International Journal of Numerical Modelling-Electronic Networks Devices and Fields最新文献

Virtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi-objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi-objective multi-verse optimization algorithm based on the inverse $��S�$ decline curve type (ISDT-MOMVO) which introduces a strategy optimization initialization solution with quasi-opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT-MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

In contemporary biological research and applications, control systems have become indispensable for understanding and managing the intricate dynamics of the human biological system. Given the critical role of components such as the pancreas structure, protein formation, insulin and glucose regulation, and the genetic regulatory network (GRN), any disturbances in these systems can lead to severe health issues. To overcome these issues, to introduced a novel hybrid controller called the fuzzy lion-based optimized fractional order system (FL-OFOS) for evaluating the performance of the system. This controller aims to efficiently govern and regulate key components of the human biological system, including insulin dynamics, protein synthesis, pancreas functionality, and GRN management. The controller is specifically tailored to regulate insulin, protein synthesis, pancreas function, and GRN dynamics within the human biological system. The optimization of biological parameter values is achieved through the incorporation of the fuzzy lion function. The results of this study highlight the efficacy of the FL-OFOS controller in optimizing and regulating various biological parameters. The system demonstrates minimal error rates, rapid response times, reduced overshoot, and high control precision and accuracy. The proposed controller achieves a minimal error rate of 0.98%, with only minor overshoot occurring in the outcomes. As a result, the FL-OFOS controller offers substantial gains and delivers optimal results in the realm of biological systems.

With continuous downscaling of transistor sizes, the sensitivity to single event effect (SET) has become one of the most important reliability issues for aerospace integrated circuits. Besides the SET, integrated circuits will be affected by multiphysics such as temperature and voltage when working in space. Currently, the commonly used modeling methods are based on physical mechanisms and the double exponential pulse current. However, both methods are hard to build an accurate SET current model when various variables are considered. In this article, a novel machine learning modeling method of multiphysics SET is proposed, using intelligent algorithms to optimize the network also. With this method, we can obtain a reasonable and accurate multiphysics SET model based on neural network with single hidden layer, and there is no need to consider complex physical mechanisms. The model data is collected from TCAD simulation. Ant colony algorithm was used to optimize the initial values of the network. The RMS (root mean square error) of modeling result is less than 2%.

This work presents an analytical model for the drain and gate currents of silicon nanowire and nanosheet MOS transistors valid in all operating regions in the temperature range from 300 to 500 K. Analytical models for the tunneling components as well as for the reversely biased drain-to-channel PN junction are presented. Also, the models accounting for the necessary modifications in the silicon physical quantities for high-temperature operation, such as the maximum carrier mobility, the bandgap, and the intrinsic carrier concentration, are presented. The proposed model uses a single set of parameters, extracted at room temperature, to describe the high-temperature operation of silicon nanowire MOSFETs. The model is validated with comparisons between modeled and experimental results for devices with different fin widths and operating temperatures, with good agreement.

In the present work, we investigate the impact of structure dimensional parameters on the short channel effects which occurs especially below 20 nm regime particularly gate induced drain leakage (GIDL) current. Using technology computer aided design simulation (TCAD), we have examined the GIDL for SiGe as source/drain in NTJLFET. The structural dimensional parameters such as the nanotube thickness, core and outer gates thickness and gate electrode work function shows the significant impact on the band to band tunneling in lateral direction (L-BTBT) which induced GIDL current. It is analyzed that increase in the nanotube thickness and physical oxide thickness increase the GIDL current, while increasing the gate electrode work function, core gate and outer gate thicknesses gives reduced GIDL current. The SiGe S/D NTJLFET produce a remarkable high I_ON/I_OFF ratio ~ 10¹¹. A compact model for GIDL current is also developed which shows the dependency of structure parameters on leakage current. The SiGe has been incorporated as source and drain in NTJLFET which creates the energy band discontinuity. Furthermore, SiGe S/D NTJLFET is fairly compared with the conventional NT JLFET and nanowire (NW) JLFET and shows an improved performance.

High-performance frequency selective surfaces (FSSs) have gained attention for their spatial filtering characteristics in 5G communication systems. In this work, we propose an efficient and accurate design methodology for the FSS. Three different artificial neural network methods (ANN) are employed, and their performances are compared for analysis and synthesis purposes. Results show that GRNN has the highest performance for both training and test phase of ANN based FSS analysis and synthesis. A novel, compact, low-profile triple band FSS unit cell is introduced, and the working mechanism is described. By applying ANN based design procedure, the unit cell dimensions to resonate at the 5G mm-wave frequency band is extracted. A unit cell with the extracted physical dimensions is simulated with a full-wave analysis tool. The simulation results show that the FSS has the filtering feature at the predetermined mm-wave frequencies of the 5G communication. The prototype of the FSS is fabricated, as well. The simulations are verified experimentally with measurement results. The results show that proposed ANN based analysis and synthesis method can be an effective tool for the design of FSS band-pass filter for 5G applications.

This work presents a numerical approach for solving the time–space fractional-order wave equation. The time-fractional derivative is described in the conformal sense, whereas the space-fractional derivative is given in the Caputo sense. The investigated technique is based on the third-kind of shifted Chebyshev polynomials. The main problem is converted into a system of ordinary differential equations using conformal mapping, Caputo derivatives, and the properties of the third-kind shifted Chebyshev polynomials. Then, the Chebyshev collocation method and the non-standard finite difference method will be used to convert this system into a system of algebraic equations. Finally, this system can be solved numerically via Newton's iteration method. In the end, physics numerical examples and comparison results are provided to confirm the accuracy, applicability, and efficiency of the suggested approach.

The high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short-term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride-through (LVRT), high voltage ride-through (HVRT), continuous fault ride-through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30-node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios.

In this study, a flexible control approach is presented for smart home with DES system in microgrid connection. The smart home with DES has operation capability under GC-Mode and I-Mode functions with the proposed control algorithm. The control algorithm is also developed in terms of international standards IEEE 929 and IEC 61727 for grid connection requirements. Additionally, a PLL-less control algorithm is embedded in the proposed control algorithm for the active/reactive power management and the grid synchronization. Thus, slow dynamic response and calculation burden issues in controller is avoided and the power flow capability of the grid-connected DES in smart home is enhanced. Furthermore, the coordinate transformation is fulfilled for the power flow control in the control loop via the generation of cosine and sinus functions. Besides, the mathematical formulations of the proposed control algorithm are presented in detail. To show the effectiveness of the proposed control algorithm, a smart home with a photovoltaic DES model is designed and constructed in a simulation environment. The proposed control method is tested under various operation cases such as dynamic environmental conditions, GC-Mode/I-Mode, reactive power support and voltage variations. The proposed method shows efficient performance under applications of the different operation situations. Moreover, the international requirements are satisfied for GC-Mode and I-Mode transitions.

This paper presents the development of an optimization and modeling method for the objective functions of output power, efficiency and weight of an outer rotor permanent magnet brushless DC (BLDC) motor based on radial basis function (RBF) approximation technique. The proposed RBF-based Pareto optimization method requires less knowledge about electric/magnetic formulas and can replace conventional optimizations based on these equations with higher accuracy. To apply the proposed optimization method, the initial design should be developed using such equations. Therefore, RBFs are used to model and predict engine behavior. To optimize the objective functions, we used a genetic algorithm optimization technique with nonlinear electric and magnetic constraints to find the Pareto front set. The design obtained by the proposed radial basis function Pareto optimization (RBFPO) method was finally verified by Ansoft Maxwell. The results of optimal design using the RBFPO method have higher output power and efficiency. Also, in addition to the advantage of a favorable accuracy, RBF-based models are significantly faster than models available in simulation tools.