The combinatorial fusion cascade provides a surprisingly simple and complete explanation for the origin of the genetic code based on competing protocodes. Although its molecular basis is only beginning to be uncovered, it represents a natural pattern...
In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used t...
The rise of social networks has greatly contributed to creating information cascades. Overtime, new nodes are added to the cascade network, which means the cascade network is dynamically variable. At the same time, there are often only a few nodes in...
The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow c...
In response to the inaccurate positioning of traditional camera relocation methods in scenes with large-scale or severe viewpoint changes, this study proposes a camera relocation method based on a cascaded graph neural network to achieve accurate sce...
Face detection for security cameras monitoring large and crowded areas is very important for public safety. However, it is much more difficult than traditional face detection tasks. One reason is, in large areas like squares, stations and stadiums, f...
In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controll...
Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a singl...
Multi-scale vortex structures constitute the intrinsic skeleton of turbulent flows and govern the energy cascade process in oceanic turbulence. Elucidating their evolutionary dynamics is crucial for understanding turbulent mixing and transport. In th...
This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve ob...
A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and...
Unknown variables in the environment, such as wind disturbance during a flight, affect the accurate trajectory of multi-rotor UAVs. This study focuses on the intelligent supervisory neurocontrol of trajectory tracking for a nonplanar twelve-rotor UAV...
As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, includi...
Cross-modal image registration for unmanned aerial vehicle (UAV) platforms presents significant challenges due to large-scale deformations, distinct imaging mechanisms, and pronounced modality discrepancies. This paper proposes a novel multi-scale ca...
Design: At the heart of time series forecasting, if nonlinear and nonstationary data are analyzed using traditional time series, the results will be biased. At the same time, if just using machine learning without any consideration given to input fro...
Instance segmentation (IS) of remote sensing (RS) images can not only determine object location at the box-level but also provide instance masks at the pixel-level. It plays an important role in many fields, such as ocean monitoring, urban management...
Container shipping networks are vulnerable to cascading failures due to seaport disruptions, underscoring the need for resilient multimodal transport systems. This study proposes a cascading failure model for the seaport–dry-port network in con...
With the continuous progress of machine vision technology, crack detection in pipelines has been greatly improved. For crack detection in deep holes, inner tubes, and other environments, it is not only necessary to detect the existence of cracks, but...
Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series and location data. Methods: In the GSTAR model, there is an assumption that the research locations are...
Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning mo...
Table detection is a preliminary step in extracting reliable information from tables in scanned document images. We present CasTabDetectoRS, a novel end-to-end trainable table detection framework that operates on Cascade Mask R-CNN, including Recursi...
A 10 kV distribution network is a crucial piece of infrastructure to guarantee enterprises’ and households’ access to electricity. Stripping cables is one of many power grid maintenance procedures that are now quickly expanding. However,...
Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of arti...
Information cascades refer to a type of learning behavior in social networks where individuals make decisions by observing the actions of others, rather than relying solely on their own private information. Network centrality, which measures the rela...
To characterize delay propagation and dispatch feedback under sudden disturbances in railway networks, this study formulates a risk measurement and control framework integrating time-indexed graph attention (TI-GAT), cascade-failure dynamics, and dee...
Current research on the cascading failure of coupling networks is mostly based on hierarchical network models and is limited to a single relationship. In reality, many relationships exist in a network system, and these relationships collectively affe...
The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a...
The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial int...
With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire...
A cyber-physical supply network is composed of an undirected cyber supply network and a directed physical supply network. Such interdependence among firms increases efficiency but creates more vulnerabilities. The adverse effects of any failure can b...
The current analysis of cascading failures in command and control networks pays little attention to their roles and mechanisms, resulting in challenges in quantifying survivability evaluation metrics and limiting practical application. To address the...
To explore the ability of taxi transportation service capacity in unexpected conditions, based on the taxi GPS trajectory data, this paper presented a taxi transportation network and explored a cascading failure model with the non-linear function of...
Cascading failures are the significant cause of network breakdowns in a variety of complex infrastructure systems. Given such a system, uncovering the dependence of cascading failures on its underlying topology is essential but still not well explore...
The structural characteristics and robustness of subway networks are important for improving the safety and efficiency of subway operations. Based on complex network theory, this study analyzed the structural characteristics of the Hefei subway netwo...
A cascaded neural network framework is developed to solve the two-dimensional inverse scattering problem under transverse magnetic wave illumination. The framework employs a multi-stage deep learning architecture in which each sub-network iteratively...
25 December 2021
Analysis of the robustness and vulnerability of metro networks has great implications for public transport planning and emergency management, particularly considering passengers’ dynamic behaviors. This paper presents an improved coupled map la...
In the past few decades, extensive research has been conducted on the modeling of cascading failures and their recovery processes in freeway networks. In practice, the restoration of functionality and structure in complex networks that suffer large-s...
Most existing studies model interdependent networks as simple network systems consisting of two or more undirected subnets, and the interdependent edges between the networks are undirected. However, many real-world interdependent networks are coupled...
One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal...
In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement af...
As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data...
Enterprise performance in real-world markets is shaped by dynamic factors, including competitors, collaborators, and hidden associates. Existing models struggle to capture the interplay between time-varying network dynamics and financial asset price...
When studying an unfamiliar system, we first look for the symmetry that the system has so that we can make many predictions about the possible properties of the system. The symmetry in transport network security needs to maintain a stable state and m...
A reasonable definition of nodes load and capacity is essential for improving the robustness of scale-free networks against cascading failure, which has gained significant attention over recent years. This paper presents two methods for defining the...
The growing importance of critical infrastructure systems (CIS) makes maintaining their normal operation against deliberate attacks such as terrorism a significant challenge. Combining game theory and complex network theory provides a framework for a...
In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background n...
In the case of cascade failure, due to the close connection of the automobile supply chain network, the chain reaction caused by it should not be ignored; therefore, to find out the important nodes in the automobile supply chain network, to reduce th...
Amid intensifying extreme climate events, geopolitical conflicts, and sudden trade policy disruptions, the resilience and vulnerability of global staple food trade systems have emerged as pressing governance concerns. This study constructs directed w...
Urban bus networks are exposed to cascading disruptions whose propagation depends not only on network topology but also on temporally varying traffic states and passenger-flow distributions. Existing vulnerability analyses often rely on static topolo...
Graph convolutional networks (GCNs) have received widespread attention in skeleton-based human action recognition because of their remarkable advantages in both recognition accuracy and computational efficiency. However, most existing GCN-based metho...
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