Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network

Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced...

The Seven-League Scheme: Deep Learning for Large Time Step Monte Carlo Simulations of Stochastic Differential Equations

We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By employing an artificial neural network to learn these...

Adaptive time-delay estimation and control of optimized Stewart robot

Aiming at a more efficient and accurate performance of parallel manipulators in the existence of complex kinematics and dynamics, a robust generalizable methodology is proposed here for an integrated 6-DOF Stewart platform with rotary time-delayed actuators torque control. The suggested method employs a time-delay linear–quadratic integral...

A Generalized Transformer-Based Pulse Detection Algorithm

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse...

Smart Redundancy Schemes for ANNs against Fault Attacks

Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks....

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...

A Spatially Enhanced Data-Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two-step data-driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells...

Blijvend vlakke wegen effect van variaties in de ondergrond op de voorspelling van langsvlakheid

Dit rapport bevat de resultaten van het onderzoek dat door Deltares TNO is uitgevoerd in de periode 2007-2009 binnen het Delft Cluster onderzoek Blijvend Vlakke Wegen. Het onderzoek is gericht op het opzetten van een methodiek om variaties in de ondergrond te vertalen naar te verwachten restzettingsverschillen langs een wegtracé.

Stochasticity, nonlinearity and forecasting of streamflow processes

Abstract not available

Using artificial neural networks for the transformation of human body postures based on landmarks

Designers, engineers and ergonomists are seeking to exploit the opportunities offered by the 3D anthropometric technologies. These technologies make 3D measurements possible and provide us with a more detailed description of human body in comparison with the traditional 1D or 2D data processing. In many industrial design cases, there is a need...