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Synergistic effects of halloysite nanotubes with metal and phosphorus additives on the optimal design of eco-friendly sandwich panels with maximum flame resistance and minimum weight

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NANOTECHNOLOGY REVIEWS 2022年第1期11卷 252-265页

作者： Kamarian, Saeed Yu, Ruiwen Song, Jung-il Changwon Natl Univ Dept Mech Engn Chang Won South Korea

The present work addresses the optimal design of sandwich panels made of flax fabric (FF)/vinyl ester (VE) composite face sheets and honeycomb VE core. The sandwich structures are first optimized in terms of flammability by obtaining the best combination of ammonium polyphosphate (APP), halloysite nanotube (HNT), and magnesium hydroxide (MH) as three flame retardants (FRs). Using the Taguchi method and horizontal burning test, it is shown that [6, 3, and 3%] and [1, 0.5, and 0%] are the optimal combinations of APP, HNT, and MH for the face sheets and core, respectively. Cone calorimeter test results indicate that the optimal FR combinations significantly decrease the mass lost rate (MLR), heat rate release (HRR), total smoke release (TSR), and maximum average release heat emission (MARHE). The FR sandwich structures are then geometrically optimized under compressive loads based on their weight. Different failure modes are considered as the design constraints of the optimization problem. imperialist competitive algorithm (ICA), as a powerful meta-heuristic algorithm, is implemented to considerably reduce the computational cost of the optimization process. The results of this study show that proper combinations of FR additives can increase the flame retardancy while decreasing the weight of sandwich panels.

关键词： honeycomb sandwich structures flax fabric vinyl ester composites flame retardant additives weight minimization imperialist competitive algorithm

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Prediction of blast-induced flyrock by using neural-imperialist competitive method (Case Study: Sungun Copper Mine)

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RUDARSKO-GEOLOSKO-NAFTNI ZBORNIK 2024年第5期39卷 109-120页

作者： Hanifehnia, Jalil Esmaeilzadeh, Akbar Mikaeil, Reza Atalou, Solat Islamic Azad Univ Dept Min Engn Ahar Branch Ahar Iran Urmia Univ Technol Environm Fac Dept Min Engn Orumiyeh Iran

This research focuses on conducting studies that predict the distance of blast-induced flyrock, which is an undesirable environmental phenomenon in open-pit mines. While there are experimental methods available for predicting blastinduced flyrock, the complex process of assessing the distance of flyrock has reduced the efficiency of these approaches. This study employs artificial intelligence methods and statistical techniques to forecast the flyrock distance in the Sungun copper mine. Thus, an Artificial Neural Network (ANN-MLP) and a new hybrid model of Artificial Neural Network (ANN) optimized by the imperialist competitive algorithm (ICA), known as (ICA-ANN), are used to predict the flyrock distance, considering crucial parameters such as the number of holes, hole spacing, burden, total charge, specific drilling, charge per hole and specific charge. The results showed that the Artificial Neural Network, with RMSE, MAE, and R2 error values of 9.31 m, 7.10 m, and 0.81, respectively, was able to predict the flyrock distance well compared to the measured data in the test phase. However, the implementation of the imperialist competitive algorithm optimizer in the neural network enhanced the prediction of the flyrock distance, yielding RMSE, MAE, and R2 values of 5.66 m, 4.60 m, and 0.89, respectively. Finally, by performing sensitivity analysis on the input parameters of the flyrock distance, it was determined that the amount of explosive consumption and the number of holes have the greatest impact on the blastinduced flyrock distance.

关键词： flyrock distance blast artificial neural network imperialist competitive algorithm Sungun copper mine

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Optimum Network Reconfiguration to Improve Power Quality and Reliability in Distribution System

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第4期9卷 101-110页

作者： Bashardoust, Arash Farrokhifar, Meisam Fard, Amin Yousefzadeh Safari, Amin Mokhtarpour, Ehsan Islamic Azad Univ Heris Branch Dept Elect Engn Heris Iran Univ Tabriz Dept Elect & Comp Engn Tabriz Iran Azarbaijan Shahid Madani Univ Dept Elect Engn Tabriz Iran

In this paper, network reconfiguration has been conducted to improve the power quality and reliability of distribution systems in presence of distributed generations. In this case, network reconfiguration is analyzed for different indices. The most important indices are the number of propagated voltage sags (N sag), sustained average interruption frequency index, average system interruption frequency index and momentary average interruption frequency index. The imperialist competitive algorithm (ICA) is hired to find the optimal configuration of the distribution system in order to minimize the mentioned indices. Simulation result is discussed with and without distributed generation.

关键词： Power Quality Reliability imperialist competitive algorithm Distributed Generations Reconfiguration

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Estimation of nuclear magnetic resonance (NMR) log permeability by integrating artificial neural network and imperialist competition optimization algorithm (ICA) in one of the oil fields of southwestern Iran

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第22期14卷 2418-2418页

作者： Mohsenipour, Abouzar Soleimani, Bahman Zahmatkesh, Iman Veisi, Iman Shahid Chamran Univ Ahvaz Fac Earth Sci Dept Petr Geol & Sedimentary Basins Ahvaz Iran Natl Iranian South Oil Co NISOC Ahvaz Iran

Permeability calculation is one of the most complex subjects in characterizing a hydrocarbon reservoir. Because among the well logs, only nuclear magnetic resonance (NMR) log is possible to do in an accurate continuous measurement way. In the present research paper, the permeability values obtained from the NMR log in the Asmari reservoir by Schlumberger or T-2 (SDR) and free fluid (Coates) methods were used to perform estimation by intelligent methods. Then, using an artificial neural network (ANN) and combining it with the imperial competitive optimal algorithm (ANN-ICA), the permeability values were predicted. Finally, predicted permeability values are compared with real values, and the prediction accuracy was regarded in view of mean squared error (MSE) and correlation coefficient (R-2). The results of this study indicated that the efficiency of ANN in permeability prediction was high when this method was optimized with the imperialist competitive algorithm. This method can be used as a powerful tool in the prediction of other reservoir parameters.

关键词： Nuclear magnetic resonance (NMR) Permeability models Neural network imperialist competitive algorithm Asmari reservoir

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The production-distribution problem with order acceptance and package delivery: models and algorithm

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MANUFACTURING REVIEW 2016年第1期3卷

作者： Khalili, Majid Esmailpour, M. Naderi, B. Islamic Azad Univ Karaj Dept Ind Engn Moazen BlvdShoora St Karaj Iran Kharazmi Univ Dept Ind Engn Fac Engn Enghelab StSouth Moffateh St Tehran Iran

The production planning and distribution are among the most important decisions in the supply chain. Classically, in this problem, it is assumed that all orders have to produced and separately delivered;while, in practice, an order may be rejected if the cost that it brings to the supply chain exceeds its revenue. Moreover, orders can be delivered in a batch to reduce the related costs. This paper considers the production planning and distribution problem with order acceptance and package delivery to maximize the profit. At first, a new mathematical model based on mixed integer linear programming is developed. Using commercial optimization software, the model can optimally solve small or even medium sized instances. For large instances, a solution method, based on imperialist competitive algorithms, is also proposed. Using numerical experiments, the proposed model and algorithm are evaluated.

关键词： The production planning and distribution problem Order acceptance Scheduling The package delivery imperialist competitive algorithm

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A New Control Strategy Based on Reference Values Changing for Enhancing LVRT Capability of DFIG in Wind Farm

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2019年第4期9卷 1626-1637页

作者： Rafiee, Zahra Rafiee, Mansour Aghamohammadi, Mohammadreza Shahid Beheshti Univ Fac Elect Engn Shahid Shahriari SqDaneshjou Blvd Tehran Iran

Low voltage ride through (LVRT) capability enables a doubly fed induction generator (DFIG) - based wind farm (WF) to remain online supporting the electric grid during fault condition. This paper proposes a control-based strategy for improving LVRT capability of DFIG, in which by changing active and reactive powers reference values following a voltage dip (VD) condition. Moreover, the direct power control (DPC) method is modified to adaptively change the reference values based on the severity of VD. In the proposed modified DPC (MDPC) method, the parameters of the PI controllers are tuned using the imperialist competitive algorithm (ICA). For activating/deactivating the proposed strategy, a voltage dip index (VDI) is proposed which is updated within a moving time window including samples measured by phasor measurement unit (PMU). In order to evaluate rout mean square (RMS) value of the voltage from the measured values by PMU, the DFT technique is used. For activating/deactivating the control strategy, two threshold values a and beta are defined. In the active mode, the active and reactive power are changed to zero and one p.u, while in the deactivate mode theses are changed to one and zero p.u, respectively. Based on the proposed control strategy, during a VD condition, DFIG will be able to smooth the DC-link voltage fluctuations and significantly reduces the oscillations of the stator and rotor currents. The simulation results show the effectiveness of the proposed control strategy for improving LVRT capability of DFIG.

关键词： Doubly fed induction generator (DFIG) low voltage ride through (LVRT) direct power control (DPC) voltage dip imperialist competitive algorithm

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Development of an alternative PSO-based algorithm for simulation of endurance time excitation functions

ENGINEERING REPORTS

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ENGINEERING REPORTS 2019年第3期1卷

作者： Mashayekhi, Mohammadreza Harati, Mojtaba Estekanchi, Homayoon E. Sharif Univ Technol Dept Civil Engn Tehran Iran Univ Sci & Culture Dept Civil Engn Rasht *** Iran

This paper presents a particle swarm optimizer for production of endurance time excitation functions (ETEFs). These excitations are intensifying acceleration time histories that are used as input motions in endurance time (ET) method. The accuracy of the ET methods heavily depends on the accuracy of ET excitations. Unconstrained nonlinear optimization is employed to simulate these excitations. Particle swarm optimization (PSO) method as an evolutionary algorithm is examined in this paper to achieve a more accurate ETEF, where optimal parameters of the PSO are first determined using a parametric study on the involved variables. The proposed method is verified and compared with the trust-region-reflective method as a classical optimization method and imperialist competitive algorithm as a recently developed evolutionary method. Results show that the proposed method leads to more accurate ET excitations.

关键词： classical optimization methods discrete wavelet transform dynamic analysis endurance time method imperialist competitive algorithm particle swarm optimization

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A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network algorithms

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EARTHS FUTURE 2023年第11期11卷

作者： Panahi, Mahdi Khosravi, Khabat Rezaie, Fatemeh Ferreira, Carla S. S. Destouni, Georgia Kalantari, Zahra KTH Royal Inst Technol Dept Sustainable Dev Environm Sci & Engn SEED Stockholm Sweden Stockholm Univ Bolin Ctr Climate Res Dept Phys Geog S-10691 Stockholm Sweden Florida Int Univ Dept Earth & Environm Miami FL 33199 USA Univ Prince Edward Isl Sch Climate Change & Adaptat Charlottetown PE Canada Korea Inst Geosci & Mineral Resources KIGAM Geosci Data Ctr Daejeon South Korea Korea Univ Sci & Technol Dept Geophys Explorat Daejeon South Korea Univ Hawaii Manoa Water Resources Res Ctr Dept Civil & Environm Engn Honolulu HI 96822 USA Polytech Inst Coimbra Appl Res Inst Coimbra Portugal Polytech Inst Coimbra Res Ctr Nat Resources Environm & Soc CERNAS Coimbra Agr Tech Sch Coimbra Portugal

Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large-scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the imperialist competitive algorithm (ICA). With Sweden as an application case for nation-wide flood susceptibility mapping, this modeling approach considers ten geo-environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo-environmental input factors. In general, accurate nation-wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well-performing and cost-effective for the case of Sweden, calling for further application and testing in other world regions.

关键词： large-scale flood prediction nation-wide flood susceptibility mapping convolutional neural network gray wolf optimizer imperialist competitive algorithm Sweden

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Correlation Analysis of Factors Affecting Firm Performance and Employees Wellbeing: Application of Advanced Machine Learning Analysis

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algorithmS 2022年第9期15卷 300-300页

作者： Pap, Jozsef Mako, Csaba Illessy, Miklos Dedaj, Zef Ardabili, Sina Torok, Bernat Mosavi, Amir Szechenyi Istvan Univ Szechenyi Univ Doctoral Sch Management SzEEDSM H-9026 Gyor Hungary Natl Univ Publ Serv Inst Informat Soc H-1083 Budapest Hungary Eotvos Lorand Res Network Ctr Social Sci H-1097 Budapest Hungary Univ Pecs Doctoral Sch Business Adm H-7622 Pecs Hungary Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Bratislava 81243 Slovakia Obuda Univ John von Neumann Fac Informat H-1034 Budapest Hungary

Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that "skills requirements and skill matching" and "employee voice" are the two factors that matter most in enhancing firm performance and wellbeing.

关键词： organizational performance machine learning big data imperialist competitive algorithm employee wellbeing artificial neural networks firm performance artificial intelligence deep learning data science

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Breast Cancer Diagnosis Using Data Mining Methods,Cumulativehistogramfeatures,and GarylevelcoOccurrence Matrix

Breast Cancer Diagnosis Using Data Mining Methods,Cumulative...

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8th International Conference on Researches in Engineering, Technology and Sciences (ICRETS)

作者： Asgarali Bouyer Soheil Zarei Department of Information Technology Azarbaijan Shahid Madani University Department of Computer science Shabestar BranchIslamic Azad University

In recent years,computerized tools have played an important role in the diagnosis of breast *** article attempted to propose a method for breast cancer diagnosis in digitalized mammography using graylevel co-occurrence matrix(GLCM) and cumulativehistogram *** this paper,a developed method obtained from data mining and decision tree classifier methodswas introduced based on GLCM properties and histogram cumulative *** is an efficient tool for the early diagnosis of breast ***,computerized methods have proved to be an extraordinary tool for radiologistsin terms of the diagnosis of breast *** images are classified into three groupsof benign,malignant,and normal *** efficiency of this methodwasinvestigatedon DDSM(digital database for screening mammography)considering theaccuracy criterion and selection of appropriate features for *** accuracy could be improved by most of the related GLCM properties selected by the imperialist competitive algorithm.

关键词： Mammography GLCM decision tress imperialist competitive algorithm

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