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Finding the optimum conductor's pattern for unequally spaced grounding grids can be achieved by using empirical formulas or optimization techniques. Empirical formulas give an approximate solution depending on human experience and never cover the entire search space. On the other hand, the optimization techniques generally require a significant effort and time. To overcome the previous issues, this paper presents a closed-form solution based on optimized results. The main motivation behind this formula is the comprehensive study carried out by the authors to find optimum location of grounding grid conductors over different areas. In that work, particle swarm optimization technique (PSO) has been adapted with combined integration matrix method (CIMM) to facilitate the study. In this paper, the results obtained previously were summarized in the proposed formula. The proposed formula gives the optimum design of unequally grounding grids by specifying the conductor's locations as percentage of the side length for square and rectangular shapes. As long as the side length and the number of conductors are given, the location of each conductor as a percentage of the side length can be obtained. The effect of adding driven rods and the stratified soil conditions on the results of the optimized design were investigated. The validity of the proposed closed-form solution was verified experimentally. [ABSTRACT FROM AUTHOR]

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently. [ABSTRACT FROM AUTHOR]

In this paper, a class of cyclic (noncyclic) operators of condensing nature are defined on Banach spaces via a pair of shifting distance functions. The best proximity point (pair) results are manifested using the concept of measure of noncompactness (MNC) for the said operators. The obtained best proximity point result is used to demonstrate existence of optimum solutions of a system of differential equations involving ψ -Hilfer fractional derivatives of complex order. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are currently being substantially integrated into the power grid because of the recent liberalization of the energy market and associated benefits to both the environment and the economy. A significant amount of renewable energy sources are stochastic and weather dependent, being integrated into the power system that causes a mismatch between the generation of power and the load demand, which creates major issues like variations in frequency and power transmission between the regions. So it is necessary to maintain the frequency and tie-line power variations within a defined range. This is known as automatic generation control (AGC) or load frequency control (LFC). This paper uses brown bear optimization algorithm (BOA) to tune the PID and cascaded PI-PDN controllers for LFC of the renewable integrated two-area power system. The ITAE objective function is utilized to obtain optimal gains of the PID controller. The performance of LFC with BOA-tuned PID controller is compared with other optimization techniques named grey wolf optimization technique (GWO) and particle swarm optimization (PSO) on a RES integrated two-area power system under various scenarios. Further, the effectiveness of the BOA is verified by comparing performance of the PID and cascaded PI-PDN controllers. According to simulation results, BOA offers superior performance in comparison with the GWO and PSO technique. [ABSTRACT FROM AUTHOR]

During the coronavirus pandemic, demand surges and supply chain disruptions have resulted in healthcare commodity shortages. Limitations in modeling approaches forced decision makers to make quick decisions with little information. This paper presents a practical optimization model to guide ordering plans for a set of healthcare commodities and populations, such as masks for health care providers, during a pandemic. We collaborated with several organizations managing inventory for healthcare commodities to identify the key challenges, decisions, and objectives they face during a pandemic. The proposed model differs from other inventory and order management models in that the optimization balances the impact of commodity substitutions with delays in meeting demand forecasts. To balance these impacts, we introduce a Healthcare Commodity Metric that quantifies the relative consequences of delay and substitutions for multiple commodities and populations that vary in criticality. To the best of our knowledge, our model is the first to balance the consequences of delays and substitutions for multiple healthcare commodities during a pandemic. The model supports an agile and collaborative decision-making process needed in a constantly changing environment. The model is agile in that it can be adapted quickly to changes in demand, supply capacities, supply costs, and lead times. The model supports collaborative decision making by estimating the impacts of operational (e.g., ordering) and strategic (e.g., budget) decisions. We present an example use case to illustrate how the model balances delay and substitution and can be used to support agile and collaborative decision making. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications. [ABSTRACT FROM AUTHOR]

Sparse matrix-vector multiplication (SpMV) plays a critical role in a wide range of linear algebra computations, particularly in scientific and engineering disciplines. However, the irregular memory access patterns, extensive memory usage, high bandwidth requirements, and underutilization of parallelism hinder the computational efficiency of SpMV on GPUs. In this paper, we propose a novel approach called block-wise dynamic mixed-precision (BDMP) to address these challenges. Our methodology involves partitioning the original matrix into uniformly sized blocks, with each block's size determined by considering architectural characteristics and accuracy requirements. Additionally, we dynamically assign precision to each block using a precision selection method that takes into account the value distribution of the original sparse matrix. We develop two distinct SpMV computation algorithms for BDMP: BDMP-PBP (Precision-based partitioning) and BDMP-TCKI (Tailored compression and kernel implementation). BDMP-PBP partitions the matrix into two independent matrices for separate computations based on block precision, offering flexibility for integration with other optimization techniques. Meanwhile, BDMP-TCKI focuses on achieving significant thread-level parallelism and memory utilization by tailoring an appropriate compressed storage format and kernel implementation for each block. We compare BDMP with NVIDIA's cuSPARSE library and three state-of-the-art SpMV methods, including SELLP, MergeBase, and BalanceCSR, using matrices from the University of Florida's SuiteSparse dataset collection. BDMP-PBP and BDMP-TCKI show average speedups up to 2.64 × and 2.91 × on Turing RTX 2080Ti, and up to 2.99 × and 3.22 × on Ampere A100. The results demonstrate that BDMP enables the optimization of computation speed without compromising the precision necessary for reliable results. [ABSTRACT FROM AUTHOR]

This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design. [ABSTRACT FROM AUTHOR]

Background: MathWorks has provided an invaluable tool for designing and implementing FPGAs. MATLAB HDL coder serves a dual purpose, providing a quick proof of concept on the one hand and providing the g an easy-to-use platform for testing and verification on the other. It has main drawbacks over these advantages; it generates a code that is not optimized for both area and frequency. Results: In this paper, we provide a technique for optimizing both area and frequency without losing the main advantages. The most affecting problem we found is loops. This paper classifies loop writing purposes into two types. The first one is preferable and introduces ease of writing a few lines instead of repeating the code. The second type is the problem that we intended to solve. Type II loop is appearing when the algorithm should perform these lines for several clock cycles. Writing it traditionally, force the synthesizer to implement all the repetitive clock cycles as repetitive hardware to be done in one clock cycle. This clock cycle is wide in time and is slow in frequency. This paper introduces an optimization technique for this problem. We compare before and after the implementation of our proposed technique. Conclusions: We used Xilinx Spartan 6 XC6SLX4-2CPG196 FPGA. Our proposed technique improves the number of slice LUTs (Look Up Tables) requirement from 366 to 72%. The frequency improved from: 26.574 to 185.355 MHz. Based on that, we now recommend using MATLAB HDL coder in FPGA Design. [ABSTRACT FROM AUTHOR]

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving. [ABSTRACT FROM AUTHOR]

In many service systems, the service rates of the servers or the machines degrade over time due to a variety of reasons like fatigue, deficiency (due to manufacturing defect or energy loss, deformation, or excessive heat), and lack of proper maintenance. To make a service system beneficial for both customers and management points of view, it is imperative that models are developed to understand the impact of degradation. In this paper, we study M A P / P H / 1 - type queueing models incorporating degradation, failures/breakdowns and repairs. The degradation and to restore to normalcy in the service rate we look at two scenarios. In the first one, the service is restored to normalcy immediately after the server becomes idle or a fixed number of services is offered. In the second one, the restoration takes a random time that is modeled using a phase type distribution. Also, there are a variety of reasons that lead to failures/breakdowns of the server. We model these using (possibly) different phase type distributions. Both the models are studied analytically using matrix-analytic methods and illustrative numerical examples bringing out the qualitative behavior of the impact of degrading services are discussed. Two cost optimization problems whose solutions are obtained using particle swarm optimization technique along with a few illustrative examples are presented. [ABSTRACT FROM AUTHOR]

A helicopter is a multivariable, nonlinear, higher order and strongly coupled system. The helicopter dynamics is subjected to unknown external disturbances and sensitive to parametric uncertainties. Such complications require a sophisticated control approach which may handle all these difficulties. The paper presents a trajectory control approach using an improved teaching learning based optimization (iTLBO) method, which optimizes the parameters of proportional integral derivative controller. This control approach is validated by minimizing the yaw and pitch errors of two degrees of freedom helicopter system. It is observed that the experimental results in a laboratory setup does not stand true when subjected to weather changes during the helicopter flight. To overcome this situation, a real time like conditions are created in the laboratory using the two external fans creating strong wind disturbance and the proposed controller is implemented to eliminate the resulting external disturbances created due to high speed wind of external fans. Initially, modelling of helicopter system is achieved using the Lagrangian approach. Then, the TLBO is tuned to improve the controller performance using MATLAB platform. The effectiveness and superiority of iTLBO is proved over other optimization techniques available in literature like GA, PSO and TLBO by using linear and nonlinear benchmark functions. The optimized parameters hence obtained are implemented on helicopter system under the presence of external air disturbance and disturbance less results are obtained. Results obtained are validated using time response analysis on the MATLAB/Simulation platform. [ABSTRACT FROM AUTHOR]

The rapid growth of technology and uneven demographic population distribution has led to uneven distribution of electrical load demand across the three phases of the electrical distribution network. It has resulted in a steep rise in voltage unbalance scenarios. Such occurrences may have a lasting negative impact on the distribution system in the form of energy loss and extra costs for generation. This paper proposes a Rao optimization-based demand-side management (DSM) model to help the distribution utilities mitigate these issues. The proposed methodology has been designed to effectively distribute the load demand pattern in each node connected across each phase. The proposed DSM methodology has been designed keeping in view to discard any possibility of the need for lateral/load re-phasing. A day ahead load scheduling format has been adopted to design this proposed methodology. It has been implemented on IEEE European 906bus low voltage distribution network. Further, to verify the effectiveness of the proposed methodology, Gauss implicit Zbus load-flow method is performed upon the test system. The test system considered is IEEE European 906bus low voltage distribution system. The entire methodology is designed in the MATLAB 2018a platform. The implementation of the proposed methodology has reduced the phase voltage unbalance rate in the original test system by ~ 50%. Following the application of the suggested technique, the peak to average ratio in the original test system reduced by 6.22%, 38.7%, 3.22%, and 26.46% in each of the three phases, including the total load consumption of the end users. [ABSTRACT FROM AUTHOR]

In this paper, different methods are utilized to solve the coordination issue involving directional overcurrent relays (DOCRs) and distance relays. The proper coordination of DOCRs and distance relays is a critical issue for system security in electrical networks. Finding the DOCRs setting, pickup current(Ip) and timed dial setting (TDS), and operating time for zone-2 of distance relays is the primary objective of solving the coordination problem. The constant parameters A & B of the directional overcurrent relay that are responsible to control the form of the relay´s characteristics as well as the Ip and TDS have been regarded as variables in this problem. The optimal value for these four DOCR settings has been determined using different optimization techniques. The primary and backup relays must operate sequentially and without any violations, and this must be guaranteed by optimization techniques. In order to determine the operation time for zone-2 and DOCRs setting, optimization methods are examined utilizing the 8-bus and IEEE 30-bus networks. Different optimization algorithms, including recent and traditional techniques, are compared. The obtained results show the superiority of the genetic algorithm (GA) in solving the coordination problem of distance relays and DOCRs. Also, the obtained results prove the ability of the GA method compared to the particle swarm algorithm (PSO), grey wolf optimization (GWO), water cycle technique (WCA), equilibrium optimizer (EO), African vultures optimization algorithm (AVOA), flow direction algorithm (FDA), and gorilla troops optimizer (GTO) techniques. [ABSTRACT FROM AUTHOR]

Time-consuming objective functions are inevitable in practical engineering optimization problems. This kind of function makes the implementation of metaheuristic methodologies challenging since the designer must compromise between the quality of the final solution and the overall runtime of the optimization procedure. This paper proposes a novel multi-objective optimization algorithm called FC-MOEO/AEP appropriate for highly time-consuming objective functions. It is based on an equilibrium optimizer equipped with an archive evolution path (AEP) mechanism. The AEP mechanism considers the evolutionary trajectory of the decision space and anticipates the potential regions for optimal solutions. Besides having a high convergence rate, the newly proposed approach also possesses an intelligent balance between exploration and exploitation capabilities, enabling the algorithm to effectively avoid getting stuck in the local Pareto. To assess the efficacy of the FC-MOEO/AEP, a range of mathematical optimization problems and three real-world structural design problems were employed. In order to gauge the method's performance against other approaches, a novel performance indicator known as convergence speed was introduced and utilized, in addition to standard metrics. The numerical findings demonstrate the robust and consistent performance of the FC-MOEO/AEP in tackling complex multi-objective problems. [ABSTRACT FROM AUTHOR]

In this paper, we present an entirely simulation-based method to predict the performance of a complete rectenna system that includes all its components: the receiving antenna, the matching circuits between the antenna and the rectification circuit, and the load circuit. Whereas previous efforts to predict the performance of a rectenna system subdivided the system into the antenna part (radiation to AC power conversion) and the circuit part (AC power to DC power conversion), and made assumptions about the performance of the non-linear part of the rectenna based on a specified power level and frequency, in this method, the radiation part of the system is incorporated into the simulation by using Thevenin theorem. The method proposed in this work enables the rectenna designer to predict the performance of the complete rectenna system, at the design stage, for variation in the incident field's power density, angle of incidence, and operating frequency. Such performance prediction was not available before. Furthermore, the proposed method enables the rectenna designers to optimize the entire system over a portion of or the entire range of the operating frequency. Experimental results are provided to demonstrate the accuracy of the method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to suppressing bounded exogenous disturbances in a linear discrete-time control system by a static state- or output-feedback control law. The approach is based on reducing the original problem to a nonconvex matrix optimization problem with the gain matrix as one variable. The latter problem is solved by the gradient method; its convergence is theoretically justified for several important special cases. An example is provided to demonstrate the effectiveness of the iterative procedure proposed. [ABSTRACT FROM AUTHOR]

Phase field modeling finds utility in various areas. In optimization theory in particular, the distributed control and Neumann boundary control of phase field models have been investigated thoroughly. Dirichlet boundary control in parabolic equations is commonly addressed using the very weak formulation or an approximation by Robin boundary conditions. In this paper, the Dirichlet boundary control for a phase field model with a non-singular potential is investigated using the Dirichlet lift technique. The corresponding weak formulation is analyzed. Energy estimates and problem-specific embedding results are provided, leading to the existence and uniqueness of the solution for the state equation. These results together show that the control to state mapping is well defined and bounded. Based on the preceding findings, the optimization problem is shown to have a solution. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of an extensive number of low-power sensor nodes to gather information from their environment and monitor physical activities. This makes node localization a crucial aspect in most WSN applications since measurement data is worthless unless the location from where the data is acquired is known precisely. The majority of localization solutions rely on anchor nodes for estimating the node locations with different localization accuracy, complexity, and hence different applicability. But, the cost and complexity in the localization of large-scale WSNs are not significantly reduced. In this paper, a novel Hybridized Dragonfly and Jaya Optimization technique (HyDAJ) is introduced for improving localization accuracy and performance of mobile WSNs. The proposed hybrid technique combines the advantages of Dragonfly algorithm and Jaya algorithm to localize the sensor nodes in a more efficient way and overcomes the limitations of the original algorithm. The hybrid algorithm verifies that all target nodes are precisely localized with higher accuracy. Simulation results reveal that HyDAJ outperforms existing methods under multiple metrics including localization efficiency, mean localization error, computation time, and energy consumption. [ABSTRACT FROM AUTHOR]

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. [ABSTRACT FROM AUTHOR]

This paper presents a prototype filter design using the orthant optimization technique to assist a filter bank multicarrier (FBMC) modulation scheme of a NextG smart e-healthcare network framework. Low latency and very high reliability are one of the main requirements of a real-time e-healthcare system. In recent times, FBMC modulation has gotten more attention due to its spectral efficiency. The characteristics of a filter bank are determined by t's, prototype filter. A prototype filter cannot be designed to achieve an arbitrary time localization (for low latency) and frequency localization (spectral efficiency), as time and frequency spreading are conflicting goals. Hence, an optimum design needed to be achieved. In this paper, a constraint for perfect or nearly perfect reconstruction is formulated for prototype filter design and an orthant-based enriched sparse ℓ1-optimization method is applied to achieve the optimum performance in terms of higher availability of subcarrier spacing for the given requirement of signal-to-interference ratio. Larger subcarrier spacing ensures lower latency and better performance in real-time applications. The proposed FBMC system, based on an optimum design of the prototype filter, also supports a higher data rate as compared to traditional FBMC and OFDM systems, which is another requirement of real-time communication. In this paper, the solution for the different technical issues of physical layer design is provided. The presented modulation scheme through the proposed prototype filter-based FBMC can suppress the side lobe energy of the constituted filters up to large extent without compromising the recovery of the signal at the receiver end. The proposed system provides very high spectral efficiency; it can sacrifice large guard band frequencies to increase the subcarrier spacing to provide low-latency communication to support the real-time e-healthcare network. [ABSTRACT FROM AUTHOR]

Fuzzy C-means (FCM) is one of the prominent and effective cluster-based image segmentation techniques exceedingly susceptible to noise and initial cluster centers, thereby effortlessly converging toward local optima. However, FCM consumes exceptionally high computation time due to the repetitive computation of the distance amid cluster centers and pixels. To resolve this apprehension, this paper aims to consider a histogram-based fast fuzzy image clustering (HBFFIC) procedure that primarily tends to carry out morphological reconstruction (MR) operation over the image to assure noise immunity and safeguard details of the imagery. Further, as a replacement for pixels of a summed image, clustering is carried out based on gray-level histogram. This with no qualm radically trims down the computational time as the number of gray levels in an image is normally to a great extent lesser than that of the number of its pixels. Though HBFFIC is a proficient local optimizer however, owing to arbitrary initialization that is carried out in FCM, HBFFIC has the utmost possibility to get effortlessly wedge into local optima. Consequently, this is where the role of nature-inspired optimization algorithms (NIOA) comes into picture. For that reason, this paper successfully makes use of NIOA to prevail over the dilemma using Archimedes optimizer (AO) to discover the most favorable cluster centers. The real-world images particularly synthetic, grayscale, and color pathology images are exercised to perform the experimental study. The experimental study clearly highlights that the proposed hybrid algorithm (HBFFIC-AO) for noisy image segmentation outperforms the other state-of-art algorithms in terms of segmentation accuracy (SA), comparison score (CS), MSE, and PSNR. The visual along with numerical outcomes projected in the experimental study point toward the pre-eminence of the proposed algorithm as compared with the prevailing leading-edge image segmentation algorithms. [ABSTRACT FROM AUTHOR]

Satellite clock bias is the key factor affecting the accuracy of the single point positioning of a global navigation satellite system. The traditional model back propagation (BP) neural network is prone to local optimum problems. This paper presents a prediction model and algorithm for the clock bias of the BP neural network based on the optimization of the mind evolutionary algorithm (MEA), which is used to optimize the initial weights and thresholds of the BP neural network. The accuracy of the comparison between clock bias data is verified with and without one-time difference processing. Compared with grey model (GM (1,1)) and BP neural network, this paper discusses the advantages and general applicability of this method from different constellation satellites, different atomic clock type satellites, and the amount of modeling data. The accuracy of the grey model (GM(1,1)), BP, and MEA-BP models for satellite clock bias prediction is analyzed and the root mean square error, range difference error, and the mean of the clock bias data compared. The results demonstrate that the prediction accuracy of the three satellites significantly increased after one-time difference processing and that they have good stability. The prediction accuracy of four sessions of 2 h, 3 h, 6 h, and 12 h obtained using the MEA-BP model was better than 0.74, 0.80, 1.12, and 0.87 ns, respectively. The MEA-BP model has a specific degree of improvement in the prediction accuracy of the different sessions. Additionally, the prediction accuracy of different models has a specific relationship with the length of the original modeling sequence, of which BP model is the most affected, and MEABP is relatively less affected by the length of the modeling sequence, indicating that the MEA-BP model has strong anti-interference ability. [ABSTRACT FROM AUTHOR]

This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers. [ABSTRACT FROM AUTHOR]

The paper considers the optimization of a liquid system for compensating excess reactivity by varying the number of gadolinium-containing fuel elements in a VVER fuel assembly and gadolinium mass loading into the fuel elements. Using a simplified refueling model, an ideal dependence of the neutron multiplication factor with burnable absorbers is obtained with reactor criticality, which is maintained using only burnable absorbers. The calculations were carried out using the Serpent neutron physics program code, version 2.1.32 (Finland). A significant reduction in uncompensated reactivity and low-level liquid radioactive waste generated using a liquid compensation system is estimated for real options similar to the ideal one. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

Selecting the appropriate cutter size and determining the noninterference tool orientation are the key steps in planning the four-axis machining of complex channel parts. In this paper, based on the invariance property of isometric transformation and the function of the minimum distance between surfaces, a nonlinear numerical optimization method is proposed to accurately solve the noninterference critical tool orientation and maximum taper angle of the conical cutter. Furthermore, the optimization method for the maximum taper angle of the conical cutter can handle the different cases, whether the tool orientation is preset or not. Firstly, the basic theory of the proposed optimization method for the conical cutter is given. Secondly, the optimization method for calculating the noninterference tool orientation is provided. Thirdly, the local optimization method for the taper angle of a conical cutter with the tool orientation preset, and the global optimization method for the maximum taper angle of the conical cutter are provided. The conical cutter solved by the local optimization method can reach critical state with a single constraint surface, but the conical cutter solved by the global optimization method can reach critical state with multiple constraint surfaces. Finally, the proposed method is verified by the simulation experiments. The results show that the provided method accurately calculates the result for the noninterference tool orientation of a given conical cutter and the max taper angle of the conical cutter. Furthermore, it proves that the collaborative optimization strategy of the tool orientation and cutter taper angle used in the global optimization method is much better for the max taper angle of the conical cutter. [ABSTRACT FROM AUTHOR]

Large-scale numerical optimization problems arise from many fields and have applications in both industrial and academic contexts. Finding solutions to such optimization problems efficiently requires algorithms that are able to leverage the increasing parallelism available in modern computing hardware. In this paper, we review previous work on parallelizing algorithms for nonlinear optimization. To introduce the topic, the paper starts by giving an accessible introduction to nonlinear optimization and high-performance computing. This is followed by a survey of previous work on parallelization and utilization of high-performance computing hardware for nonlinear optimization algorithms. Finally, we present a number of optimization software libraries and how they are able to utilize parallel computing today. This study can serve as an introduction point for researchers interested in nonlinear optimization or high-performance computing, as well as provide ideas and inspiration for future work combining these topics. [ABSTRACT FROM AUTHOR]

With the continuous increase in the number of vehicles and people on urban roads, various traffic problems have become increasingly serious and intelligent transportation has gradually gained widespread attention. As a computing paradigm that could effectively reduce the task processing time consumption of user device and the cost of cloud server, edge computing has become an indispensable part of intelligent transportation system. However, how to reduce the load imbalance of edge computing server while ensuring that the task processing of intelligent transportation device takes less time consumption and energy consumption has become a challenge. In order to tackle this challenge, the computation offloading decision-making problem in the intelligent transportation edge computing scenario was modeled as a multi-objective optimization problem in this paper, and an adaptive multi-objective optimization algorithm (E-NSGA-III) based on NSGA-III was used to solve this problem, and comparative experiment with other methods was made. Experimental results show that compared with NSGA-II, MOEA/D and NSGA-III, proposed algorithm (E-NSGA-III) in this paper can mostly reduce time consumption by 14.28%, 18.42% and 9.82%, energy consumption by 5.59%, 6.79% and 4.83%, and load balancing variances by 21.73%, 33.46% and 18.25%. [ABSTRACT FROM AUTHOR]

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated. [ABSTRACT FROM AUTHOR]

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm's basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms [ABSTRACT FROM AUTHOR]

This paper presents an 802.11ad-based radio frequency module for high data rate fixed wireless access and backhaul communications. The transceiver chip is manufactured in SiGe BiCMOS technology covering 57–71 GHz, providing 16 RX and 16 TX paths with combined output power of more than 20 dBm. The chip is packaged using embedded wafer-level BGA technology of the size 12.6 × 12.6 × 0.8 mm 3 and employs advanced dielectric materials with 2 metallic redistribution layers. The package integrates the transceiver chip with RX and TX high gain PCB antenna arrays, allowing effective isotropic radiated power (EIRP) of more than 40 dBm. Beam steering is achieved in ± 50 ∘ by the transceiver through providing appropriate weights to the antenna arrays. The paper presents generation of beamforming lookup table along with optimization of the power distribution to the array. This optimization results in flattening of the EIRP over the whole beam-steering range and frequency bandwidth. The module allows for data rates up to 10 Gbps by employing full-channel 128 QAM and half-channel 256 QAM single-carrier modulation. [ABSTRACT FROM AUTHOR]

In recent years, combinatorial optimization has been widely studied. The existing optimization solutions are prone to fall into local optimal solutions and have a lower probability of obtaining global optimal solutions. Quantum approximate optimization algorithm (QAOA) is an effective algorithm that can obtain the optimal solution with high probability. In this paper, the problem Hamiltonian is obtained by summing the problem function and the deformed constraints. Through theoretical formula derivation, the problem Hamiltonian is transformed into the Ising model. The performance of the experimental result under different optimizers and asynchronous lengths is verified on pyQPanda. The experimental results show that when using the problem Hamiltonian method set in this paper, the probability of obtaining the optimal solution is 99.59%. Compared with other methods, the proposed method can alleviate the risk of falling into local optimal solutions and obtain the global optimal solution with a higher probability. [ABSTRACT FROM AUTHOR]

Biophysical models contain a large number of parameters, while the spiking characteristics of neurons are related to a few key parameters. For thalamic neurons, relay reliability is an important characteristic that affects Parkinson's state. This paper proposes a method to fit key parameters of the model based on the spiking characteristics of neurons, and improves the traditional particle swarm optimization algorithm. That is, a nonlinear concave function and a Logistic chaotic mapping are combined to adjust the inertia weight of particles to avoid the particle falling into a local optimum in the search process or appearing premature convergence. In this paper, three parameters that play an important role in Parkinson's state of the thalamic cell model are selected and fitted by the improved particle swarm optimization algorithm. Using the fitted parameters to reconstruct the neuron model can predict the spiking trajectories well, which verifies the effectiveness of the fitting method. By comparing the fitting results with other particle swarm optimization algorithms, it is shown that the proposed particle swarm optimization algorithm can better avoid local optima and converge to the optimal values quickly. [ABSTRACT FROM AUTHOR]

The delayed product differentiation (DPD) recently rose as a hybrid production strategy able to overcome the main limits of make to stock (MTS) and make to order (MTO), guaranteeing the management of high variety and keeping low storage cost and quick response time by using the so-called product platforms. These platforms are a set of sub-systems forming a common structure from which a set of derivative variants can be efficiently produced. Platforms are manufactured and stocked following an MTS strategy. Then, they are customized into different variants, following an MTO strategy. Current literature proposes methods for platform design mainly using optimization techniques, which usually have a high computational complexity for efficiently managing real-size industrial instances in the modern mass customization era. Hence, efficient algorithms need to be developed to manage the product platforms design for such instances. To fill this gap, this paper proposes a two-step methodology for product platforms design and assessment in high-variety manufacturing. The design step involves the use of a novel modified algorithm for solving the longest common subsequence (LCS) problem and of the k-medoids clustering for the identification of the platform structure and the assignment of the variants to the platforms. The platforms are then assessed against a set of industrial and market metrics, i.e. the MTS cost, the variety, the customer responsiveness, and the variants production cost. The evaluation of the platform set against such a combined set of drivers enhancing both company and market perspectives is missing in the literature. A real case study dealing with the manufacturing of a family of valves exemplifies the efficiency of the methodology in supporting companies in managing high-variety to best balance the proposed metrics. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel low-frequency sensing solution based on the manipulation of the near-field distribution by employing a passive holographic magnetic metasurface, excited by an active RF coil placed in its reactive region. In particular, the sensing capability is based on the interaction between the magnetic field distribution produced by the radiating system and the magneto-dielectric inhomogeneities eventually present within the material under test. We first start from conceiving the geometrical set-up of the metasurface and its driving RF coil, adopting a low operative frequency (specifically 3 MHz) to consider a quasi-static regime and able to increase the penetration depth within the sample. Afterwards, since the sensing spatial resolution and performance can be modulated by controlling the metasurface properties, the required holographic magnetic field mask, describing the ideal distribution at a specific plane, is designed. Then, the amplitude and phase of currents, flowing in each metasurface unit-cell and required to synthetize the field mask, are determined through an optimization technique. Next, the capacitive loads necessary to accomplish the planned behavior are retrieved, by exploiting the metasurface impedance matrix. Finally, experimental measurements conducted on fabricated prototypes validated the numerical results, confirming the efficacy of the proposed approach to detect inhomogeneities in a medium with a magnetic inclusion in a non-destructive manner. The findings show that holographic magnetic metasurfaces operating in the quasi-static regime can be successfully employed for non-destructive sensing, both in industrial and biomedical fields, despite the extremely low frequencies. [ABSTRACT FROM AUTHOR]

The ECG (electrocardiogram) signals are an indicator of the electrical activity of the heart. Given its noninvasive nature ECG are an extremely popular medium for heart checkups. With the advent of modern technology, the world is moving toward a connected environment, and with the availability of wearable devices, there is an exponential increase in the transmission and storage of ECG and other physiological signals. It becomes necessary to compress the ECG signals for storage and transmission. Therefore, this paper presents an ECG compression algorithm based on discrete wavelet transform (DWT) and several nature-inspired optimization techniques. The ECG compression method uses optimization techniques to find the optimal values of wavelet design parameters and optimal threshold levels. In the proposed work, DWT is used to decompose the signal into sub-bands, and coefficients are obtained. Then, threshold values for each sub-band are selected using the optimization algorithms. After thresholding, the coefficients are further compressed using the modified run-length encoding (MRLE). The proposed work shows promising results and the original signal features are well preserved after reconstruction. The performance of this algorithm is tested by calculating different parameters such as percentage root-mean-square difference (PRD), quality score (QS), signal-to-noise ratio (SNR), and compression ratio (CR). This method is capable of providing a higher compression ratio with minimum distortion in ECG signal. [ABSTRACT FROM AUTHOR]

The spectral efficiency (SE) can approximately double when using full-duplex (FD) multiuser MIMO communications. However, there are difficulties because of multiuser interferences, self-interference (SI), and co-channel interference (CCI). To improve the SE of the downlink (DL), this paper proposes CCI-aware enhancement to SLNR (signal-to-leakage-and-noise-ratio) signal-to-leakage-and-noise-ratio (SLNR). It considers a suppressing filter at the receiver to cancel the interferences again designing a beamformer based on CCI-plus-noise covariance matrices for every user at the transmitting side. Additionally, we propose an improvement in the SLNR method by using SI-plus-noise covariance matrices to design uplink (UL) beamformers. Unlike zero-forcing and block-diagonalization, the SLNR approach serves numerous antennas at users and BS (base station). The total SE of the communication yielded using the optimized precoder, i.e., obtained from the SLNR-based precoding. To achieve maximum energy efficiency (EE), we use a power consumption model. Simulation results confirm that full-duplex performs well compared to half-duplex (HD) when the number of antennas at every user in uplink as well downlink channels grow, for all Rician factors, for slight powers of the CCI and SI, and a limited number of antennas at the BS. With the proposed scheme for given transmit power and circuit power, we demonstrate that FD has a higher EE than HD. [ABSTRACT FROM AUTHOR]

In this paper, we explore a class of Hermite–Hadamard integral inequalities for convex and m-convex functions. The Hölder inequality is used to create this class, which has a wide range of applications in optimization theory. Some trapezoid-type inequalities and midpoint error estimates are investigated. Inequalities for several q-special functions are highlighted. As particular cases, we have included several previous results. [ABSTRACT FROM AUTHOR]

In this paper, a novel population-based technique called dung beetle optimizer (DBO) algorithm is presented, which is inspired by the ball-rolling, dancing, foraging, stealing, and reproduction behaviors of dung beetles. The newly proposed DBO algorithm takes into account both the global exploration and the local exploitation, thereby having the characteristics of the fast convergence rate and the satisfactory solution accuracy. A series of well-known mathematical test functions (including both 23 benchmark functions and 29 CEC-BC-2017 test functions) are employed to evaluate the search capability of the DBO algorithm. From the simulation results, it is observed that the DBO algorithm presents substantially competitive performance with the state-of-the-art optimization approaches in terms of the convergence rate, solution accuracy, and stability. In addition, the Wilcoxon signed-rank test and the Friedman test are used to evaluate the experimental results of the algorithms, which proves the superiority of the DBO algorithm against other currently popular optimization techniques. In order to further illustrate the practical application potential, the DBO algorithm is successfully applied in three engineering design problems. The experimental results demonstrate that the proposed DBO algorithm can effectively deal with real-world application problems. [ABSTRACT FROM AUTHOR]

In this paper, production system (PS) of shale oil is optimized according to production data and indoor experiments, including core and fluid tests. Results showed that: ① Pressure drop rate at wellhead is a reasonable reference for the determination of post-fracture shut-in duration (PFSID). When pressure at a wellhead of horizontal well is relatively stable and the pressure drop less than 0.1 MPa per day for three consecutive days, PFSID ends; ② Flowback intensity of fracturing fluid affects the effectiveness of proppant underground, thus flowback intensity can be determined by the critical flow rate and safety factor of each proppant; ③ Flowback intensity should be varied during different development stages, which could be divided into four according to production gas and oil ratio(GOR) of a shale oil horizontal well: low, medium–high, high and high-low production GOR. During the stage of low production GOR, ratio of flow pressure and saturation pressure should be maintained greater than 1.0, and the initial daily liquid productivity for a hundred-meter oil-bearing lateral length in a horizontal well is 2.4 ~ 2.9 m3/d; and during the medium–high production GOR, high production GOR and high-low production GOR stages, the responding initial daily liquid productivity should be maintained between 0.8 ~ 1.0 or less than 0.8 respectively. [ABSTRACT FROM AUTHOR]

Manufacturing tolerances and uncertainties concerning material parameters, e.g., operating conditions or substrate permittivity are detrimental to characteristics of microwave components. The knowledge of relations between acceptable parameter deviations (not leading to violation of design specifications) and the nominal performance (not considering uncertainties), and is therefore indispensable. This paper proposes a multi-objective optimization technique of microwave components with tolerance analysis. The goal is to identify a set of trade-off designs: nominal performance versus robustness (quantified by the maximum input tolerance values that allow for achieving 100-percent fabrication yield). Our approach exploits knowledge-driven regression predictors rendered using characteristic points (features) of the component's response for a rapid evaluation of statistical performance figures, along with trust-region algorithm to enable low execution cost as well as convergence. The proposed methodology is verified with the use of three microstrip circuits, a broadband filter, and two branch-line couplers (a single- and a dual-band one). It is demonstrated that a Pareto set w.r.t. nominal performance and robustness objectives can be produced using only 40 to 60 EM simulations of the respective structure (per design). Reliability of the proposed algorithm is corroborated with the use of EM-based Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Many modern applications, both scientific and commercial, are deployed to cloud environments and often employ multiple types of resources. That allows them to efficiently allocate only the resources which are actually needed to achieve their goals. However, in many workloads the actual usage of the infrastructure varies over time, which results in over-provisioning and unnecessarily high costs. In such cases, automatic resource scaling can provide significant cost savings by provisioning only the amount of resources which are necessary to support the current workload. Unfortunately, due to the complex nature of distributed systems, automatic scaling remains a challenge. Reinforcement learning domain has been recently a very active field of research. Thanks to combining it with Deep Learning, many newly designed algorithms improve the state of the art in many complex domains. In this paper we present the results of our attempt to use the recent advancements in Reinforcement Learning to optimize the cost of running a compute-intensive evolutionary process by automating the scaling of heterogeneous resources in a compute cloud environment. We describe the architecture of our system and present evaluation results. The experiments include autonomous management of a sample workload and a comparison of its performance to the traditional automatic threshold-based management approach. We also provide the details of training of the management policy using the proximal policy optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios. [ABSTRACT FROM AUTHOR]

Multifactorial optimization (MFO) is a new optimization framework characterized by optimizing multiple optimization problems simultaneously, which has attracted much attention in the field of evolutionary computation. For a component problem including multiple optimization problems, positive knowledge transfer across related tasks can enhance global search capability, which is the key to make MFO outperform traditional optimization paradigms. In this paper, we propose a multifactorial brain storm optimization algorithm based on a direct search transfer mechanism and concave lens imaging learning strategy (MFBSO-DSCL). A direct search transfer mechanism is designed to transfer knowledge adaptively between tasks and accelerate the convergence of the algorithm. In addition, a concave lens imaging learning strategy is proposed to enhance the exploration ability of the algorithm and reduce the risk of falling into local optimum. To verify the effectiveness of the algorithm, MFBSO-DSCL is compared with some popular multifactorial optimization algorithms to solve 9 benchmark problems of the CEC2017 evolutionary multitasking optimization competition. Experimental results demonstrate the efficiency of MFBSO-DSCL. [ABSTRACT FROM AUTHOR]

Chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Because of its simplicity and low computational cost, the algorithm has been widely used to solve complex global optimization problem. But in the process of searching, it is easy to fall into the local optima, and the balance between exploitation and exploration cannot be realized well. In this paper, an adaptive chimp optimization algorithm called AChOA is proposed. Firstly, the Tent chaotic map is firstly used to initialize the chimp population to obtain a better initial solutions and improve convergence precision. Secondly, adaptive non linear convergence factor and adaptive weight are introduced in the global search stage, and the parameters vary adaptively according to the number of iterations and population diversity, so as to improve the population diversity. Thirdly, the Lévy flight strategy is introduced into the position update formula to mitigate the shortcomings of ChOA algorithm, such as finding the local optima rather than the global optima, and lack of balance between the exploitation and exploration process. Finally, a comparison with 10 famous algorithms on 19 benchmark functions of the solving accuracy and convergence speed of AChOA is presented. The results show that AChOA has the advantages of fast convergence speed, high solution accuracy. [ABSTRACT FROM AUTHOR]

Zafarana Wind Park is considered the largest wind farm in Middle East. By the year 2022, Zafarana wind farm will inject 545 MW to Egypt national electric network (ENEN). This wind farm has been connecting to ENEN in stages (eight stages) from ten years ago. Each stage represents a project of wind farm. Hence, the impact of the emergency condition of ENEN on the performance of the installed stage of Zafarana Wind Park should be studied. Moreover, determining the possible method to reduce the side effects of the emergency condition of ENEN on the installed stage of Zafarana Wind Park becomes an important issue. In this paper, a combination between Static Synchronous Series Compensator (SSSC) and crowbar (Cro-SSSC) is used to enhance the performance of the one installed stage of Zafarana Wind Park. The studied stage of Zafarana Wind Park is Zafarana Z1 wind farm (Z1). Zafarana Z1 wind farm represents that first stage of Zafarana Wind Park that was connected to ENEN. Hence, the paper takes Zafarana Z1 wind farm as the studied case to examine the impact of Cro-SSSC. The crowbar is used to protect the power components of the SSSC from the high current during grid fault condition. Also, in this paper multi-objective lightning attachment procedure optimization algorithm (LAPO) and the sine cosine algorithm (SCA) are used to evaluate and tune the control gains of SSSC and the value of crowbar's resistance. This step is important in order to determine the values of the control's gains of SSSC and the value of crowbar resistance which suit the studied system (one installed stage of Zafarana Wind Park). The sliding mode control (SMC) is used as an example of nonlinear control of SSSC. Also, a hybrid of LAPO and SMC is used to improve the performance of Zafarana Z1 during faults. The results of LAPO, SCA and SMC are compared during three-phase fault and single line to ground fault applied to the studied system. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic transmission expansion planning (TEP) in the presence of wind farms under uncertain load and wind source conditions when reliability and N-1 contingency is taken into consideration is proposed. The main aim of the suggested TEP is to minimize the total planning cost while satisfying techno-economic constraints. In this paper, reliability cost is also incorporated as the loss of load cost (LOLC) into the objective function in addition to the investment cost. In order to provide more accurate and real results, the N-1 contingency is also considered as a constraint in the TEP problem. Uncertainties for the wind source and loads in the TEP problem are modeled using a scenario-based approach and Monte Carlo simulation. Grey wolf optimization (GWO) is applied to solve the TEP problem, and it is compared to other optimization techniques. The simulation results are provided in various scenarios to confirm the efficiency of the suggested stochastic TEP. [ABSTRACT FROM AUTHOR]

In this paper, a battery charging model is developed for solar PV system applications. As a means of photovoltaic power controlling system, buck-boost converter with a Maximum Power Point Tracking (MPPT) mechanism is developed in this paper for maximum efficiency. This paper proposed a novel combined technique of hybrid Particle Swarm Optimisation (PSO) and Salp Swarm Optimization (SSO) models to perform Maximum Power Point Tracking mechanisms and obtain a higher efficiency for battery charging. In order to retrieve the maximum power from the PV array, the Maximum Power Point Tracking mechanism is observed which reaches the maximum efficiency and the maximum power is fed through the buck-boost converter into the load. The buck-boost converter steps up the voltage to essential magnitude. The energy drawn from the PV array is used for the battery charging by means of an isolated buck converter since the buck-boost converter is not directly connected to the battery. The Fractional Order Proportional Integral Derivative (FOPID) controller handles the isolated buck converter and battery to enhance the efficiency obtained through the Maximum Power Point Tracking mechanism. The simulation results show higher steady efficiency by using the hybrid PSOSSO algorithm in all stages. The battery is charged without losing the efficiency obtained from the hybrid PSOSSO algorithm-based Maximum Power Point Tracking mechanism. The higher efficiency was obtained as 99.99% at Standard Test Conditions (STC) and 99.52% at PV partial shading conditions (PSCs) by using the new hybrid algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a 3D nesting algorithm which combines the flower pollination algorithm (FPA) with the oriented bounding box (OBB) collision detection to solve the 3D packing of irregular shaped objects for powder-based additive manufacturing. In the powder-based 3D printing process, stacking multiple models most compactly in the build volume is an important task because no support structure is needed. Given a fixed printing area, the post-nesting build height directly impacts the printing cost and efficiency. To reduce the printing cost, 3D models must be packed as closely as possible and the build height must be minimized. This has been found to be a combinatorial optimization and an NP-hard problem. We propose to use the most recent and effective meta-heuristic optimization algorithm FPA, combined with the collision detection of printed objects set up as the optimization constraint using the OBB tree, to find the near-optimal 3D nesting solution. In FPA optimization, a significant amount of time is spent on collision detection. This paper uses the safety clearance distance to adaptively reduce the OBB tree subdivision, hence significantly reducing the computation of collision detection. As a result, the proposed method finds the model positions and rotations of the global solution, and generates the near-optimal solution in reasonable time. Finally, our method is compared with state-of-the-art commercial softwares. The results show that the proposed method produces lower build height with better efficiency. For real-world complex engine parts (30 different STL models and a total of 192,018 triangles), the computation time is only 160 s, and the build height is 12.4% and 13% better than the results from Netfabb and Magics, respectively. [ABSTRACT FROM AUTHOR]

One of the most dangerous snake species is the "Egyptian cobra" which can kill a man in only 15 min. This paper uses deep learning techniques to identify the Egyptian cobra bite in an accurate manner based on an image of the marks of the bites. We build a dataset consisting of 500 images of cobra bites marks and 600 images of marks of other species of snakes that exist in Egypt. We utilize techniques such as multi-task learning, transfer learning and data augmentation to boost the generalization and accuracy of our model. We have achieved 90.9% of accuracy. We must keep the availability and accuracy of our model as much as possible. So, we utilize cloud and edge computing techniques to enhance the availability of our model. We have achieved 90.9% of accuracy, which is considered as an efficient result, not 100%, so it is normal for the system to perform sometimes wrong classifications. So, we suggest to re-train our model with the wrong predictions, whereas the edge computing units, where the classifier task is positioned, resend the wrong predictions to the cloud model, where the training process occurs, to retrain the model. This enhances the accuracy to the best level after a small period and increases the dataset size. We use the quantum particle swarm optimization technique to determine the optimal required number of edge nodes. [ABSTRACT FROM AUTHOR]