We study the cascading failures in a system composed of two interdependent square lattice networks $A$ and $B$ placed on same Cartesian plane, where each node network depends randomly chosen within certain distance $r$ from corresponding vice versa. Our results suggest that percolation for small below ${r}_{\mathrm{max}}\ensuremath{\approx}8$ (lattice units) is second-order transition, larger first-order transition. For $r<{r}_{\mathrm{max}}$, critical threshold increases linearly with 0.593...

Despite the remarkable progresses made in deep learning based depth map super-resolution (DSR), how to tackle real-world degradation low-resolution (LR) maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which very different from what would get real sensor. In this paper, we argue that models under setting are restrictive not effective dealing with realworld tasks. We make two contributions tackling of sensors. First, propose classify...

Series arc is prone to cause fire accidents, but its occurrences induced by different load types and connections make the detection challengeable. This paper proposes a series fault location algorithm for multi-load circuit topology, especially branch faults nonlinear power loads. Several typical loads of paralleling connected are considered measure current changes caused arcing phenomenon at positions. Different aspects features extracted time-domain, frequency-domain, wavelet packet energy...

In this paper, we study the problem of structural graph clustering, a fundamental in managing and analyzing data. Given large G = (V, E), clustering is to assign vertices V clusters identify sets hub outlier as well, such that same cluster are densely connected each other while different loosely other. Firstly, prove existing SCAN approach worst-case optimal. Nevertheless, it still not scalable graphs due exhaustively computing similarity for every pair adjacent vertices. Secondly, make...

We present a novel method for multi-view depth estimation from single video, which is critical task in various applications, such as perception, reconstruction and robot navigation. Although previous learning-based methods have demonstrated compelling results, most works estimate maps of individual video frames independently, without taking into consideration the strong geometric temporal coherence among frames. Moreover, current state-of-the-art (SOTA) models mostly adopt fully 3D...

Proposing a fast detection algorithm for urban road traffic congestion based on image processing technology. Firstly, to speed up the and freely select interesting area, human-computer interaction vehicle area was put forward. Then, by using difference of texture features between unobstructed image, proposing density estimation analysis. Through grayscale relegation, gray level co-occurrence matrix calculation feature extraction, energy entropy that could reflect were obtained from area....

We study the problem of structural graph clustering, a fundamental in managing and analyzing data. Given an undirected unweighted graph, clustering is to assign vertices clusters, identify sets hub outlier as well, such that same cluster are densely connected each other while different clusters loosely connected. In this paper, we develop new two-step paradigm for scalable based on our three observations. Then, present pSCAN approach, within paradigm, aiming reduce number similarity...

We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis. Despite NeRF's extraordinary success in the neural rendering field, it suffers from blur high because its inherent multilayer perceptron struggles learn frequency details incurs a computational explosion as increases. Therefore, we propose RefSR-NeRF, an end-to-end framework first learns low representation, then reconstructs with help...

In this paper, a vision-guided autonomous quadrotor in an air-ground multi-robot system has been proposed. This is equipped with monocular camera, IMUs and flight computer, which enables flights. Two complementary pose/motion estimation methods, respectively marker-based optical-flow-based, are developed by considering different altitudes flight. To achieve smooth take-off, stable tracking safe landing respect to moving ground robot desired trajectories, appropriate controllers designed....

Some stereo matching algorithms based on deep learning have been proposed and achieved state-of-the-art performances since some public large-scale datasets were put online. However, the disparity in smooth regions detailed is still difficult to accurately estimate simultaneously. This paper proposes a novel method called WaveletStereo, which learns wavelet coefficients of rather than itself. The WaveletStereo consists several sub-modules, where low-frequency sub-module generates...

Optimizing highway alignments is a very complex engineering problem. None of existing methods has totally solved the problem In this paper we build an optimization model for 3D alignment based on two-stage dynamic programming, in which comprehensive cost and design constraints are embedded. first stage: formulate as network by establishing three-dimensional grid representation study area. Then programming used to find best corridor alignment. order obtain group scenarios, propose...

Community partition is of great importance in sociology, biology and computer science. Due to the exponentially increasing amount social network applications, a fast accurate method necessary for community networks. In view this, we investigate problem from perspective influence propagation, which one most important features communication. We formulate as combinatorial optimization that aims at partitioning into K disjoint communities such sum propagation within each maximized. When K=2...

The pose estimation of the aircraft in taxiing or parking state on airport surface has been proved helpful for level control and command, operation efficiency, capacity handling special situations airports. However, current methods cannot provide satisfied estimate results aircrafts because they regard as a point. To solve problem aircrafts, especially small-sized ones, this paper proposes an method based contour features. First, features are utilized to design two-dimensional skeleton show...

Due to the existence of information overload in social networks, it becomes increasingly difficult for users find useful according their interests. This paper takes Twitter-like networks into account and proposes models characterize process diffusion under overload. Users are classified different types in-degrees out-degrees, user behaviors generalized two categories: generating forwarding. View scope is introduced model information-processing capability overload, average number times a...

Abstract In the aerospace industry, accurately predicting remaining useful life (RUL) of aircraft engines is critical to reduce maintenance costs and increase safety. Existing RUL prediction algorithms fail account for global temporal factors, overlook non-stationary nature monitored data, neglect trends seasonal characteristics. These factors directly affect sensitivity forecast model changes in system state. light this, this study introduces an innovative end-to-end deep learning model,...

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing whole community decomposition after each modification of network, ALPA takes into account information historical updates its solution according network modifications via a local process, which generally affects only small portion network. This makes it respond changes at low computational cost. The effectiveness has been tested on...