High quality factor cold sintered Li2MoO4BaFe12O19 composites for microwave applications

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

A Novel Meander Bowtie-Shaped Antenna with Multi-Resonant and Rejection Bands for Modern 5G Communications

To support various fifth generation (5G) wireless applications, a small, printed bowtie-shaped microstrip antenna with meandered arms is reported in this article. Because it spans the broad legal range, the developed antenna can serve or reject a variety of applications such as wireless fidelity (Wi-Fi), sub-6 GHz, ultra-wideband (UWB) 5G communications due to its multiband characterization and optimized rejection bands. The antenna is built on an FR-4 substrate and powered via a 50-Ω microstrip feed line linked to the right bowtie’s side. The bowtie’s left side is coupled via a shorting pin to a partial ground at the antenna back side. A gradually increasing meandering microstrip line connected to both sides of the bowtie to enhance the rejection and operating bands. The designed antenna has seven operating frequency bands of (2.43 – 3.03) GHz, (3.71 – 4.23) GHz, (4.76 – 5.38) GHz, (5.83 – 6.54) GHz, (6.85 – 7.44) GHz, (7.56 – 8.01) GHz and (9.27 – 13.88) GHz. The simulated scattering parameter 11 reveals six rejection bands with percentage bandwidth of 33.87%, 15.73%, 11.71, 7.63%, 6.99%, 12.22% respectively. The maximum gain of the proposed antenna is 4.46 dB. The suggested antenna has been built, and the simulation and measurement results are very similar. The reported antenna is expanded to a four-element design to investigate their MIMO characteristics

High quality factor cold sintered Li2MoO4BaFe12O19 composites for microwave applications

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

Prediction of Combined Cycle Power Plant Electrical Output Power Using Machine Learning Regression Algorithms

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

Prediction of Combined Cycle Power Plant Electrical Output Power Using Machine Learning Regression Algorithms

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