Search results for " Artificial intelligence"

Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals

2020

Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…

Towards a simulation-based tuning of motion cueing algorithms

2016

Abstract This paper deals with the problem of finding the best values for the parameters of Motion Cueing Algorithms (MCA). MCA are responsible for controlling the movements of robotic motion platforms used to generate the gravito-inertial cues of vehicle simulators. The values of their multiple parameters, or coefficients, are hard to establish and they dramatically change the behaviour of MCA. The problem has been traditionally addressed in a subjective, partially non-systematic, iterative, time-consuming way, by seeking pilot/driver feedback on the generated motion cues. The aim of this paper is to introduce a different approach to solve the problem of MCA tuning, by making use of a simu…

Digital Signal Processing with Kernel Methods

2018

Adaptive backstepping control of uncertain systems in the presence of unmodeled dynamics and time-varying delays

2016

In this paper, the problem of adaptive backstepping control for uncertain systems in the presence of unmodeled dynamics and input time-varying delays is studied. Under some mild assumptions, a robust adaptive controller is designed such that the system is globally stabilized by using adaptive backstepping technique. Meanwhile, the transient system performance in L2 and norms of system output can be adjusted by choosing the design parameters. Finally, a simulation example is given to show the effectiveness of the results.

Using Two-Level Context-Based Predictors for Assembly Assistance in Smart Factories

2020

The paper presents some preliminary results in engineering a context-aware assistive system for manual assembly tasks. It employs context-based predictors to suggest the next steps during the manufacturing process and is based on data collected from experiments with trainees in assembling a tablet. We were interested in finding correlations between the characteristics of the workers and the way they prefer to assemble the tablet. A certain predictor is then trained with correct assembly styles extracted from the collected data and assessed against the whole dataset. Thus, we found the predictor that best matches the assembly preferences.

Modified F-transform Based on B-splines

2018

The aim of this paper is to improve the F-transform technique based on B-splines. A modification of the F-transform of higher degree with respect to fuzzy partitions based on B-splines is done to extend the good approximation properties from the interval where the Ruspini condition is fulfilled to the whole interval under consideration. The effect of the proposed modification is characterized theoretically and illustrated numerically.

Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

2017

Abstract The paper described how the results of measurements of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plant (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods, namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF + SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

A Novel Intelligent Technique of Invariant Statistical Embedding and Averaging via Pivotal Quantities for Optimization or Improvement of Statistical …

2020

In the present paper, for intelligent constructing efficient (optimal, uniformly non-dominated, unbiased, improved) statistical decisions under parametric uncertainty, a new technique of invariant embedding of sample statistics in a decision criterion and averaging this criterion over pivots’ probability distributions is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, the technique of invariant statistical embedding and averaging via pivotal quantities (ISE&APQ) is independent of the choice of priors and represents a novelty i…

LMI-based 2D-3D Registration: from Uncalibrated Images to Euclidean Scene

2015

International audience; This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates , and two or more uncalibrated cameras. An unknown subset of the scanned points have their image projections detected and matched across images. The proposed approach assumes the cameras only known in some arbitrary projective frame and no calibration or autocalibration is required. The devised solution is based on a Linear Matrix Inequality (LMI) framework that allows simultaneously estimating the projective transformation relating the cameras to the scene and establishing 2D-3D correspondences without triangulating image points. The proposed LMI framewo…

Improving stock index forecasts by using a new weighted fuzzy-trend time series method

2017

Define a new technical indicator for measuring the trend of the fuzzy time series.Introduce a new weighted fuzzy-trend time series method to forecast stock indices.Compare ex-post performances of weighted FTS methods using stock market indices.Assess statistical significance of ex-post forecast accuracy for weighted FTS methods. We propose using new weighted operators in fuzzy time series to forecast the future performance of stock market indices. Based on the chronological sequence of weights associated with the original fuzzy logical relationships, we define both chronological-order and trend-order weights, and incorporate our proposals for the ex-post forecast into the classical modeling…