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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Hasan, Mahmudul Acar, Pinar Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Uncertainty in the microstructures has a significant influence on the material properties. The microstructural uncertainty arises from the fluctuations that occur during thermomechanical processing and can alter the expected material properties and performance by propagating over multiple length-scales. It can even lead to the material failure if the deviations in the critical properties exceed a certain limit. We introduce a linear programming (LP) based method to quantify the effects of the microstructure uncertainty on the desired material properties of the Titanium-7wt%Aluminum (Ti-7Al) alloy, which is a candidate material for aerospace applications. The microstructure is represented using the orientation distribution function (ODF) approach. The LP problem solves for the mean values and covariance of the ODFs that maximize a volume-averaged linear material property. However, the analytical procedure is not applicable for maximizing non-linear material properties where microstructural uncertainties are present. Therefore, an artificial neural network (ANN) based sampling method is developed to estimate the mean values and covariance of the ODFs that satisfy design constraints and maximize the volume-averaged non-linear material properties. A Couple of other design problems are also illustrated to clarify the applications of the proposed models for both linear and non-linear properties.

关键词： Hyperelastic Material Machine Learning Artificial Neural Network Material Properties optimization algorithm Thermomechanical Processing Mechanical Properties Extended Finite Element Method Mathematical Models Probability Distribution

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Log-power PIESC for wind turbine power maximization below-rated wind conditions

Log-power PIESC for wind turbine power maximization below-ra...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Kumar, Devesh Rotea, Mario A. Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA Univ Texas Dallas Ctr Wind Energy Richardson TX 75080 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75080 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper proposes a Log-Power Proportional-Integral Extremum Seeking Control (LP-PIESC) framework for maximizing the power capture of a wind turbine operating belowrated wind speeds;i.e., the so-called region-2 of a turbine's power curve. Extremum seeking control (ESC) has emerged as a viable algorithm to maximize energy capture for a wind turbine operating in region-2. Despite the encouraging results of early ESC strategies, the basic algorithm suffers from slow and inconsistent convergence behavior under changing wind speed within region-2. Recently, it has been shown that replacing the power signal with its logarithm yields an algorithm with robust and predictable performance despite variations in the mean wind speed. Also, new studies have suggested that replacing conventional ESC with proportional plus integral ESC (PIESC) results in faster convergence to optimal conditions. In the current paper, the idea of log-power feedback is merged with the PIESC scheme and is applied to tune the parameters of the region-2 torque controller for the NREL 5-MW turbine reference model. The results of this new algorithm are compared with the conventional ESC using NREL OpenFAST simulations. The simulations performed under different turbulent intensity cases demonstrate the superiority of the log-power feedback PIESC over conventional log-power ESC in below-rated wind conditions.

关键词： Power Coefficient Linear Time Invariant Large Eddy Simulation Blade Pitch Turbulence Intensity Real Time optimization Control algorithm Aerodynamic Efficiency optimization algorithm Energy Production

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Communication-efficient hierarchical distributed optimization for multi-agent policy evaluation

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JOURNAL OF COMPUTATIONAL SCIENCE 2021年 49卷

作者： Ren, Jineng Haupt, Jarvis Guo, Zehua Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Beijing Inst Technol Beijing 100081 Peoples R China

Policy evaluation problems in multi-agent reinforcement learning (MARL) have attracted growing interest recently. In this setting, agents collaborate to learn the value of a given policy with private local rewards and jointly observed state-action pairs. However, existing fully decentralized algorithms treat each agent equally, without considering the communication structure of the agents over a given network, and the corresponding effects on communication and computation efficiency. In this paper, we propose a hierarchical distributed algorithm that differentiates the roles of each of the agents during the evaluation process. This method allows us to freely choose various mixing schemes (and corresponding mixing matrices that are not necessarily symmetric or doubly stochastic), in order to reduce the communication and computation cost, while still maintaining convergence at rates as fast as or even faster than the previous distributed algorithms. Theoretically, we show the proposed method, which contains existing distributed methods as a special case, achieves the same order of convergence rate as state-of-the-art methods. Extensive numerical experiments on real datasets verify that the performance of our approach indeed improves - sometimes significantly - over other advanced algorithms in terms of convergence and total communication efficiency.

关键词： Communication efficiency Hierarchical Distributed algorithm optimization algorithm Multi-agent policy evaluation

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Unstructured Anisotropic Mesh Adaptation for Quads Based on a Local Error Model

Unstructured Anisotropic Mesh Adaptation for Quads Based on ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： MacLean, Keigan Nadarajah, Siva McGill Univ Dept Mech Engn Montreal PQ H3A 0C3 Canada

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper presents a method for anisotropic adaptation for all-quad meshes. The goal is to provide a means of reducing the error associated with computational fluid dynamic simulations while limiting the added cost. This is done based on a local error model in order to construct and optimize target element sizes and shapes at each point. A global optimization is then used to find a distribution of degrees of freedom which targets error reductions throughout the computational domain. Additionally, this work will also consider the implementation of a fully unstructured quad mesh generator based on an energy minimization in the anisotropic p norm. This will serve to generate elements matching target sizes and orientations from the aforementioned estimates. Overall, the work will combine these aspects, forming an adaptive framework for use in solving high-order problems with the Discontinuous Galerkin method.

关键词： Discontinuous Galerkin Method Mesh Generation Advection Energy Derivative Data Structures optimization algorithm Stall Behavior Computer Graphics Linear Mapping

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An Improved Approach for Iterative Nodes Localization by Using Artificial Bee Colony

An Improved Approach for Iterative Nodes Localization by Usi...

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International Conference on Neural Networks, Information and Communication Engineering

作者： Gu, Shenkai Cheng, Li Wang, Jing Li, Xianglong Nanjing Tech Univ Coll Comp Sci Nanjing 211816 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Jingling Inst Technol Nanjing 211816 Peoples R China

ISBN: (数字)9781510647381

ISBN: (纸本)9781510647381;9781510647374

Recently, many artificial bee colony-based localization algorithms have been used to obtain more accurate location information. However, the situation of co-linear anchor nodes and measurement errors lead to the problem of flip ambiguities. In the current researches on the localization algorithm of artificial bee colony, the lack of detection and correction mechanism for flipped nodes causes large errors. In this paper, we propose an improved artificial bee colony localization algorithm. We firstly qualify the initial search area based on the measured distance. Then, we introduce a robustness criterion to evaluate the localization nodes, which will avoid using the possible flipped nodes as assistant anchor nodes and thus improve the localization accuracy. Simulation results show that our algorithm can effectively avoid the flip ambiguities and achieve higher accuracy than existing ABC-based and other metaheuristic-based localization algorithms.

关键词： wireless sensor network localization range-based optimization algorithm flip ambiguities detection

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An Integer Linear Programming Approach for Phasor Measurement Unit Placement

An Integer Linear Programming Approach for Phasor Measuremen...

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IEEE URUCON Conference (IEEE URUCON)

作者： Ferraz, Renato S. F. Ferraz, Rafael S. F. Rueda-Medina, Augusto C. Paiva, Marcia H. M. Univ Fed Espirito Santo UFES PPGEE Postgrad Program Elect Engn Vitoria ES Brazil

ISBN: (纸本)9781665424431

Phasor Measurement Units (PMUs) are essential devices in the monitoring and protection of modern power systems. However, its prohibitive cost may limit the wide installation of this device in the networks. For this reason, in this paper, the problem of optimal PMU placement was solved using Integer Linear Programming and some metaheuristic techniques, such as, the Genetic algorithms, Particle Swarm optimization and Cuckoo Search. In addition, the test feeders were modeled as graphs, in order to introduce the domination concepts of graph theory in this problem formulation and, using the upper and lower bounds of the domination number, evaluate the optimization algorithms results. The proposed methodology was performed using the IEEE 13, 34, 37 and 123 node test feeders, and it can be generalized for feeders with higher dimensions. Based on the results, it is possible to conclude that the Integer Linear Programming is the most suitable for this application, presenting the lowest number of PMUs and the fastest convergence.

关键词： Graph theory optimization algorithm Phasor Measurement Unit Power system observability

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Improved political optimizer for complex landscapes and engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷

作者： Askari, Qamar Younas, Irfan GIFT Univ Dept Comp Sci Gujranwala Pakistan Natl Univ Comp & Emerging Sci FAST Sch Comp Lahore Pakistan

Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon's rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms.

关键词： Political optimizer Global optimization optimization algorithm Improved algorithm Meta-heuristic Engineering optimization

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Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting optimization for human to robot interaction

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KNOWLEDGE-BASED SYSTEMS 2021年 220卷

作者： Gawali, Mahendra Bhatu Gawali, Swapnali Sunil Kopargaon Savitribai Phule Pune Univ SRESs Sanjivani Coll Engn Dept Info Tech Pune MS India

Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting optimization algorithm and Chicken Swarm optimization termed as Chicken Swarm-plus Deer Hunting optimization algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting optimization algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Skill transfer knowledge Robotic arm movement Reinforcement Learning Artificial Neural Network Reward maximization Chicken Swarm-based Deer Hunting optimization algorithm Root mean squared error

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Short-term multi-objective optimal scheduling of the integrated power grid-abandoned coal mine energy system

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RESULTS IN ENGINEERING 2024年 22卷

作者： Liu, Jing Li, Rui Wu, Tao China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China

In order to meet the urgent needs of upgrading the coal industry, energy exploitation of abandoned coal mines which may be rich in water resources and renewable resources brooks no delay. At present, the framework design and simple energy flow analysis for the reuse of abandoned coal mine resources have been basically formed. However, profound studies on the complex interaction behavior and the coordinated optimal scheduling of the integrated power grid-abandoned coal mine energy system (IPGACMES) with distributed renewable energy and underground pumped hydro energy storage (UPHES) are still lacking. In this paper, a novel multi-objective optimal coordinated scheduling model of IPGACMES is proposed aiming to achieve the lowest operation cost and the minimal carbon emissions. Especially, load balance constraint as well as operation constraints of thermal units, wind/PV units, UPHES, power-to-gas (P2G), power to hydrogen (P2H) and hydrogen fuel cells (HFC) are all considered. Different case studies have been carried out to illustrate the feasibility and effectiveness of the presented optimal scheduling model. Better economic and environmental benefits are achieved in terms of a 3.52 % decline in operation cost and a 4.58 % reduction in carbon emissions. In addition, the renewable energy curtailment is decreased 83.7% whereby the use of P2G and P2H units.

关键词： Abandoned coal mines Integrated energy system optimization algorithm P2G/P2H Renewable energy

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An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Nicholas, Neomi Nelin Nirmalrani, V. Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

This study aims to create a highly effective system for classifying email spam, with the key objective of improving performance and accuracy in classification. Rigorous pre-processing techniques, including lemmatization and tokenization, are applied to refine the dataset. The impact of optimal feature selection on deep learning algorithm stability with varying training datasets is investigated and to improve classifier performance, pre-stages such as feature extraction and selection using bag of words are utilized. The hybrid model is further enhanced by combining feature extraction and classification algorithms with optimization. The inclusion of multiple features, such as n-gram features, addresses the 'Curse of Dimensionality,' and optimal feature selection is employed to improve the spam detection process. The proposed system undergoes three main modifications: multiple feature extraction, feature selection, and an enhanced hybrid model, all aimed at improving spam detection accuracy. The e-mail classification phase involves mapping between training and testing sets, with deep learning algorithms utilized for feature extraction and classification. The recently introduced Sand Cat Swarm optimization algorithm is employed to refine weights during each epoch, enhancing accuracy and minimizing loss. The proposed system extends its capabilities to identify phishing attacks within the network, offering a comprehensive approach to email security. © 2024

关键词： Bag of words Curse of dimensionality Email spam classification optimization algorithm Sand Cat Swarm Tokenization

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