Numerical study of perforated obstacles effects on the performance of solar parabolic trough collector

The current work presents and discusses a numerical analysis of improving heat transmission in the receiver of a parabolic trough solar collector by introducing perforated barriers. While the proposed approach to enhance the collector’s performance is promising, the use of obstacles results in increased pressure loss. The Computational Fluid Dynamics (CFD) model analysis is conducted based on the renormalization-group (RNG) k-ɛ turbulent model associated with standard wall function using thermal oil D12 as working fluid The thermo-hydraulic analysis of the receiver tube with perforated obstacles is taken for various configurations and Reynolds number ranging from 18,860 to 81,728. The results are compared with that of the receiver without perforated obstacles. The receiver tube with three holes (PO3) showed better heat transfer characteristics. In addition, the Nusselt number (Nu) increases about 115% with the increase of friction factor 5–6.5 times and the performance evaluation criteria (PEC) changes from 1.22 to 1.24. The temperature of thermal oil fluid attains its maximum value at the exit, and higher temperatures (462.1 K) are found in the absorber tube with perforated obstacles with three holes (PO3). Accordingly, using perforated obstacles receiver for parabolic trough concentrator is highly recommended where significant enhancement of system’s performance is achieved

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI

Gumpy: a python toolbox suitable for hybrid brain-computer interfaces

Objective. The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain-computer interface (BCI). Approach. Gumpy provides state-of-the-art algorithms and includes a rich selection of signal processing methods that have been employed by the BCI community over the last 20 years. In addition, a wide range of classification methods that span from classical machine learning algorithms to deep neural network models are provided. Gumpy can be used for both EEG and EMG biosignal analysis, visualization, real-time streaming and decoding. Results. The usage of the toolbox was demonstrated through two different offline example studies, namely movement prediction from EEG motor imagery, and the decoding of natural grasp movements with the applied finger forces from surface EMG (sEMG) signals. Additionally, gumpy was used for real-time control of a robot arm using steady-state visually evoked potentials (SSVEP) as well as for real-time prosthetic hand control using sEMG. Overall, obtained results with the gumpy toolbox are comparable or better than previously reported results on the same datasets. Significance. Gumpy is a free and open source software, which allows end-users to perform online hybrid BCIs and provides different techniques for processing and decoding of EEG and EMG signals. More importantly, the achieved results reveal that gumpy’s deep learning toolbox can match or outperform the state-of-the-art in terms of accuracy. This can therefore enable BCI researchers to develop more robust decoding algorithms using novel techniques and hence chart a route ahead for new BCI improvements