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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2025年第3期6卷 749-758页

作者： Huang, Jianhui Luk, Kwai-Man City Univ Hong Kong Dept Elect Engn State Key Lab Terahertz & Millimeter Waves Hong Kong Peoples R China

A simple and effective method, by using '0-1' excitations, to reduce the sidelobe level (SLL) for a wideband magnetoelectric (ME) dipole antenna array is proposed. First of all, the multiple-population genetic algorithm (MPGA) is utilized to search optimal '0-1' excitations for SLL reductions. Then, the '0' and '1' excitation can be achieved by incorporating the absorbing element embedded with a resistor and an ME dipole antenna element, respectively. Different from the traditional tapered excitation techniques, the proposed array utilizes an equal power divider to distribute the power to each element. Finally, a planar 16 x 16 stripline-fed antenna array according to the optimized array configuration is designed, fabricated, and measured. An overlapped impedance bandwidth of 57% (10-18 GHz) is achieved with the standing wave ratio (SWR) less than 2, and the SLLs are lower than -17.5 dB across 40% bandwidth both in E- and H-planes. In addition, the measured realized gain of this array prototype is up to 26.2 dBi with a high realized aperture efficiency ranging from 61% to 73%. The proposed lightweight, high-gain and high-integration Ku-band antenna array with low SLL characteristics shows great potential in satellite communications.

关键词： Antenna arrays Wideband Apertures Feeds Dipole antennas Antenna measurements optimization Resistors Power dividers Genetic algorithms Low sidelobe level (SLL) magnetoelectric (ME) dipole stripline Ku-band wideband optimization algorithm

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Multi-objective optimization of Electric Pumps for Hybrid Rocket Engines

Multi-objective Optimization of Electric Pumps for Hybrid Ro...

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AIAA Aviation Forum

作者： Ferrero, A. Gedda, G. Masseni, F. Pastrone, D. Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

A multi-objective optimization of an electric pump for hybrid rockets is carried out through an evolutionary algorithm. The pump is modeled by means of a meanline flow method. For given values of required head and flow rate, some of the pump design parameters are varied to minimize the power and the torque to be delivered by the motor. The two minima do not coincide because of the dependence of pump power losses on shaft speed for different geometries;Pareto-optimal solutions are found near the upper bound of the chosen speed range.

关键词： Hybrid Rocket Engines Particle Swarm optimization Hybrid Rocket Drive Pump Liquid Propellant Rocket Propellant Electric Power Multidisciplinary optimization optimization algorithm Mass Flow Rate

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Resource optimization algorithm Based on Evaluation Decision mModel of Information Entropy

Resource Optimization Algorithm Based on Evaluation Decision...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Huang, Limin He, Guosong Chen, Zhi Hubei Univ Sci & Technol Modern City & Dev Res Inst Coll Resources & Environm Sci Xianning 437100 Peoples R China

ISBN: (纸本)9783038351153

Xianning is located in the fourth pole of economic growth core area of The Central Triangle, through the location entropy analysis method we can know that, tourism industry is an advantaged industry in Xianning and the adjacent region of The Central Triangle, and have their own advantages and characteristics of tourism resources. According to the space constraint issues on function realization of advantaged tourism resources in Xianning, space breakthrough strategy based on The Central Triangle pattern has been presented.

关键词： Information entropy optimization algorithm Resource utilization Function division Weighted objective Radiation

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Calculation of the mechanical properties of high-performance concrete employing hybrid and ensemble-hybrid techniques

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STRUCTURAL CONCRETE 2024年第5期25卷 3765-3787页

作者： Zhang, Leilei Zhao, Yuwei Zhengzhou Shengda Univ Zhengzhou 450000 Henan Peoples R China Sun Life Co EED Baku Azerbaijan

This study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structures in civil engineering. In this regard, obtaining the estimation of the mechanical properties of HPC is complex energy and time-consuming. Due to this, an observed database was compiled, including 168 datasets for CS and 120 for TS. This database trained and validated two machine learning models: SVR and RT. The models combine the prediction outputs from the meta-heuristic algorithms to build hybrid and ensemble-hybrid models, which include dwarf mongoose optimization, PPSO, and moth flame optimization. According to the observed outputs, the ensemble models have great potential to be a recourse to deal with the overfitting problem of civil engineering, thus leading to the development of more supportable and less polluting concrete structures. This research significantly improves the efficiency and accuracy of predicting vital mechanical properties in high-performance concrete by integrating machine learning and metaheuristic algorithms, offering promising avenues for enhanced concrete structure design and development.

关键词： compressive and tensile strength high-performance concrete optimization algorithm regression tree support vector regression

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An enhanced human conception optimizer for over-current relay coordination problem in distribution system

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ELECTRICAL ENGINEERING 2024年 1-15页

作者： Acharya, Debasis Rizk-Allah, Rizk M. Brainware Univ Dept Comp Sci & Engn Cybersecur & Data Sci Barasat India Menoufia Univ Fac Engn Basic Engn Sci Dept Menoufia 32511 Egypt

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.

关键词： optimization algorithm Time dial setting (TDS) Plug setting (PS) Coordination time interval (CTI)

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Feature importance in the age of explainable AI: Case study of detecting fake news & misinformation via a multi-modal framework

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期317卷 401-413页

作者： Kumar, Ajay Taylor, James W. EMLYON Business Sch 23 Ave Guy Collongue F-69130 Ecully France Univ Oxford Said Business Sch Park End St Oxford OX1 1HP England

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

关键词： Analytics Feature selection Machine learning Explainable data analytics optimization algorithm

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Optimal enhanced backstepping method for trajectory tracking control of the wheeled mobile robot

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第6期45卷 2762-2790页

作者： Zhang, Ke Chai, Bin Tan, Minghu Northwestern Polytech Univ Sch Astronaut Xian Peoples R China

This paper proposes a novel control method applied to the trajectory tracking of the wheeled mobile robot. This method can solve the tracking difficulty caused by the non-holonomic constraint and the under-actuated properties. First, according to the kinematic and dynamic tracking error models, the desired velocities for trajectory tracking purposes are obtained. Second, the control method, consisting of an enhanced backstepping controller with fewer gains and an optimization algorithm, is designed. The actual trajectory of the mobile robot is exactly converged and kept at the predefined reference trajectory by the operation of this method. Next, this method with globally uniformly asymptotically stability is theoretically analyzed. Finally, simulation comparisons and physical experiments are conducted in different scenarios. The tracking performance is evaluated by three metrics, namely convergence speed, tracking accuracy and robustness, thus verifying the effectiveness of the novel control method. An optimal enhanced backstepping tracking control method is proposed for wheeled mobile robot. The control method has fast convergence speed, high tracking accuracy and robustness. The actual trajectory converges rapidly and stably remains on the reference trajectory under the action of the control method. image

关键词： control method enhanced backstepping controller optimization algorithm trajectory tracking wheeled mobile robot

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Neuro-Adaptive Finite Time Composite Fault Tolerant Control for Attitude Control Systems of Satellites

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RADIO SCIENCE 2024年第1期59卷 1-29页

作者： Wang, Yingdong Liang, Tiantian Yang, Jianxiong Liu, Jian Dalian Jiaotong Univ Sch Automat & Elect Engn Dalian Peoples R China

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

关键词： attitude control systems fault-tolerant control time-varying delays adaptive finite time neural networks optimization algorithm

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Rough Sets Hybridization with Mayfly optimization for Dimensionality Reduction

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Computers, Materials & Continua 2022年第10期73卷 1087-1108页

作者： Ahmad Taher Azar Mustafa Samy Elgendy Mustafa Abdul Salam Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer Studies Arab Open UniversityCairoEgypt Faculty of Information Technology and Computer Science Nile UniversityShikh ZaidEgypt

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.

关键词： Dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection

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A new energy-aware technique to improve the network lifetime of wireless Internet of Things using a most valuable player algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第6期27卷 7251-7271页

作者： Xiao, Yongjun Voronkova, Daria K. Hubei Engn Univ Sch Phys & Elect Informat Engn Xiaogan 432000 Peoples R China Gulf Univ Sci & Technol Dept Math & Nat Sci Mishref Campus Kuwait 32093 Kuwait Bauman Moscow State Tech Univ Moscow Russia

With the fast evolution of Internet of Things (IoT) applications, Wireless Sensor Networks (WSNs) have become a crucial part of modern infrastructure. The efficient provision of services and wiser use of resources are currently of great importance. WSNs consist of several sensor nodes that collaborate to monitor and send data to a central location known as a sink. The sink, also called a base station, serves as the endpoint for data transmission in each round. However, due to the limited computation, storage, and energy resources of sensor nodes, they often face challenges in changing clusters. Optimal selection of a node, aimed at minimizing network fragmentation and enhancing energy utilization, necessitates a sophisticated evaluation and computational procedure, demanding a substantial energy investment. Subsequently, the task at hand is the development of a system facilitating the connection of remote sensing sources to WSNs with minimal energy consumption. The primary goals of WSNs based on the IoT revolve around extending network longevity and enhancing energy efficiency. In the realm of IoT-based WSNs, where the efficiency of data collection and management is paramount, cluster-based methodologies have demonstrated their effectiveness. This investigation proposes the implementation of a most valuable player algorithm (MVPA) specifically tailored for IoT-based WSNs, taking into account diverse factors influencing node energy and network lifespan. The MVPA is a highly competitive optimization method that converges faster (with fewer function evaluations) and has a greater overall success rate. In this case, the optimum cluster head for an IoT-based WSN was chosen using an MVPA to maximize energy savings. Simulation results demonstrate that the recommended strategy, when compared to other current methods, increases the network lifetime by using the minimum amount of energy needed to function.

关键词： IoT-based WSN Cluster-head Energy optimization algorithm Most valuable player algorithm

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