In this paper, an enhanced human conception optimizer (EHCO) is proposed to solve the over-current relay coordination problem in a power distribution system. Generally, a randomly generated population is used in most ...
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In this paper, an enhanced human conception optimizer (EHCO) is proposed to solve the over-current relay coordination problem in a power distribution system. Generally, a randomly generated population is used in most of the available metaheuristic optimizers, where some of the initial search agents may be oriented along the boundary of the search space or in the opposite direction of a possible global solution in the search space. Such a random population will lead to slow convergence. To solve such an issue, in EHCO, a healthy initial population is proposed. To avoid local trapping problems, a wavelet mutation concept is also proposed. The convergence performance of the EHCO is tested with 29 benchmark functions and compared with some existing results. Two relay coordination problems with IEEE-8 bus 14 over-current relay and 7 over-current relay-based distribution systems are verified to validate the proposed optimizer for relay coordination problems. The simulation results show a 60-70% improvement in operating time for such distribution systems in comparison with existing results.
In recent years, fake news has become a global phenomenon due to its explosive growth and ability to leverage multimedia content to manipulate user opinions. Fake news is created by manipulating images, text, audio, a...
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In recent years, fake news has become a global phenomenon due to its explosive growth and ability to leverage multimedia content to manipulate user opinions. Fake news is created by manipulating images, text, audio, and videos, particularly on social media, and the proliferation of such disinformation can trigger detrimental societal effects. False forwarded messages can have a devastating impact on society, spreading propaganda, inciting violence, manipulating public opinion, and even influencing elections. A major shortcoming of existing fake news detection methods is their inability to simultaneously learn and extract features from two modalities and train models with shared representations of multimodal (textual and visual) information. Feature engineering is a critical task in the fake news detection model's machine learning (ML) development process. For ML models to be explainable and trusted, feature engineering should describe how many features used in the ML models contribute to making more accurate predictions. Feature engineering, which plays an important role in the development of an explainable AI system by shaping the features used in the ML models, is an interconnected concept with explainable AI as it affects the model's interpretability. In the research, we develop a fake news detector model in which we (1) identify several textual and visual features that are associated with fake or credible news;specifically, we extract features from article titles, contents, and, top images;(2) investigate the role of all multimodal features (content, emotions and manipulation-based) and combine the cumulative effects using the feature engineering that represent the behavior of fake news propagators;and (3) develop a model to detect disinformation on benchmark multimodal datasets consisting of text and images. We conduct experiments on several real-world multimodal fake news datasets, and our results show that on average, our model outperforms existing single-mod
An adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time-varying delays, concurren...
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An adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time-varying delays, concurrent actuator and sensor faults, system uncertainties, modelable external disturbance and operating noise. An uncertain time-varying state space model for ACSs of satellites is established, and sensor faults are equivalent to actuator-like faults. A disturbance observer is designed for estimating the modelable external disturbance, and an improved dwarf mongoose optimization (DMO) algorithm based on the Levy flight distribution is utilized to optimize the basis function of hyperbasis function neural networks to better estimate the augmented actuator faults that include the actuator fault and the actuator-like fault. Furthermore, an adaptive finite time composite fault-tolerant controller is proposed, which includes the delay-dependent feedback control law, disturbance estimation based-disturbance compensation law and the adaptive fault compensation law based on the augmented fault estimation using the improved DMO-hyper basis function neural network. The finite time boundness of the close-loop dynamics to the uncertainties, operating noise, and augmented actuator faults and the robustness of the measurement to the uncertainties, operating noise and augmented actuator faults are analyzed, and the observer and controller design is formulated as the linear matrix inequalities. Simulation examples for ACSs in different working conditions are considered to exhibit the proposed method's effectiveness. An adaptive finite time composite fault-tolerant controller is proposedLevy flight distribution is introduced to improve the performance of the dwarf mongoose optimization algorithmDisturbance observer is designed to estimate the modelable external disturbance
A highly integrated Earth-observing satellite can possess several maneuverable payloads to perform different missions simultaneously, which brings some challenges to the method of task scheduling. This paper addresses...
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A highly integrated Earth-observing satellite can possess several maneuverable payloads to perform different missions simultaneously, which brings some challenges to the method of task scheduling. This paper addresses the selection and scheduling problem of an agile satellite with several independently maneuverable optical payloads. Some differences compared to the traditional scheduling problem of agile satellites are presented and considered in a constrained optimization model. A two-stage method is proposed to accomplish the scheduling of the satellite and payloads in different stages. Clusters are generated from preprocessed tasks by a clique partition algorithm, and their centers are used to calculate the pointing direction of the satellite in the first stage. A multiobjective local search algorithm is introduced to schedule tasks in each selected cluster in the second stage. Considering the time-dependent property of the transition time, the problem of determining the start observation time is transformed into linear programming in a proposed insertion operator that guarantees the feasibility of generated solutions. Two types of instances are created and tested to demonstrate the effectiveness of the proposed method, and some analyses are conducted based on the experimental results.
This paper provides a tutorial on inverse design approaches for metasurfaces with a systematic analysis of the fundamental methodologies and underlying principles for achieving targeted optical properties. Traditional...
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This paper provides a tutorial on inverse design approaches for metasurfaces with a systematic analysis of the fundamental methodologies and underlying principles for achieving targeted optical properties. Traditionally, metasurfaces have been designed with extensive trial-and-error methods using analytical modeling and numerical simulations. However, as metasurface complexity grows, these conventional techniques become increasingly inefficient in exploring the vast design space. Recently, machine learning and optimization algorithms have emerged as powerful tools for overcoming these challenges and enabling more efficient and accurate inverse design. We begin by introducing the fundamentals of optical simulations used for forward modeling of metasurfaces and their relevance to inverse design. Next, we explore recent advancements in applying machine learning techniques such as neural networks, Markov decision processes, and Monte Carlo simulations, as well as optimization algorithms, including automatic differentiation, the adjoint method, genetic algorithms, and particle swarm optimizations, and show their potential to revolutionize the metasurface design process. Finally, we conclude with a summary of key findings and insights from this review.
With the expansion of the scale of renewable energy units connected to the power system, the problems of volatility and instability brought about by them are becoming more and more prominent. Compressed air energy sto...
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This article investigates the optimization H-infinity non-parallel distribution compensation (non-PDC) control issue for nonlinear systems under Takagi-Sugeno (T-S) fuzzy framework. First, sufficient conditions of des...
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This article investigates the optimization H-infinity non-parallel distribution compensation (non-PDC) control issue for nonlinear systems under Takagi-Sugeno (T-S) fuzzy framework. First, sufficient conditions of designing fuzzy non-PDC controller to assure asymptotic stability while maintaining H-infinity performance for studied systems are presented. Afterward, in the case of guaranteeing performance requirements, based on the feasible region of controller membership functions, a novel membership functions online learning algorithm utilizing gradient decent strategy is first proposed to adjust controller membership functions in real time to achieve a superior H-infinity performance. Compared with conventional non-PDC fuzzy control scheme, the actual response of interference attenuation performance can be decreased efficaciously. In the light of Lyapunov stability theory, sufficient condition is derived to ensure the error convergence of cost function. At last, two illustrative examples are provided to demonstrate the effectiveness and usefulness of the proposed online learning algorithm.
The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019. It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especiall...
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The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019. It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especially for engineering design problem. In this paper, a comprehensive survey conducted about FDO and its applications. Consequently, despite of having competitive performance of FDO, it has two major problems including low exploitation and slow convergence. Therefore, a modification of FDO (MFDO) is proposed for solving FDO issues. MFDO used two methods to enhance the performance of FDO: firstly, optimizing the range of weight factor between 0 and 0.2 which is used for finding fitness weight. Secondly, using sine cardinal mathematical function to update fitness weight and pace which is referred to the speed of the bees. To evaluate the performance of MFDO, 19 classical benchmark functions and CEC2019 benchmark functions are used. MFDO compared against all the enhancement of FDO and also it is compared with Grey Wolf optimization (GWO), Chimp optimization algorithm (ChOA), Genetic algorithm (GA), and Butterfly optimization algorithm (BOA). Statistical results proved that MFDO achieved significant performance compared to other algorithms. Finally, MFDO is used to solve three applications: Welded Beam Design (WDB), Pressure Vessel Design (PVD), and Spring Design Problem. Results proved that MFDO outperformed well in solving these applications against FDO, Gravitational Search algorithm (GSA), GA, and Grasshopper optimization algorithm (GOA).
Wind power forecasting is critical to the safe running of the power grid. However, due to the strong intermittence and instability of wind, reliable forecast of wind power remains a significant difficulty. In this stu...
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Wind power forecasting is critical to the safe running of the power grid. However, due to the strong intermittence and instability of wind, reliable forecast of wind power remains a significant difficulty. In this study, a novel multivariable machine learning hybrid prediction system that incorporates data preprocessing, prediction, and multi-objective system optimization is designed to quantify the certainty and uncertainty of wind power. To increase the quality of data input, the data preparation module performs outlier tests based on the correlation between wind power and wind speed, as well as feature extraction, on the original data. In the prediction process, this paper offers an incremental kernel extreme learning machine (IK-elm), the parameters of which are set synchronously by an enhanced multi-objective optimization technique (MOCEHHO) developed in this paper. It overcomes the restrictions of duplicated hidden layer nodes and low learning efficiency caused by classic ELM and successfully maximizes the model's prediction capabilities. The simulation results on four datasets from Turkish wind farms show that the hybrid forecasting system outperforms the benchmark and may be utilized as a useful tool for wind power forecasting. (C) 2021 Elsevier Ltd. All rights reserved.
Big data is a vast amount of structured and unstructured data that must be dealt with on a regular *** reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information...
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Big data is a vast amount of structured and unstructured data that must be dealt with on a regular *** reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed *** tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning *** achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in *** performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the *** simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data ***,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data ***-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies.
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