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genetic optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM)

INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2014年第1期12卷 205-214页

作者： Chen, Xi Zhong, Wenqi Wang, Tiancai Liu, Fei Zhang, Zhi Southeast Univ Key Lab Energy Thermal Convers & Control Minist Educ Sch Energy & Environm Nanjing 210096 Jiangsu Peoples R China Nanjing Iron & Steel Grp Corp Nanjing 210035 Jiangsu Peoples R China

Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

关键词： genetic optimization algorithm support vector machine (SVM) pellet shaft furnace

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Inverse determination of modeling parameters to consider inhomogeneities of semicrystalline thermoplastics in structure simulations

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ARCHIVE OF APPLIED MECHANICS 2013年第6期83卷 889-897页

作者： Kaiser, Jan-Martin Stommel, Markus Univ Saarland Lehrtsuhl Polymerwerkstoffe Saarbrucken Germany

Two semicrystalline thermoplastics, an isotactic polypropylene (iPP, LynedllBasell Moplen HP501L) and a polyethylene-high-density (PE-HD, LynedllBasell Hostalen GC7260), were selected to approve a new approach. The developed approach allows the inverse determination of the amorphous and crystalline mechanical as well as the crystalline geometric constituents' properties. Commonly, these properties are unknown in structure simulations, and hence, the application of micromechanical models to the inhomogeneous microstructure of semicrystalline thermoplastics is restricted. Rather, a homogenous microstructure is assumed, and only one Young's modulus and Poisson's ratio are used in calculations. Thus, the quality and reliability of simulations are limited. In the current study, a new approach was exemplarily conducted for the inverse determination of the required properties by combining a Mori-Tanaka mean field approach with a genetic optimization algorithm. Conclusive results were achieved for both polymers. According to the results, the attained geometric parameters for the crystalline constituents resemble the aspect ratio of the spherulite diameter and the long period of the real crystalline microstructure, and the mechanical properties of the amorphous and crystalline constituents are located within reasonable bounds.

关键词： Semicrystalline polymers Micromechanical modeling Mori-Tanaka homogenization genetic optimization algorithm

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Numerical and experimental transition results evaluation for a morphing wing and aileron system

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AERONAUTICAL JOURNAL 2018年第1251期122卷 747-784页

作者： Botez, R. M. Koreanschi, A. Gabor, O. S. Tondji, Y. Guezguez, M. Kammegne, J. T. Grigorie, L. T. Sandu, D. Mebarki, Y. Mamou, M. Amoroso, F. Pecora, R. Lecce, L. Amendola, G. Dimino, I. Concilio, A. Ecole Technol Super Res Lab Act Control Avion & AeroServoElast LARCASE Montreal PQ Canada NRC Aerosp Aerodynam Lab Ottawa ON Canada Univ Naples Federico II Ind Engn Dept Aerosp Div Naples Italy Italian Aerosp Res Ctr CIRA Adapt Struct Div Capua CE Italy

A new wing-tip concept with morphing upper surface and interchangeable conventional and morphing ailerons was designed, manufactured, bench and wind-tunnel tested. The development of this wing-tip model was performed in the frame of an international CRIAQ project, and the purpose was to demonstrate the wing upper surface and aileron morphing capabilities in improving the wing-tip aerodynamic performances. During numerical optimisation with 'in-house' genetic algorithm software, and during wind-tunnel experimental tests, it was demonstrated that the air-flow laminarity over the wing skin was promoted, and the laminar flow was extended with up to 9% of the chord. Drag coefficient reduction of up to 9% was obtained when the morphing aileron was introduced.

关键词： aerodynamics Morphing Wing Tip genetic optimization algorithm Flow Transition Drag Coefficient Reduction Wind Tunnel Tests Morphing Aileron Classification Description: Aerodynamics Aeroelasticity Avionics and Systems Computational Fluid Dynamics Experimental Fluid Dynamics

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Effect of Operating Conditions on PV Performance of PVA Membranes: Experimental and Neural Network Modeling

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SEPARATION SCIENCE AND TECHNOLOGY 2012年第10期47卷 1472-1484页

作者： Shirazi, Yaser Farno, Ehsan Majareh, Hadi Shad Sadrzadeh, Mohtada Mohammadi, Toraj Kasiri, Norollah Iran Univ Sci & Technol IUST Fac Chem Engn Res Ctr Membrane Separat Proce Tehran Iran Iran Univ Sci & Technol IUST Dept Chem Engn Comp Aided Proc Engn Lab CAPE Tehran Iran Islamic Azad Univ S Tehran Branch Dept Chem Engn Tehran Iran

Poly (vinyl alcohol) membranes were prepared by in-situ crosslinking of poly(vinyl alcohol) with glutaraldehyde as crosslinking agent and hydrochloric acid as catalyst and used for dehydration of IPA mixtures. Effects of feed composition, operating temperature, vacuum pressure, and Reynolds number on the permeation performance of the membranes were evaluated. Eighty-nine experimental data was applied to investigate ANN modeling. A multi layered feedforward neural network was applied to model the PV membranes. Two major training algorithms and optimum number of neurons and layers were investigated. As a result, Bayesian regularization successfully predicted experimental data. Different network structures were optimized, using multi object genetic optimization algorithm. The results concluded that the network with structure composing two hidden layers performs better than the other with one hidden layer, and also there is an excellent compatibility between the experimental data and the predicted values of optimum network structure (4:3:2:2). Furthermore, the optimum network was applied to predict extrapolation data and the results showed that this network can extrapolate data as well as interpolating.

关键词： ANN modeling feedforward neural network genetic optimization algorithm pervaporation PVA membrane

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A combined hole position error correction method for automated drilling of large-span aerospace assembly structures

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ASSEMBLY AUTOMATION 2022年第3期42卷 293-305页

作者： Hu, Junshan Sun, Xinyue Tian, Wei Xuan, Shanyong Yan, Yang Wang Changrui Liao, Wenhe Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China Wuhu Machinery Factory Wuhu Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing Peoples R China

Purpose Aerospace assembly demands high drilling position accuracy for fastener holes. Hole position error correction is a key issue to meet the required hole position accuracy. This paper aims to propose a combined hole position error correction method to achieve high positioning accuracy. Design/methodology/approach The bilinear interpolation surface function based on the shape of the aerospace structure is capable of dealing with position error of non-gravity deformation. A gravity deformation model is developed based on mechanics theory to efficiently correct deformation error caused by gravity. Moreover, three solution strategies of the average, least-squares and genetic optimization algorithms are used to solve the coefficients in the gravity deformation model to further improve position accuracy and efficiency. Findings Experimental validation shows that the combined position error correction method proposed in this paper significantly reduces the position errors of fastener holes from 1.106 to 0.123 mm. The total position error is reduced by 43.49% compared with the traditional mechanics theory method. Research limitations/implications The position error correlation method could reach an accuracy of millimeter or submillimeter scale, which may not satisfy higher precision. Practical implications The proposed position error correction method has been integrated into the automatic drilling machine to ensure the drilling position accuracy. Social implications The proposed position error method could promote the wide application of automatic drilling and riveting machining system in aerospace industry. Originality/value A combined position error correction method and the complete roadmap for error compensation are proposed. The position accuracy of fastener holes is reduced stably below 0.2 mm, which can fulfill the requirements of aero-structural assembly.

关键词： Automatic drilling Bilinear interpolation surface Deformation analysis genetic optimization algorithm Hole position error Hole position correction Gravity-induced deformation

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A genetic-based clustering algorithm for efficient resource allocating of IoT applications in layered fog heterogeneous platforms

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1313-1331页

作者： Abedpour, Kimia Shirvani, Mirsaeid Hosseini Abedpour, Elmira Tabarestan Chalus Inst Dept Comp Engn Chalus Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Univ Turin Interuniv Dept Terr Sci Project & Polit Polytech Turin Italy

Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate.

关键词： Fog computing Internet of Things k-means clustering method Jaccard measurement genetic optimization algorithm

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DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

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Computers, Materials & Continua 2024年第11期81卷 3301-3323页

作者： Yuanyuan Ma Ang Chen Cunzhi Hou Ruixia Jin Jinghui Zhang Ruixiang Li College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China Intelligent Medical Engineering SanQuan Medical CollegeXinxiang453003China Information Engineering University Information Engineering UniversityZhengzhou450001China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou450001China

Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer ***,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change *** address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP ***,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent ***,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent ***,the risk value of the target IP is calculated and the IP is identified based on the risk value *** results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent ***,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud *** results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.

关键词： Fraudulent IP identification homology detection clustering genetic optimization algorithm telecom fraud identification

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A novel detection and defense mechanism against false data injection attack in smart grids

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第20期17卷 4514-4524页

作者： Cui, Jinlong Gao, Beibei Guo, Baojun Cangzhou Jiaotong Coll Sch Elect & Elect Engn Huanghua Peoples R China

As the next generation of green power system, smart grids have gradually enhanced the operation efficiency of power system. Meanwhile, the application of communication and intelligent technologies make the power grid more vulnerable to the emerging cyber-physical attacks, such as the false data injection attack (FDIA). Particularly, the deception property of the FDIA on the output measurement estimation can fool the current security mechanism without triggering an alarm. Motivated by this problem, this paper aims at developing a novel detection and recovery mechanism against FDIA in smart grid. Based on the established state space grid model derived from the three-phase sinusoidal voltage equations, an improved principal component analysis (PCA)-based detection method is proposed. By introducing the mathematical transformation principle method, the detection performance such as detection rate and false positive rate can be improved. To keep the stable running of power system, a genetic optimization algorithm-based linear quadratic regulator (LQR) defense method is developed. In addition, to improve the response performance to external attacks, an artificial intelligence method named genetic optimization algorithm is introduced to optimize the robust performance of the proposed defense method. Finally, the simulation results on the IEEE 6-bus and 118-bus grid system demonstrate the superiority of the proposed genetic algorithm optimization-based LQR defense method.

关键词： false data injection attack genetic optimization algorithm principal component analysis smart grid voltage protection

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A New Procedure for Tuning an Allocator and Designing a Robust High-Level Control Law for Over-Actuated Systems.

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IFAC-PapersOnLine 2017年第1期50卷 12741-12747页

作者： Evain, Hélène Rognant, Mathieu Alazard, Daniel Mignot, Jean Ph.D student ONERA Toulouse France Research Scientist ONERA Toulouse France ISAE SUPAERO Toulouse France AOCS Senior Expert CNES Toulouse France

This paper presents a new integrated procedure to tune a control law for over-actuated mechanical systems that may encounter singularities. First, the allocator that divides the commands among the actuators is tuned thanks to a genetic optimization algorithm, that computes the optimal values of its parameters. Then, the open-loop system including the allocator is identified and a robust closed-loop controller is computed with the structured H∞method. Indeed, near singularities, the system and the allocator may create errors to deviate from these points or create delays to reconfigure the actuators, hence there is a need to create a closed-loop controller robust to these characteristics and to parameter variations. This procedure is carried out on a planar redundant robotic manipulator example. Simulations of the behaviours of the open-loop system with the allocator and in a closed loop are presented. © 2017

关键词： Controllers Actuators Control engineering Control theory Electronic guidance systems Flexible manipulators Machine design Navigation Robot applications Robots Allocation Closed loop controllers Design Methodology genetic optimization algorithm High level control Overactuated systems Robot manipulator Robotic manipulators

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Enhancing breast cancer diagnosis accuracy through genetic algorithm-optimized multilayer perceptron

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4433-4449页

作者： Talebzadeh, Hossein Talebzadeh, Mohammad Satarpour, Maryam Jalali, Fereshtehsadat Farhadi, Bahar Vahdatpour, Mohammad Saleh Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Texas A&M Univ Dept Civil & Environm Engn College Stn TX USA Univ Pittsburgh Swanson Sch Engn Dept Bioengn Pittsburgh PA USA Shahid Beheshti Univ Med Sci Fac Med Dept Obstet & Gynecol Tehran Iran Islamic Azad Univ Fac Med Mashhad Branch Mashhad Iran Georgia State Univ Dept Comp Sci Atlanta GA USA

This research paper investigates the application of a genetic algorithm (GA) to optimize the performance of a Multilayer Perceptron (MLP) model for breast cancer diagnosis. Various configurations of hidden layers in the MLP are explored, and their corresponding accuracies range from 0.92 to 0.972. To ensure robust evaluation, k-fold cross-validation is employed, alongside comprehensive preprocessing of the dataset, including normalization, scaling, and encoding. These methodologies contribute to the model's consistent performance and its enhanced ability to generalize. However, the incorporation of a genetic algorithm significantly improves the accuracy range, resulting in values between about 0.97 and 0.99 across different generations. The genetic algorithm optimizes the MLP model by evolving a population of potential solutions (individuals) over multiple generations. Each individual represents a specific set of MLP parameters, such as the number of hidden layers, neurons per layer, and learning rate. The fitness of each individual is evaluated based on the MLP model's accuracy on the breast cancer dataset. The fittest individuals are selected for reproduction, and genetic operators like crossover and mutation are applied to generate new offspring. This iterative process of selection, crossover, and mutation gradually enhances the MLP model's performance. The primary objective of this research is to improve breast cancer diagnosis accuracy by leveraging the strengths of MLP and the optimization capabilities of the genetic algorithm. The results demonstrate that the combined approach effectively enhances the accuracy of breast cancer prediction compared to using MLP alone.

关键词： Breast cancer detection Artificial neural networks genetic optimization algorithm GA-MLP

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