Position-Dependent Snap Feedforward: A Gaussian Process Framework

Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e., the compliance, should be compensated for by means of snap feedforward to achieve high accuracy. Position-dependent compliance, which often occurs in motion systems, requires the snap feedforward...

Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach

Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in...

Development and testing of a prototype of a dental extraction trainer with real-time feedback on forces, torques, and angular velocity

The need for a training modality for tooth extraction procedures is increasing, as dental students do not feel properly trained. In this study, a prototype of a training setup is designed, in which extraction procedures can be performed on jaw models and cadaveric jaws. The prototype was designed in a way that it can give real-time feedback on...

Towards Unsupervised Rehabilitation: Development of a Portable Compliant Device for Sensorimotor Hand Rehabilitation

Sensorimotor impairments of the hand after stroke can drastically reduce the ability to perform activities of daily living. Recently, there has been an increased interest in minimally supervised and unsupervised rehabilitation to increase therapy dosage and to complement conventional therapy. Several devices have been developed that are...

A Two-Stage Bayesian optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation

This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the average sum of the states’...

R3L: Connecting Deep Reinforcement Learning To Recurrent Neural Networks For Image Denoising Via Residual Recovery

State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture...

Spreadsheet testing in practice

Despite being popular end-user tools, spreadsheets suffer from the vulnerability of error-proneness. In software engineering, testing has been proposed as a way to address errors. It is important therefore to know whether spreadsheet users also test, or how do they test and to what extent, especially since most spreadsheet users do not have...

Preparing to be unprepared: Training for resilience

Training methods for operators working under high pressure and in dynamic, unpredictable settings could benefit from a focus on resilience. In such settings, formal training often focuses on procedural conformity to train for particular scenarios, but resilient performance taps into a wider experience base and often more tacit skills. In this...

A 41 μW real-time adaptive neural spike classifier

Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator...

Improved deep reinforcement learning for robotics through distribution-based experience retention

Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform distribution. Through experiments on a...

Deep convolutional neural networks for detection of rail surface defects

In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated...