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Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network

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MICROPROCESSORS AND MICROSYSTEMS 2020年 76卷 103096-103096页

作者： Rai, Ashok Kumar Senthilkumar, Radha Kumar, Aswin R. Anna Univ Fac Informat & Commun Engn Dept Informat Technol Chennai Tamil Nadu India

A Hyperspectral camera provides discriminating features for capturing human faces that cannot be obtained by any other imaging technique. Nevertheless, it has new issues comprising curse of dimensionality, physical parameter retrieval, fast computing and inter band misalignment. As a result of, the literature of Hyperspectral Face Recognition is more scanty and confined to improvised dimensionality reduction and minimization of wide-ranging bands, and thus the objective can be obtained by the use of the Convolution Neural Network (ConvNet). Since ConvNet is of great attention in recent times, it can offer outstanding performance in face recognition systems, where the quantity of training data is amply large. We propose a Hyperspectral Face Recognition system using firefly algorithm for band fusion and the Convolution Neural Network for classification. In addition to this, the present work is extended 11 exiting face recognition methods to perform Hyperspectral Face Recognition task. Thus the work has been framed as Hyperspectral Face Recognition problem to an image-set classification problem and assessment of the performance has been done on six state-of-the-art image-set problem techniques, and similarly it was examined on five state-of-the-art RGB and gray scale face recognition system, subsequently applied improved firefly band selection algorithm on Hyperspectral Images to get appropriate band. Assessment with the eleven extended and five existing HSI Face Recognition system on two benchmark datasets (CMU-HSFD & UWA-HSFD) demonstrates that the proposed system overtakes all by a noteworthy margin. Lastly, we execute the band selection demonstration to get the novelty for most informative bands in Visible Near Infrared (VNIR). (C) 2020 Elsevier B.V. All rights reserved.

关键词： Face Recognition Convolution Neural Network Hyperspectral Imaging firefly algorithm Band Selection

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A equação unidimensional de difusão de nêutrons com modelo multigrupo de energia e meio heterogêneo : avaliação do fluxo para problemas estacionários e de cinética

A equação unidimensional de difusão de nêutrons com mode...

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作者： Ceolin, Celina

In the present dissertation the one-dimensional neutron diffusion equation for stationary and kinetic problems in a multi-layer slab has been solved considering the multi-group energy model. One of the objectives and innovation in this work is to obtain an approximate solution with error estimation, accuracy control and in the form of an analytical expression. With this solution there is no need for interpolation schemes, which are usually needed in case of discretization of the domain. The neutron flux is expanded in a Taylor series whose coefficients are found using the differential equation and the boundary and interface conditions. The domain is divided into several layers, whose size and the polynomial order can be adjusted according to the required accuracy. To solve the eigenvalue problem the conventional power method has been used. The methodology is applied in a benchmark problem consisting of the solution of the diffusion equation as an initial condition and solving kinetic problems for different transients. The results are compared successfully with the ones in the literature. The convergence of the series is guaranteed by applying a criterion based on the Lipschitz criterion for continuous functions. Note that the solution obtained, together with the convergence analysis, shows the robustness and accuracy of this methodology. ...

关键词： Structural optimization Equações de transporte de neutrons Meta-heuristic algorithms Reator nuclear Métodos numéricos Harmony search firefly algorithm Truss structures Tese

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A Hybrid Approach to Extend the Life Time of Heterogeneous Wireless Sensor Networks

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Procedia Computer Science 2015年 63卷 136-141页

作者： Fateh boutekkouk Fatima Taibi Khawla Meziani

In this work, we propose to use two optimization meta-heuristics that are: firefly and simulated annealing to extend the life time of a heterogeneous wireless sensors network (WSN) with a fixed base station by reducing the communication energy consumption. The proposed approach combines between the clustering and the chain approaches. Our optimization algorithm tries at first time to find the optimal clustering over the network nodes using the firefly algorithm then inside each cluster it attempts to find the best chain using simulated annealing. A particularity in our algorithm is the dynamic adjustment of clusters number over rounds.

关键词： WSN Energy consumption Clustering Chain firefly algorithm Simulated annealing

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PSO-VFA: A Hybrid Intelligent algorithm for Coverage Optimization of UAV-Mounted Base Stations

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JOURNAL OF INTERNET TECHNOLOGY 2022年第3期23卷 487-495页

作者： Chen, Xuefeng Tang, Wan Yang, Ximin Zhou, Lingyun Li, Liuhuan South Cent MinZu Univ Sch Comp Sci Beijing Peoples R China

When the number of outdoor wireless users surges and fixed base stations (BSs) can hardly accommodate high-load communication traffic, unmanned aerial vehicles (UAVs) carrying BSs can provide wireless communication services, and the location deployment of the UAV-mounted BSs directly influences the reliability of network communications. For the target area scenario where the UAVs uniformly cover user nodes, we propose a hybrid intelligent coverage algorithm called PSO-VFA to optimize the coverage of a fixed number of UAV-BSs. The PSO-VFA algorithm consists of two phases employing different intelligent algorithms. First, we adopt a particle swarm optimization (PSO) method for a global search of the coverage areas. Then, for local search, a virtual-repulsive-force-based firefly algorithm (VFA) is proposed in this paper to maximize the user coverage. In the VFA algorithm, the users are treated as the objects attracting the UAVs, and the virtual repulsive force is used for UAV location adjustment. Simulation results show that the proposed PSO-VFA hybrid algorithm has faster convergence and significantly increases the communication coverage of UAVmounted BSs compared with individual intelligent algorithms such as VFA, PSO, genetic algorithm (GA), and simulated annealing (SA).

关键词： UAV-mounted base station Deployment coverage Intelligent algorithm firefly algorithm Particle swarm optimization

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Irrigation water infiltration modeling using machine learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 180卷 105921-105921页

作者： Sayari, Sareh Mandavi-Meymand, Amin Zounemat-Kermani, Mohammad Shahid Bahonar Univ Kerman Water Engn Dept Kerman *** Iran

The present study proposes five standard artificial intelligence models including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multivariate Linear Regression (MLR), and Support Vector Regression (SVR) as well as their integrative models combined with the nature-inspired firefly algorithm (FA) to forecast the infiltrated water in the furrow irrigation system. The FA functions as an optimizer tool in constructing the integrative models. Data for assessing these models was gathered from published literatures and field experiments conducted on researching farm at SBUK University, Kerman, Iran. The input parameters used in modeling were furrow length (L), the inflow rate (Q), advance time at the end of the furrow (TL), cross-sectional area of inflow (A(0)) and infiltration opportunity time (T-0). The root mean square (RMSE), the mean absolute error (MAE), the correlation coefficient (R-2), the Nash-Sutcliffe efficiency index (NSE), and the index of agreement (IA) were used to evaluate the predicting performance of the suggested models. The best values of the statistical criteria and in training and testing phase of the simulation imply that the MLPNN-FA and SVR-FA provided an excellent fit to the observed data. Comparison of the results revealed that the FA is able to boost the accuracy of the models so that the improvement in RMSE values was 5%, 1%, 4%, 47% in the ANFIS, GMDH, MLPNN, and SVR, respectively. Having the calculated value for the comprehensive synthesis index (SI) indicated that using the combination of the SVR and FA noticeably enhance the performance of the standard SVR model by 97%. Even though the FA algorithm had a trivial influence on the GMDH model;however, it can be introduced as a powerful tool to enhance the complicated modeling processes.

关键词： Artificial intelligence Hybrid models Infiltration Furrow irrigation firefly algorithm

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Daily Runoff Prediction Based on FA-LSTM Model

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WATER 2024年第16期16卷 2216-2216页

作者： Chai, Qihui Zhang, Shuting Tian, Qingqing Yang, Chaoqiang Guo, Lei North China Univ Water Resources & Elect Power Sch Water Conservancy Zhengzhou 450046 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Henan Water Conservancy Investment Grp Co Ltd Zhengzhou 450002 Peoples R China Henan Key Lab Water Environm Simulat & Treatment Zhengzhou 450002 Peoples R China

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1-5 days). Four quantitative evaluation metrics-mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling-Gupta efficiency coefficient (KGE)-are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

关键词： runoff prediction LSTM model firefly algorithm machine learning hyperparameter optimization

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Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation

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ENERGIES 2018年第5期11卷 1134页

作者： Jallad, Jafar Mekhilef, Saad Mokhlis, Hazlie Laghari, Javed Badran, Ola Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Kuala Lumpur 50603 Malaysia Univ Malaya Dept Elect Engn Kuala Lumpur 50603 Malaysia Quaid e Awam Univ Engn Sci & Technol QUEST Dept Elect Engn Nawabshah 67450 Sindh Pakistan

In a radial distribution network integrated with distributed generation (DG), frequency and voltage instability could occur due to grid disconnection, which would result in an islanded network. This paper proposes an optimal load shedding scheme to balance the electricity demand and the generated power of DGs. The integration of the firefly algorithm and Particle Swarm Optimization (FAPSO) is proposed for the application of the planned load shedding and under frequency load shedding (UFLS) scheme. In planning mode, the hybrid optimization maximizes the amount of load remaining and improves the voltage profile of load buses within allowable limits. Moreover, the hybrid optimization can be used in UFLS scheme to identify the optimal combination of loads that need to be shed from a network in operation mode. In order to assess the capabilities of the hybrid optimization, the IEEE 33-bus radial distribution system and part of the Malaysian distribution network with different types of DGs were used. The response of the proposed optimization method in planning and operation were compared with other optimization techniques. The simulation results confirmed the effectiveness of the proposed hybrid optimization in planning mode and demonstrated that the proposed UFLS scheme is quick enough to restore the system frequency without overshooting in less execution time.

关键词： load shedding technique voltage stability index islanding distribution network firefly algorithm particle swarm optimization

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Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

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FRONTIERS IN MATERIALS 2023年 10卷

作者： Wang, Qiang Zhou, Mengmeng China Univ Min & Technol Sch Mines Xuzhou Peoples R China

Recycled concrete from construction waste used as road material is a current sustainable approach. To provide feasible suggestions for civil engineers to prepare recycled concrete with high flexural strength (FS) for the road pavement, the present study proposed three hybrid machine learning models by combining support vector machine (SVM), decision tree (DT) and multiple linear regression (MLR) with the firefly algorithm (FA) for the computational optimization, named as SVM-FA, DT-FA, and MLR-FA, respectively. Effective water-cement ratio (WC), aggregate-cement ratio (AC), recycled concrete aggregate replacement ratio (RCA), nominal maximum recycled concrete aggregate size (NMR), nominal maximum normal aggregate size (NMN), bulk density of recycled concrete aggregate (BDR), bulk density of normal aggregate (BDN), water absorption of RCA (WAR) and water absorption of NA (WAN) were employed as the input variables. To determine the predicting results of varying hybrid models, root mean square error (RMSE) and correlation coefficient (R) were used as performance indexes. The results showed that the SVM-FA demonstrated the highest R values and the lowest RMSE values, and the fitting effect of the predicted values and the actual values of the FS of recycled concrete is the best. All the above analysis proving that the SVM optimized by FA hyperparameters has the highest prediction accuracy and SVM-FA can provide engineers a more accurate and convenient tool to evaluate the FS of recycled concrete. The results of sensitivity analysis showed that WC has the most significant influence on the FS of recycled concrete, while RCA has the weakest influence on the FS, which should be noticed when engineers apply recycled concrete to road design in the future.

关键词： machine learning flexural strength hyperparameter firefly algorithm root mean square error

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Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 91卷 103573-000页

作者： Tian, Zhongda Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Peoples R China

Accurate prediction of wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A new prediction method for short-term wind speed based on local mean decomposition (LMD) and combined kernel function least squares support vector machine (LSSVM) is proposed. The short-term wind speed time series is decomposed into some components by the LMD algorithm. Based on LSSVM, radial basis function and the Polynomial function are used to generate the combined kernel function. The combined kernel function LSSVM combines the advantages of the radial basis function and the Polynomial function, which can achieve better prediction accuracy. The decomposed wind speed time series are predicted separately by the combined kernel function LSSVM model. At the same time, an improved firefly algorithm is proposed to optimize the parameters of the combined kernel function LSSVM. The final predictive value can be obtained by superimposing the predicted value of each combined kernel function LSSVM prediction model. The actual collected short-term wind speed data is chosen as the research object, the simulation experiments with four prediction horizons have been implemented. Compared with state-of-the-art prediction methods, through the comparison result curve between the prediction and actual wind speed, the box-plot results of predictive error distribution, the comparison results of the relative prediction error, the performance indicators, the Pearson's test, the DM test and the Taylor diagram results show that the proposed prediction method has higher prediction accuracy and is able to reflect the laws of wind speed correctly. Furthermore, the simulation results of four new datasets and adding noise to the input data of training set show that the proposed prediction method has strong robustness.

关键词： Wind speed prediction Local mean decomposition Kernel function LSSVM firefly algorithm

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Multi-objective stochastic techno-economic-environmental optimization of distribution networks with G2V and V2G systems

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 218卷

作者： Ahmadi, Seyed Ehsan Kazemi-Razi, S. Mahdi Marzband, Mousa Ikpehai, Augustine Abusorrah, Abdullah Northumbria Univ Elect Power & Control Syst Res Grp Ellison Pl Newcastle Upon Tyne NE1 8ST England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia Sheffield Hallam Univ Dept Engn & Math Sheffield S11WB England King Abdulaziz Univ Fac Engn KA CARE Energy Res & Innovat Ctr Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

Plug-in electric vehicles (PEVs) are one of the most promising technologies for decarbonizing the transportation sector towards the global Net-zero target. However, charging/discharging of PEVs impacts the electricity network's stability, increases the operating costs, and affects the voltage profile. This paper proposes a flexible multi-objective optimization approach to evaluate and deploy vehicle-to-grid and grid-to-vehicle technologies considering techno-economical and environmental factors. Furthermore, life cycle of PEV batteries, charg-ing/discharging pattern, and driving behaviours of the PEV owners are considered. The simulations are run over a modified IEEE 69-bus radial distribution test system to minimize two objective functions including the operating costs and CO2 emissions using the heuristic-based firefly algorithm in a stochastic optimization framework considering renewable generations, load consumption, and charging/discharging timing of PEVs as the uncertain parameters. The results demonstrate significant reductions in the operating costs and CO2 emissions, and the voltage profile of the network is improved properly. Besides, by implementing the discharging facility of PEVs in the network, the PEV owners save a considerable amount in operating costs.

关键词： Plug-in electric vehicle CO2 emission firefly algorithm Multi-objective optimization

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