DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...

Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic...

Designing Recurrent and Graph Neural Networks to Predict Airport and Air Traffic Network Delays

In this paper, we propose open machine learning models that can provide airport delay predictions in a network with an error of around or less than five minutes. Due to the complexity of different components of air traffic networks, traditional flight performance model-based predictions fall short when dealing with numerous flights and often are...

Neural network model for predicting variation in walking dynamics of pedestrians in social groups

Pedestrian spaces are increasingly becoming popular locations for shopping, recreation, festivities, and other social activities. Therefore, an improved understanding of the factors that make walking environments enjoyable and safe is essential. Most existing studies focus on modelling walking behaviours of individual pedestrians. However,...

Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning

Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic...

Intelligent adaptive optimal control using incremental model-based global dual heuristic programming subject to partial observability

The scarcity of information regarding dynamics and full-state feedback increases the demand for a model-free control technique that can cope with partial observability. To deal with the absence of prior knowledge of system dynamics and perfect measurements, this paper develops a novel intelligent control scheme by combining global dual...

Incremental model-based global dual heuristic programming with explicit analytical calculations applied to flight control

A novel adaptive dynamic programming method, called incremental model-based global dual heuristic programming, is proposed to generate a self-learning adaptive flight controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online local dynamics identification, instead of the...

Design exploration of quantitative performance and geometry typology for indoor arena based on self-organizing map and multi-layered perceptron neural network

During the early design process, simulations allow numeric assessment and 3D models allow visual inspection for qualitative criteria. However, exploring different design alternatives based on both is challenging. To support the design exploration of quantitative performance and geometry typology of various design alternatives during the early...

Automatic keypoint detecting of wireframe gates

This work applies keypoint detection method to solve gate recognition problem. Unlike regular object detection task, gate recognition problem is made difficult by the fact that gate is empty wireframe which means that the object surrounded by gate-edge is not relevant and should not be taken into consideration when detecting. However, regular...

Incremental Model-Based Global Dual Heuristic Programming for Flight Control

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic Programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial...

Recurrent knowledge graph embedding for effective recommendation

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities...