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7th IEEE International Symposium on Smart Electronic Systems (IEEE-iSES)

作者： Solanki, Brijraj Singh Kumawat, Renu Srinivasan, Seshadhri Manipal Univ Jaipur Elect & Commun Engn Jaipur Rajasthan India Kalasalingam Acad Res & Educ Instrumentat & Control Engn Srivilliputhur Tamil Nadu India

ISBN: (纸本)9781728187532

Emerging trends, development and growth of industrial-networked control systems (i-NCSs) with real-time communication network drive it more susceptible to malicious intended intrusion and attack. Due to numerous advantages such as reduced maintenance, ease to install, ease of diagnosis and system wiring draws attention to use in various industrial and critical fields. The control performance and robust stability of NUS is directly related to reliable and successful transmission of critical information's. So in this paper an approach is illustrated to alleviate the effects and avert the unwanted intended intrusive data through the designing of stability conditions and optimized control policies. An unwanted intrusion effects also forced us to design a controlled system, which is hard to be estimated by attackers, through the applications of optimization algorithms.

关键词： Networked control system (NCS) denial of service (DoS) attacks optimal control optimization algorithm delays packer loss

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Exergo-economic analysis and multi-objective multi-verse optimization of a solar/biomass-based trigeneration system using externally-fired gas turbine, organic Rankine cycle and absorption refrigeration cycle

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APPLIED THERMAL ENGINEERING 2021年 191卷 116889-116889页

作者： Nazari, Navid Mousavi, Seyedmostafa Mirjalili, Seyedali Univ Melbourne Fac Engn & Informat Technol FEIT Dept Infrastruct Engn Renewable Energy & Energy Efficiency Grp Melbourne Vic 3010 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia

In the present work, an innovative configuration is proposed based on solar pre-heating concept and biomass direct-combustion for combined production of electricity, hot water and cooling load. The studied system consists of an externally fired gas turbine, a trans-critical organic Rankine cycle, and a Li-Br/H2O absorption refrigeration cycle. The proposed system is thoroughly analyzed from the energy, exergy, and exergo-economic viewpoint. The results of the exergo-economic analysis demonstrate that the energy and exergy efficiencies of the reference case are 55.56% and 20.38% and the product cost rate is 26.4 $/h. Comparing the thermodynamic results to the literature, it is proved that the proposed system generates a considerably higher amount of power, heating, and cooling load which results in an improvement of energy efficiency by about 10%. In further, the main design parameters of each cycle are parametrically assessed to gain a comprehensive understanding of the system behavior. Finally, a new multi-objective optimization algorithm called multi-objective multi-verse optimizer (MOMVO) is employed to maximize exergy efficiency and minimize the product cost rate of the system. Compared to the base case, TOPSIS implementation revealed that the optimal final solution of the Pareto-frontier found by the MOMVO has about 9% higher second law efficiency while the product cost rate is decreased by around 6%. This result proved the better performance of the MOMVO compared to other conventional evolutionary-based multi-objective optimzation algorithms.

关键词： Biomass Solar thermal Exergo-economic CCHP Multi-objective multi-verse optimizer (MOMVO) optimization algorithm

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Dynamic data driven sensor placement for enabling capability estimation of self - aware aerospace vehicles

Dynamic data driven sensor placement for enabling capability...

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AIAA Aviation Forum and Exposition

作者： Singh, Arjun Allaire, Douglas Texas A&M Univ J Mike Walker Dept Mech Engn 66 College Stn TX 77843 USA

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

Unmanned aerial vehicles (UAV) are utilized in numerous industries and in recent years with the advent of learning techniques, the focus is now on developing Self - aware UAVs that rely on an array of environmental sensors to replace a pilots awareness of the structural capability of the UAV and provide time critical analysis of the sensor data to make complex decisions in real time. Hence a self-aware UAV is capable of dynamically and autonomously sense its structural state and act accordingly to perform the required task. This work proposes a data driven approach to producing estimates of capability for a self - aware UAV and optimizing the placement of sensors to minimize the error between true and predicted capability. This process involves using high physics-based models such as ASWING in tandem with Akselos modeler, a cloud based FEA solver, to produce an offline library that comprises of damage states along with capabilities corresponding to different kinematic states of a UAV. Further this generated information is used to create a classification model which is used to predict the capability for real time data. The classification model serves as an enabler for the optimization algorithm to measure the error value between the true and the predicted capability of the UAV to determine the optimum sensor placement. We demonstrate the improvement in performance through a comparison between optimum placement and the standard placement of sensors. We also provide evidence of proof of concept of how dynamic sampling of information can improve the process of capability estimation in self - aware aerospace vehicles.

关键词： Sensor Technology Unmanned Aerial Vehicle optimization algorithm Genetic algorithm Naive Bayes Classifier Structural Health Monitoring Allowable Stress Structural Load Shear Strength Bayes Theorem

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Design of a Dual Thrust Solid Motor using Star Grains

Design of a Dual Thrust Solid Motor using Star Grains

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

作者： El-Naggar, Mostafa Belal, Hatem Abdalla, Hamed M. Egyptian Armed Forces Cairo Egypt Mil Tech Coll Cairo Egypt

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Dual thrust profile using single chamber offers great opportunity in extending range of tactical missiles by mitigating the drawbacks of other dual thrust motor arrangements. This method for achieving dual thrust profile can be implemented using different grains arrangement e.g. tubular-tubular, star tubular, star-end burning etc. or different propellant compositions. In this paper, Star-star configuration is presented. The advantage of the proposed arrangement is higher volumetric loading compared to other arrangement. In addition, effects of different geometric parameters on dual thrust profile are investigated. Finally Genetic algorithm optimization module is applied in order to fit the design into certain objective mission. Results showed that number of star points and star angular fraction affect only transition phase, while geometry of narrow star affect both booster and sustainer phase.

关键词： Dual Thrust Composite Solid Propellant Genetic algorithm Tactical Missiles Solid Rocket Motor Universal Gas Constant Burning Rate Space Shuttle Solid Rocket Booster optimization algorithm Stagnation Pressure

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Uncertainty Quantification of the Metallic Microstructures with Analytical and Machine Learning Based Approaches

Uncertainty Quantification of the Metallic Microstructures w...

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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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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 106945-106945页

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

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JOURNAL OF COMPUTATIONAL SCIENCE 2021年 49卷 101280-101280页

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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In situ stress prediction model in complex geology: A hybrid GA-ANN with nonlinear boundary condition

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Journal of Rock Mechanics and Geotechnical Engineering 2025年

作者： Rong, Peng Zuo, Yujun Lin, Jianyun Zheng, Lujing Pan, Chao Sun, Wenjibin Chen, Qinggang Chen, Bin College of Resources and Environmental Engineering Guizhou University Guiyang 550025 China Mining College of Guizhou University Guizhou University Guiyang 550025 China Guizhou Jinfeng Mining Limited Southwest of Guizhou Guiyang 562204 China

In regions characterized with great mining depths, complex topography, and intense geological activities, solely relying on lateral pressure coefficients or linear boundary conditions for predicting the in situ stress field of rock bodies can induce substantial deviations and limitations. This study focuses on a typical karst area in Southwest Guizhou, China as its research background. It employs a hybrid approach integrating machine learning, numerical simulations, and field experiments to develop an optimization algorithm for nonlinear prediction of the complex three-dimensional (3D) in situ stress fields. Through collecting and fitting analysis of in situ stress measurement data from the karst region, the distributions of in situ stresses with depth were identified with nonlinear boundary conditions. A prediction model for in situ stress was then established based on artificial neural network (ANN) and genetic algorithm (GA) approach, validated in the typical karst landscape mine, Jinfeng Gold Mine. The results demonstrate that the model's predictions align well with actual measurements, showcasing consistency and regularity. Specifically, the error between the predicted and actual values of the maximum horizontal principal stress was the smallest, with an absolute error 0.01–3 MPa and a relative error of 0.04–15.31%. This model accurately and effectively predicts in situ stresses in complex geological areas. © 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

关键词： Geomechanics In situ stress prediction Machine learning Nonlinear boundary conditions optimization algorithm

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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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