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9th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Khalel, Sara Ismaeel Khudher, Shaker M. Univ Mosul Dept Elect Engn Mosul Iraq

ISBN: (纸本)9781665467544

The rapid increase in the reliance on modern communication technologies of electrical grid has a great influence on the achieved improvement in the performance of network. However, a serious threat to the operation of the grid itself has emerged, which is the cyber-attack. To reduce the impact of this kind of attack on the electrical network, a new strategy was presented to provide an effective method to discover the nature of electrical disturbances according to specific criteria. One of the methods of artificial intelligence was used to discover the nature of cybernetic disturbances and distinguish them from other disturbances. The methodology of this method was tested on the IEEE 14-bus test system. Simulation results showed the capabilities of artificial intelligence to reach the target with high accuracy which helps grid operators of control center to better protect power system against threat of cyber-attack.

关键词： power system cyber-attacks artificial intelligence technique

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Design optimisation of a waveguide-based LP₀₁-LP_0m mode converter by using artificial intelligence technique

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ELECTRONICS LETTERS 2018年第11期54卷 703-704页

作者： Mellah, H. Mirjalili, S. M. Zhang, X. Concordia Univ Dept Elect & Comp Engn iPhoton Labs Montreal PQ H3G 1M8 Canada

A novel optimisation method for designing new and highly complex mode converters is presented. Owing to the complex relationship between the structural parameters and the output performance merit factor, it is very difficult to obtain a general algorithm. As a case study, a mode converter with eight structural parameters is designed with the proposed method. Owing to a large number of structural parameters and complexity of the designing process, the problem is formulated and optimised by using a recent optimisation algorithm called Grey Wolf Optimiser. Six optimal mode converter designs are obtained for LP01-LP0m (m = 2, 3,..., 7). These optimal designs outperform existing state-of-the-art designs. Furthermore, achieving these results is done without any human involvement.

关键词： optimisation convertors waveguides artificial intelligence grey systems circuit complexity electronic engineering computing waveguide-based LP01-LP(0)m mode converter artificial intelligence technique optimisation method structural parameter grey wolf optimiser

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Metric learning-based generalized Gaussian kernel for nonlinear classification

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ENGINEERING APPLICATIONS OF artificial intelligence 2025年 139卷

作者： Wang, Yidan Yang, Liming China Agr Univ Coll Sci Beijing Peoples R China Hebei Univ Math & Informat Sci Baoding Peoples R China

Metric learning and kernel learning have attracted a lot of interest for nonlinear classifications in the artificial intelligence field. Most existing nonlinear classification methods are with preset kernel functions, while classification performances depend on kernel functions. To address these shortcomings, we propose a classification framework based on two-stage metric learning. Specifically, in the first stage, a robust metric is trained by applying a robust loss and the maximal margin principle. Then dual technology accompanied by an alternate iteration algorithm is adopted to optimize the problem, and the strong duality property is demonstrated theoretically. The resulting algorithm is convergent. In the second stage, a generalized Gaussian kernel is trained based on the learned metric. This is an adaptive kernel and its main properties are analyzed theoretically. Then with the learned kernel, a nonlinear classification framework is built to train the optimal separation hyperplane, as a classification decision rule. This can avoid effectively the limitation of k-nearest neighbor classifiers. In addition, we analyze the robustness and generalization error bound of the proposed method. Numerical experiments are conducted on two different databases. In the application database, experiments on different spectral regions show that the proposed framework outperforms significantly the traditional methods. Compared with traditional nonlinear methods, the proposed framework increases classification accuracy by an average of 5.36% in the application database. The statistical tests show the ranking of the proposed framework is the best of all methods. These experiments show the feasibility and effectiveness of the proposed method.

关键词： Metric learning Kernel learning Nonlinear classification Support vector machine artificial intelligence technique

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Additive fault diagnosis techniques in rotor systems: a state-of-the-art review

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2024年第3期49卷 1-49页

作者： Kumar, Prabhat Tiwari, Rajiv Natl Inst Technol Manipur Dept Mech Engn Imphal West 795004 Manipur India Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 Assam India

Faults in rotating systems can cause significant damage to the machinery and can result in downtime and production losses. Hence, the timely detection and diagnosis of faults are very important for the smooth running of machines and the assurance of their safety and reliability. In view of this, a review of the literature has been presented in the article on the types of additive faults and their identification using conventional signal-based techniques and automated artificial intelligence techniques. Through a literature survey, the faulty rigid and flexible rotor systems mounted on rolling element bearings, hydrodynamic bearings, and active magnetic bearings have been studied. The faults incorporated in this article are the additive fault types, in which the process is affected by adding process variables. The rotor unbalances, shaft or bearing misalignment, crack, internal damping, bow in the shaft, rotor-to-stator rub, and mechanical looseness are the classifications of additive faults. Additionally, understanding the rotor response through theoretical and experimental investigations influenced by the additive faults and its detection and diagnosis using vibration and current-induced signals is extremely important, and therefore the present paper briefly discusses this. Following the state of the art in the dynamic analysis and identification of multiple hazardous faults, the general remarks and future directions for further research have been suggested at the end of this article.

关键词： Additive faults rotor systems bearings signals based identification artificial intelligence technique

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artificial intelligence modeling of induction contour hardening of 300M steel bar and C45 steel spur-gear

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INTERNATIONAL JOURNAL OF MATERIAL FORMING 2023年第3期16卷 1-17页

作者： Garois, Sevan Daoud, Monzer Traidi, Khalil Chinesta, Francisco HESAM Univ Arts & Metiers Inst Technol PIMM Lab CNRSCnam 151 Blvd Hop F-75013 Paris France Inst Rech Technol Met Mat & Proc 4 Rue Augustin Fresnel F-57070 Metz France Safran Tech Rue Jeunes Bois F-78117 Chateaufort France

Induction hardening is a heat surface treatment technique widely employed for steel components in order to improve their fatigue life without affecting the metallurgy of the bulk material. The control of the treated components goes through the prediction and the optimization of the induction hardening process parameters. The aim of this work is to propose an approach based on artificial intelligence technique to predict the in-depth hardness profile. For this purpose, experimental tests were first carried out on 300M steel bar and C45 steel spur-gear under single and double frequencies, respectively. Intermediate variables were then generated to be used as input data. Data-driven model based on XGBoost library was finally developed. It was found that the proposed approach predicts with good agreement the hardness profiles and can be used in induction treatment process optimization.

关键词： Induction hardening Hardness artificial intelligence technique Data-driven model XGBoost

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Accurate prediction of chemical exergy of technical lignins for exergy-based assessment on sustainable utilization processes

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ENERGY 2022年第0期243卷 123041-123041页

作者： Huang, Youwang Wang, Haiyong Zhang, Xinghua Zhang, Qi Wang, Chenguang Ma, Longlong Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou 510640 Peoples R China Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China CAS Key Lab Renewable Energy Guangzhou 510640 Peoples R China Guangdong Key Lab New & Renewable Energy Res & De Guangzhou 510640 Peoples R China

The exergy-based assessment on the sustainable utilization processes of technical lignin is important for potential identify and process optimization. In this study, chemical exergy of technical lignin was evaluated for the first time based on the Gibbs free energy relation. The chemical exergy of technical lignin was from 17653.89 to 33337.92 kJ kg(-1).The effects of O/C and H/C ratios on the chemical exergy and standard entropy were investigated by using contour plot analysis. The chemical exergy of technical lignin is more significantly influenced by the O/C ratio, compared with the H/C ratio. Three types of prediction models including artificial neural network model with the input of elemental composition, HHV-based correlation, and element-based correlation were developed. The artificial neural network model has an excellent performance of predicting the chemical exergy of technical lignin, with the prediction relative error of less than +/- 0.15% under the confidential level of 97%. The prediction relative errors of the HHV-based correlation and the element-based correlation are within +/- 1.0% and +/- 2.5%, respectively. This work will provide the basic data for exergy-based assessment on the valorization processes of technical lignin, which is an important aspect of improving the economic level of biorefinery industry. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Technical lignin Chemical exergy Standard entropy Prediction model artificial intelligence technique

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Investigating Aerothermodynamic Characteristics of Environmentally Friendly Materials in Composite Plates Embedded in a Kerr-Type Foundation Using Mathematics Formulation and artificial intelligence techniques

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2025年

作者： Wang, Zhiwen Zhou, Yuhang Brahmia, Ameni Jastaneyah, Zuhair Southwest Univ Sch Natl Governance Chongqing Peoples R China Chongqing Municipal Party Sch Chongqing Peoples R China King Khalid Univ Coll Sci Dept Chem POB 9004 Abha 61413 Saudi Arabia Univ Business & Technol Coll Engn Dept Mech Engn Jeddah 21361 Saudi Arabia

This study investigates the aerothermodynamic behavior of eco-friendly functionally graded materials (FGMs) in composite plates subjected to supersonic airflow and thermal loads, critical for aerospace applications where structural stability, flutter suppression, and thermal resistance are paramount. Traditional metal-ceramic FGMs are replaced with sustainable alternatives bio-ceramics (rice husk ash silica) and bio-polymers (PLA) to reduce environmental impact while maintaining performance. The composite plate, embedded in a Kerr-type foundation to model shear and stiffness interactions, is analyzed as a three-directional FGM with temperature-dependent properties. A meshless method coupled with R-function theory is employed to solve nonlinear governing equations derived from Sanders-Koiter nonlinear plate theory, enabling accurate prediction of large amplitude vibrations under thermal-aerodynamic coupling. To validate results, deep neural networks (DNNs) algorithm as an artificial intelligence technique is trained on datasets generated from the mathematical model, ensuring robustness in predicting nonlinear frequencies and dynamic responses. Key findings highlight the efficacy of eco-FGMs in mitigating flutter and thermal stresses, while the hybrid numerical-AI framework demonstrates superior adaptability to material heterogeneity and boundary conditions. This work advances sustainable material design for high-speed aerospace structures, offering a paradigm shift toward greener alternatives without compromising mechanical integrity.

关键词： Aerodynamics aeroelasticity artificial intelligence technique aeroelastic control environmentally friendly materials Kerr-type foundation

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Seismic response prediction of FRC rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques

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JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2021年第11期12卷 10105-10123页

作者： Mehrabi, Peyman Honarbari, Soheil Rafiei, Shervin Jahandari, Soheil Alizadeh Bidgoli, Mohsen KN Toosi Univ Technol Dept Civil Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Yadegar E Imam Khomeini RAH Shahre Rey Branch Tehran Iran Amirkabir Univ Technol Dept Construct Engn & Management Tehran Iran Western Sydney Univ Ctr Infrastruct Engn Penrith NSW 2751 Australia

This research study focused on the dynamic response and mechanical performance of fiber-reinforced concrete columns using hybrid numerical algorithms. Whereas test data has non-linearity, an artificial intelligence (AI) algorithm has been incorporated with different metaheuristic algorithms. About 317 datasets have been applied from the real test results to detect the promising factor of strength subjected to the seismic loads. Adaptive neuro-fuzzy inference system (ANFIS) was carried out as an AI beside the combination of particle swarm optimization (PSO) and genetic algorithm (GA). Extreme Machine Learning (ELM) was also performed in order to approve the obtained results. According to the findings, it is demonstrated that ANFIS-PSO predicts the lateral load with promising evaluation indexes [R-2 (test) = 0.86, R-2 (train) = 0.90]. Mechanical performance prediction was also carried out in this study, and the results showed that ELM predicts the compressive strength with promising evaluation indexes [R-2 (test) = 0.66, R-2 (train) = 0.86]. Finally, both ANFIS-GA and ANFIS-PSO techniques illustrated a reliable performance for prediction, which encourage scholars to replace costly and time-consuming experimental tests with predicting utilities.

关键词： artificial intelligence technique Fibre-reinforced concrete Seismic load Mechanical performance Dynamic response

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Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

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HELIYON 2023年第2期9卷 e13245页

作者： Sharafi, Saeed Ghaleni, Mehdi Mohammadi Scholz, Miklas Arak Univ Dept Environm Sci & Engn Arak Iran Arak Univ Dept Water Sci & Engn Arak Iran Oldenburg Ostfries Wasserverband Dept Asset Management & Strateg Planning Georgstr 4 D-26919 Brake Unterweser Germany Univ Johannesburg Sch Civil Engn & Built Environm Dept Civil Engn Sci Kingsway CampusPOB 524Aukland Pk ZA-2006 Johannesburg South Africa Univ Salford Sch Sci Engn & Environm Directorate Engn Future Newton Bldg Manchester M5 4WT England South Ural State Univ Natl Res Univ Dept Town Planning Engn Networks & Syst 76 Lenin prospekt Chelyabinsk 454080 Russia

To accurately manage water resources, a precise prediction of reference evapotranspiration (ETref) is necessary. The best empirical equations to determine ETref are usually the temperature -based Baier and Robertson (BARO), the radiation-based Jensen and Haise (JEHA), and the mass transfer-based Penman (PENM) ones. Two machine learning (ML) models were used: least squares support vector regression (LSSVR) and ANFIS optimized using the particle swarm optimization algorithm (ANFPSO). These models were applied to the daily ETref at 100 synoptic stations for different climates of Iran. Performance of studied models was evaluated by the correlation co-efficient (R), coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI) and the Nash-Sutcliffe efficiency (NSE). The combination-based ML models (LSSVR4 and ANFPSO4) had the lowest error (RMSE = 0.34-2.85 mm d-1) and the best correlation (R = 0.66-0.99). The temperature-based empirical relationships had more pre-cision than the radiation-and mass transfer-based empirical equations.

关键词： Aridity index artificial intelligence technique Environmental software evaluation Machine learning Scatter index Water resources management

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Economic emission dispatch of hydro-thermal-wind using CMQLSPSN technique

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IET RENEWABLE POWER GENERATION 2020年第14期14卷 2680-2692页

作者： Robert, Leena Rose Ravi Singh, Lal Raja Singh Sree Sakthi Engn Coll Dept Elect & Elect Engn Coimbatore Tamil Nadu India KIT Kalaignarkarunanidhi Inst Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

This study presents a multi-objective based on a hybrid methodology to apply the problem of economic emission dispatch that is incorporated with hydro, thermal and wind power units. The hybrid method is the combination of both the modified quantum-behaviour lightning search algorithm (MQLSA) and artificial intelligence technique with the help of PSNN. To determine the optimal amounts of the generated powers from the thermal, wind farms and hydro units, the proposed approach is introduced by minimising the cost of generation and the emission level simultaneously. MQLSA is utilised to generate the optimal combination of thermal power with the objective of the minimum error function to minimise the fuel and emission cost of the system with wind speed factor. To capture the optimised wind power with the objective of minimum speed factor, the PSO-artificial neural network technique is utilised. The algorithm is integrated with the feasible solution constraint handling techniques. To validate the effectiveness of the proposed method, the six and ten generating thermal system with wind power is studied. The proposed hybrid technique is implemented in MATLAB/Simulink platform and the simulation result demonstrates the superiority of the proposed method compared to the various existing techniques.

关键词： power generation economics artificial intelligence power generation dispatch wind power plants neural nets particle swarm optimisation hydroelectric generators thermal power stations power engineering computing emission level MQLSA thermal power units minimum error function emission cost wind speed factor optimised wind power PSO-artificial neural network technique thermal system hybrid technique economic emission dispatch hydro-thermal-wind CMQLSPSN technique hybrid methodology artificial intelligence technique particle swarm optimisation technique generated powers thermal wind farms hydro power units modified quantum-behaviour lightning search algorithm wind power units MATLAB-Simulink platform

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