High quality factor cold sintered Li2MoO4- BaFe12O19 composites for microwave applications
Authors : Sinan S. Faouri, Ali Mostaed, Julian S Dean, Dawei Wang, Derek C Sinclair, Shiyu Zhang, William G Whittow, Yiannis Vardaxoglou, Ian M Reaney
Abstract : Ceramics-ceramic composites in series (1-x)Li2MoO4-xBaFe12O19 (LMO-BF12, 0.00 ≤ x ≤ 0.15) have been cold sintered at 120 °C and their structure and properties characterized. X-ray diffraction, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) confirmed that compositions were dual phase and had a dense microstructure. Composites in the xBF12-(1-x)LMO (0.0 ≤ x ≤ 0.15) series resonated at MW frequencies (∼6 GHz) with 5.6≤εr ≤ 5.8 and Qf = 16,000–22,000 GHz, despite the black colour of compositions with x > 0. The permeability of the composites was measured in the X band (∼8 GHz) and showed an increase from 0.94 (x = 0.05) to 1.02 (x = 0.15). Finite element modelling revealed that the volume fraction of BF12 dictates the conductivity of the material, with a percolation threshold at 10 vol% BF12 but changes in εr as a function of x were readily explained using a series mixing model. In summary, these composites are considered suitable for the fabrication of dual mode or enhanced bandwidth microstrip patch antennas.
Keywords : Cold sintering process,
Ceramic material,
Composite,
Dielectric,
Finite element modelling (FEM)
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Prediction of combined cycle power plant electrical output power using machine learning regression algorithms
Authors : Nader S. Santarisi, Sinan S.Faouri
Abstract : In order to monitor the performance and related efficiency of a combined cycle power plant (CCPP), in addition to the best utilization of its power output, it is vital to predict its full load electrical power output. In this paper, the full load electrical power output of CCPP was predicted employing practically efficient machine learning algorithms, including linear regression, ridge regression, lasso regression, elastic net regression, random forest regression, and gradient boost regression. The original data came from an actual confidential power plant, which was working on a full load for 6 years, with four major features: ambient temperature, relative humidity, atmospheric pressure, and exhaust vacuum, and one target (electrical power output per hour). Different regression performance measures were used, including R2 (coefficient of determination), MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error). Research results revealed that the gradient boost regression model outperformed other models with and without using the dimensionality reduction technique (PCA) with the highest R2 of 0.912 and 0.872, respectively, and had the lowest MAPE of 0.872 % and 1.039 %, respectively. Moreover, prediction performance dropped slightly after using the dimensionality reduction technique almost in all regression algorithms used. The novelty in this work is summarized in predicting electrical power output in a CCPP based on a few features using simpler algorithms than reported deep learning and neural networks algorithms combined. That means a lower cost and less complicated procedure as per each, however, resulting in practically accepted results according to the evaluation metrics used.
Keywords : combined cycle power plants, machine learning, predictive models, linear regression
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Examining stability of machine learning methods for predicting dementia at early phases of the disease
Authors : Sinan S.Faouri, Mahmood AlBashayreh, Mohammad Azzeh
Abstract : Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells
stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a
vital task to rescue patients’ lives from bad consequences and provide them with better
healthcare. Machine learning methods have been proven to be accurate in predicting dementia in
the early phases of the disease. The prediction of dementia depends heavily on the type of
collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and
Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic
Resonance Imaging (MRIs). Other biological features such as age and gender can also help in
the diagnosis of dementia. Although many studies use machine learning for predicting dementia,
we could not reach a conclusion on the stability of these methods for which one is more accurate
under different experimental conditions. Therefore, this paper investigates the conclusion
stability regarding the performance of machine learning algorithms for dementia prediction. To
accomplish this, a large number of experiments were run using 7 machine learning algorithms
and two feature reduction algorithms namely, Information Gain (IG) and Principal Component
Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection
were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted
in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were
recorded. The obtained results show that among seven algorithms the support vector machine
and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it
was found that using IG would seem more efficient than using PCA for predicting Dementia.
These promising results open the door to a new era of early prognosis of Alzheimer’s Disease
and Related Dementias (ADRD).
Keywords : Dementia disorder, Machine learning, Stability analysis, Feature selection, Feature reduction
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