Enhanced Sliding Mode Wheel Slip Controller for Heavy Goods Vehicles

This paper introduces an improved version of a sliding mode slip controller for pneumatic brake system ofheavy goods vehicles, HGVs. Using the Fast Actuating Brake Valve, FABV, allows to adopt advance control approaches forwheel-slip controllers which provide features such as fast dynamic response, stability and robustness. In this paper, the slidingmode algorithm which was developed for the speed dependent wheel slip control using the FABV hardware is analysed andimproved. The asymptotic convergence properties of the control algorithm are proven using Lyapunov stability theory and therobustness of the method is investigate

Determination of anisotropic thermal conductivity of VIP laminate using transient plane source method

The use of super insulation materials, such as vacuum insulation panels (VIP), is expected toincrease in buildings in the future. One key aspect for successful implementation is the qualitycontrol. At the factory, the thermal performance can easily be controlled by measuring theinternal pressure of the VIP. Preferably, the performance should be controlled again at theconstruction site before installation. The thermal conductivity of a VIP is possible to measureby using the transient plane source method (TPS). This method uses a sensor which measurethe temperature increase during a heat pulse. For the analysis of the measurement, informationon the thermal conductivity of the metalized multi-layer polymer laminate, used around the VIP,is needed. This paper presents the results obtained from different measurement setups of thelaminate. The aim of the study is to identify a practical approach to analyse the results, and togive recommendations on the best measurement setup. Two measurement submodules wereused; ‘anisotropic’ and ‘thin film’. The thermal conductivity of the laminate was measured inplaneand perpendicular to in-plane. The volumetric heat capacity was measured by differentialscanning calorimeter (DSC). The measurement results were compared to calculations. Theresults from the ‘anisotropic’ module was in best agreement with the calculated results. It wasalso illustrated that the TPS may be used for relative measurements to find damaged VIP

Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning

Major depressive disorder (MDD) has been considered a severe and common ailment with effects on functional frailty, while its clear manifestations are shrouded in mystery. Hence, manual detection of MDD is a challenging and subjective task. Although Electroencephalogram (EEG) signals have shown promise in aiding diagnosis, further enhancement is required to improve accuracy, clinical utility, and efficiency. This study focuses on the automated detection of MDD using EEG data and deep neural network architecture. For this aim, first, a customized InceptionTime model is recruited to detect MDD individuals via 19-channel raw EEG signals. Then a channel-selection strategy, which comprises three channel-selection steps, is conducted to omit redundant channels. The proposed method achieved 91.67% accuracy using the full set of channels and 87.5% after channel reduction. Our analysis shows that i) only the first minute of EEG recording is sufficient for MDD detection, ii) models based on EEG recorded in eyes-closed resting-state outperform eyes-open conditions, and iii) customizing the InceptionTime model can improve its efficiency for different assignments. The proposed method is able to help clinicians as an efficient, straightforward, and intelligent diagnostic tool for the objective detection of MDD