of the
cities. However, LPDR faces a big challenge in a practical environment. The
license plates can have extremely diverse sizes, fonts and colors, and the
plate images are usually of poor quality caused by skewed capturing angles,
uneven lighting, occlusion, and blurring. In applications such as surveillance,
it often requires fast processing. To enable real-time and accurate license
plate recognition, in this work, we propose a set of techniques: 1) a contour
reconstruction method along with edge-detection to quickly detect the candidate
plates; 2) a simple zero-one-alternation scheme to effectively remove the fake
top and bottom borders around plates to facilitate more accurate segmentation
of characters on plates; 3) a set of techniques to augment the training data,
incorporate SIFT features into the CNN network, and exploit transfer learning
to obtain the initial parameters for more effective training; and 4) a
two-phase verification procedure to determine the correct plate at low cost, a
statistical filtering in the plate detection stage to quickly remove unwanted
candidates, and the accurate CR results after the CR process to perform further
plate verification without additional processing. We implement a complete LPDR
system based on our algorithms. The experimental results demonstrate that our
system can accurately recognize license plate in real-time. Additionally, it
works robustly under various levels of illumination and noise, and in the
presence of car movement. Compared to peer schemes, our system is not only
among the most accurate ones but is also the fastest, and can be easily applied
to other scenarios.Comment: License Plate Detection and Recognition, Computer Vision, Supervised
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Outstanding supercapacitive properties of Mn-doped TiO2 micro/nanostructure porous film prepared by anodization method.
Mn-doped TiO2 micro/nanostructure porous film was prepared by anodizing a Ti-Mn alloy. The film annealed at 300 °C yields the highest areal capacitance of 1451.3 mF/cm(2) at a current density of 3 mA/cm(2) when used as a high-performance supercapacitor electrode. Areal capacitance retention is 63.7% when the current density increases from 3 to 20 mA/cm(2), and the capacitance retention is 88.1% after 5,000 cycles. The superior areal capacitance of the porous film is derived from the brush-like metal substrate, which could greatly increase the contact area, improve the charge transport ability at the oxide layer/metal substrate interface, and thereby significantly enhance the electrochemical activities toward high performance energy storage. Additionally, the effects of manganese content and specific surface area of the porous film on the supercapacitive performance were also investigated in this work
Finite Element Analysis and Lightweight Optimization Design on Main Frame Structure of Large Electrostatic Precipitator
The geometric modeling and finite element modeling of the whole structure of an electrostatic precipitator and its main components consisting of top beam, column, bottom beam, and bracket were finished. The strength calculation was completed. As a result, the design of the whole structure of the electrostatic precipitator and the main components were reasonable, the structure was in a balance state, its working condition was safe and reliable, its stress variation was even, and the stress distribution was regular. The maximum von Mises stress of the whole structure is 20.14 MPa. The safety factor was large, resulting in a waste of material. An optimization mathematical model is established. Using the ANSYS first-order method, the dimension parameters of the main frame structure of the electrostatic precipitator were optimized. After optimization, more reasonable structural design parameters were obtained. The model weight is 72,344.11 kg, the optimal weight is 49,239.35 kg, and the revised weight is 53,645.68 kg. Compared with the model weight, the optimal weight decreased by 23,104.76 kg and the objective function decreased by 31.94%, while the revised weight decreased by 18,698.43 kg and the objective function decreased by 25.84%
The algebraic geometry of perfect and sequential equilibrium: an extension
We extend the generic equivalence result of Blume and Zame (Econometrica 62: 783-794, 1994) to a broader context of perfectly and sequentially rational strategic behavior (including equilibrium and nonequilibrium behavior) through a unifying solution concept of "mutually acceptable course of action" (MACA) proposed by Greenberg et al. (2009). As a by-product, we show, in the affirmative, Dekel et al.'s (1999) conjecture on the generic equivalence between the sequential and perfect versions of rationalizable self-confirming equilibrium. JEL Classification: C70, C7
Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild
Multi-view clustering (MVC) can explore common semantics from unsupervised
views generated by different sources, and thus has been extensively used in
applications of practical computer vision. Due to the spatio-temporal
asynchronism, multi-view data often suffer from view missing and are unaligned
in real-world applications, which makes it difficult to learn consistent
representations. To address the above issues, this work proposes a deep MVC
framework where data recovery and alignment are fused in a hierarchically
consistent way to maximize the mutual information among different views and
ensure the consistency of their latent spaces. More specifically, we first
leverage dual prediction to fill in missing views while achieving the
instance-level alignment, and then take the contrastive reconstruction to
achieve the class-level alignment. To the best of our knowledge, this could be
the first successful attempt to handle the missing and unaligned data problem
separately with different learning paradigms. Extensive experiments on public
datasets demonstrate that our method significantly outperforms state-of-the-art
methods on multi-view clustering even in the cases of view missing and
unalignment
Low Complexity SLP: An Inversion-Free, Parallelizable ADMM Approach
We propose a parallel constructive interference (CI)-based symbol-level
precoding (SLP) approach for massive connectivity in the downlink of multiuser
multiple-input single-output (MU-MISO) systems, with only local channel state
information (CSI) used at each processor unit and limited information exchange
between processor units. By reformulating the power minimization (PM) SLP
problem and exploiting the separability of the corresponding reformulation, the
original problem is decomposed into several parallel subproblems via the ADMM
framework with closed-form solutions, leading to a substantial reduction in
computational complexity. The sufficient condition for guaranteeing the
convergence of the proposed approach is derived, based on which an adaptive
parameter tuning strategy is proposed to accelerate the convergence rate. To
avoid the large-dimension matrix inverse operation, an efficient algorithm is
proposed by employing the standard proximal term and by leveraging the singular
value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted
to fully eliminate the matrix inversion, and a parallel inverse-free SLP
(PIF-SLP) algorithm is finally obtained. Numerical results validate our
derivations above, and demonstrate that the proposed PIF-SLP algorithm can
significantly reduce the computational complexity compared to the
state-of-the-arts
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