One of the most important components in the radar and microwave industry that provides transmitting and receiving features for an antenna at the same time is called a circulator. Ferrite circulators are passive three‐port devices in which the RF or microwave signal is transmitted from one port to other port without leakage to the third port. One of the main disadvantages of these waveguide elements is their large dimensions and especially their height, which cannot be used in some systems due to the limited workspace. This paper solves this problem by exploiting an incomplete wall waveguide to reduce the height of the circulator by 50%. These changes always reduce the frequency bandwidth of the element, the proposed technique optimizes the dimensions of the ferrite and its magnetic characteristics and increases the bandwidth to 400 MHz in 2.9 GHz, which is the ideal bandwidth required by many radar systems that have been reached. The ferrite circulator designed and built in this article has a port with a width of 72.13 mm and a height of 17 mm in the S‐band. The method results in a low insertion loss which is less than 0.5 decibels and a high isolation which created more than 20 decibels. The designed and optimized elements have been implemented and the test results verify the theoretical results.

This paper solves this problem by exploiting an incomplete wall waveguide to reduce the height of circulator by 50%. Although these changes always reduce the frequency bandwidth of the element, the proposed technique optimizes the dimensions of the ferrite and its magnetic characteristics, increases the bandwidth to 400 MHz in 2.9 GHz, which is the idea bandwidth required by many radar systems which has been reached.image

The Local Fitness Method (LFM) and Speaker‐Listener Label Propagation (SLPA) algorithms are widely used to detect overlapping communities in complex networks. The main problem with these two algorithms is that they are extremely sensitive to the setting of the hyperparameters. Previous methods set the hyperparameters of LFM or SLPA based on either empirical values or random choices, resulting in a large amount of computation for adjusting those parameters. To solve this problem, in this paper, a machine‐learning‐based approach is proposed to automatically set the hyperparameters of these two algorithms. Experimental results show that compared with the manual method, automatically setting the hyperparameter using machine learning models can lead to higher‐quality divisions. Furthermore, in comparison to the well‐known hyperparameter tuning method using Bayesian Optimization, the proposed predictive model‐based approach can find suitable para meters for LFM and SLPA much faster, while achieving competitive results in terms of division quality.

The Local Fitness Method (LFM) and Speaker‐Listener Label Propagation (SLPA) are classical overlapping community detection algorithms. However, it is challenging to find suitable parameter settings for these algorithms. In this paper, we introduce for the first time, a machine learning‐based framework that automatically generates predictive models for setting the hyperparameters of LFM and SLPA.image

Currently, the generation of energy relies primarily on the usage of harmful fossil fuels all over the world. New technologies, on the other hand, have enabled renewable energy resources to have a reduced environmental effect, greater long‐term sustainability, and, in some cases, to be cheaper than fossil fuels. Microgrids that use renewable resources can deliver low‐cost clean electricity while also improving local resiliency. They do, however, have certain issues in terms of regulating internal frequency and voltage within a microgrid, which is hampered by the intermittent nature of renewable sources. An islanded Microgrid for San Andres, Colombia has been designed for 24.57 kW peak load composed of a PV‐wind‐storage system by considering dispatch strategy based control in Homer Pro and assessed utilizing Simulink/MATLAB. Lastly, a PLC‐SCADA system along a HMI (Human‐Machine Interface) in C# was created to monitor the Microgrid in real‐time and produce an alarm as the microgrid elements are not operating within their ideal range of operational. The data from HMI is automatically uploaded to a cloud database. This work incorporates the dispatch control based techno‐economic design, assessment and a complete supervisory control strategy for an optimum microgrid.

An islanded Microgrid for San Andres, Colombia has been designed for 24.57 kW peak load composed of a PV‐wind‐storage system by considering dispatch strategy based control in Homer Pro and assessed utilizing Simulink/MATLAB. Lastly, a PLC‐SCADA system along a HMI (Human‐Machine Interface) in C# was created to monitor the Microgrid in real‐time and produce an alarm as the microgrid elements are not operating within their ideal range of operational. The data from HMI is automatically uploaded to a cloud database. This work incorporates the dispatch control based techno‐economic design, assessment and a complete supervisory control strategy for an optimum microgridimage

Solar photovoltaic (PV) systems will drive deep electrification of energy systems leading to clean energy 2050. However, connecting large amounts of solar PV systems on direct current (DC) networks, like solar farms and potential future DC distribution systems, would lead to over voltages and loss of solar PV power output due to voltage issues. Further, current PV integration within distribution networks operate exclusively to maximize output using maximum power point tracking algorithms, without network coordination, which may lead to reduced solar output due to voltage issues. Here, a coordinated optimization model for solar PV systems and distribution network voltage regulators is presented. The proposed model optimally controls the settings of voltage controllers (DC‐DC converters), placed at the outputs of solar PV units and selected distribution lines, while maximizing solar power output and minimizing substation power (i.e. system losses). The solar PV systems are modelled using a trained neural network. Testing various systems against uncoordinated situations revealed that the proposed model yielded an increase in solar power of up to 60.06%, in the 28‐bus case. The proposed method will be an excellent tool enabling deep electrification using solar PV system and it overcomes limitations of uncoordinated systems used in practice today.

A new coordinated optimization model for solar PV systems and DC distribution systems optimally controls the settings of voltage controllers (DC‐DC converters), placed at the outputs of solar PV units and selected distribution lines, while maximizing solar power output and minimizing substation power (i.e. system losses).Testing various systems against uncoordinated situations revealed that the proposed model yielded an increase in solar power of up to 60.06% and overcomes limitations of uncoordinated systems used in practice today.image

The continued quest for finding a low‐power and high‐performance hardware algorithm for signed number multiplication led to designing a simple and novel radix‐8 signed number multiplier with 3‐bit grouping and partial product reduction performed using magnitudes of the multiplicand and the multiplier. The pre‐computation stage constitutes magnitude calculation and non‐trivial computations required to generate partial products. A new partial product reduction strategy is deployed in the design to improve the speed with low cost. 8×8, 16×16, 32×32, and 64×64 designs are presented for the proposed architectures. Performance results include area, power, delay, and power‐delay‐product of synthesized and post‐layout designs using 32 nm CMOS technology with 1.05 V supply voltage.

A simple and novel radix‐8 signed number multiplier algorithm with 3‐bit grouping performed on the magnitudes of the operands to achieve low power and high performance, especially in the case of wider multipliers such as 32×32 and 64×64. P‐D‐A results from the synthesized and post‐layout designs using 32 nm CMOS technology with 1.05 V supply voltage.image