Hybrid Intelligent Method Research Articles

Modeling of magnetocaloric effect in RE2X2Y ternary compounds for cooling technology using hybrid intelligent learning computational methods

Cooling technology that employs magnetocaloric effect of magnetic refrigerants has been lately recognized as a green system with immense potential to meet the global refrigeration demand. Hugeness of magnetocaloric impact is one of the major factors which determines the choice of magnetic refrigerants for cooling applications. This research endeavour explores and models magnetocaloric effect (MCE) in ternary system of compounds which combine rare-earth metals (RE = Tm, Er, Ho, Dy, Tb and Gd) due to the localized f-orbital electrons, transition metals (X = Co, Cu and Ni) due to their ferromagnetism near room temperature and other metals (Y = Al, Ga, In, Cd and Sn) using genetic algorithm hybridized support vector regression (GSVR) computational approach. The results of Gaussian transformation function-based hybrid model (G-MCE-GSVR) and polynomial transformation function-based hybrid model (P-MCE-GSVR) are compared with stepwise regression-based model (SR-MCE) using different performance metrics such as mean absolute error (MAE), correlation coefficient (CC) and root mean square error (RMSE). G-MCE-GSVR model outperforms P-MCE-GSVR model using testing data samples with improvement of 66.93 % for RMSE metric, 5.69 % for CC metric and 68.58 % for MAE metric. Similarly, the P-MCE-GSVR model performs better than SR-MCE with improvements of 60.36 %, 37.63 % and 63.41 % corresponding to performance metrics RMSE, CC and MAE, respectively. G-MCE-GSVR model was also employed for investigating the influence of applied magnetic field on magnetocaloric effect in system of RE2X2Y ternary materials. The outstanding performance demonstrated by the developed models coupled with the obtained huge magnetocaloric effect would be of great significance in strengthening practical implementation of magnetic cooling technology for addressing energy crisis and global refrigeration demand.

Hybrid intelligent and numerical methods to estimate the transmission coefficients of rectangular floating breakwaters

ABSTRACT Breakwaters are used to reduce incoming wave energy at harbors and shorelines. This paper presents a comparison of novel two-dimensional hybrid intelligent models for the idealization of the effects of waves on the performance of moored rectangular floating breakwaters (FBs). Fluid structure interactions (FSIs) were idealized by airy-type monochromatic regular waves generated in a numerical wave tank. The coupled Volume of Fluid-Fast Fictitious Domain (VOF-FFD) interpolation method was used to evaluate FB motions. Different forms of Least Squares Support Vector Machine Methods (LSSVMs) that utilized 183 data streams were used to model FB performance for different wave height-to-water depth ratios, dimensional aspect ratios, and specific length-to-water depth ratios. Of those, 80% were used to train the model and 20% to test it. Parametric studies have shown that during training a Least Squares Support Vector Machine Method-Bat Algorithm (LSSVM-BA) with R2 = 0.8725, MAE = 0.0276, and RMSE = 0.0488 presents the most appropriate model for the evaluation of FB performance. Notwithstanding this, during testing a Least Squares Support Vector Machine Method-Cuckoo Search (LSSVM-CS) Algorithm with corresponding values of 0.6841, 0.0519, and 0.0708 performs better.

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.

Hybrid Intelligent Dynamic Optimization of Switched Systems

In this article, a hybrid intelligent dynamic optimization method integrating of particle swarm optimization (PSO) and gradient-based optimization (GBO) is proposed for optimal control of switched systems. The method both improves the solution accuracy of PSO and avoids falling into local minima generated by the deterministic optimization. First, the PSO algorithm with ring topology is used to explore the whole search space to detect the global optimum area. Secondly, the GBO algorithm is deployed in the detected global optimum area to achieve faster convergence rate and higher precision solution than those of pure PSO. Finally, the simulation results show that the algorithm outperforms both PSO and GBO in terms of solution accuracy and computational cost.

Performance Enhancement of Cross-Layer Cognitive Media Access Control Protocol for Wireless Sensing Networks Using Hybrid Intelligent Optimization Approach

In recent times, wireless sensing networks (WSNs) application usage is increased in a great way. Access to the communication channel is handled by the Medium Access Control (MAC) layer. At the MAC, a control channel is utilized to determine collision-free data transport pathways. As a result, with cognitive radio (CR) technology, the control channel architecture is critical to obtaining mandatory quality of Service (QoS). However, latency, network communication delay, and energy consumption are the main problem. In this article, a novel African Buffalo allied Jellfish Optimization (AFJO) is proposed for clustering and optimal Cluster Head (CH) selection. The hybrid intelligence method uses a unique probabilistic assessment rule purpose as a fitness role to find the best data transmission path while avoiding congestion, which is named as Fuzzy Interfaced Red Deer (FIRD). The proposed protocol’s performance is evaluated using Network Simulator (NS2), which takes into account parameters such as energy consumption, computational complexity, and Quality of Service (QoS) performance with radio frequency integrated parameters. The suggested technique decreases energy consumption, end-to-end latency, communication overhead, and maximizes network throughput when compared to state-of-the-art cross layer cognitive mac protocol for WSNs system approaches.

Revenue forecast models using hybrid intelligent methods

Abstract The aim of this study is to forecast the revenue of a seller taking part in an online e-commerce marketplace by using hybrid intelligent methods to help the seller build a solid financial plan. For this purpose, three different approaches are applied in order to accurately forecast the revenue. In the first approach, after applying simple preprocessing steps on the dataset, forecast models are developed with Random Forest (RF). In the second approach, Isolation Forest (IF) is used to detect outliers on the dataset, and minimum Redundancy Maximum Relevance (mRMR) is utilized to select the features that affect the quality of revenue forecast, correctly. In the last approach, a feature selection process is performed first and then the Density-Based Spatial Clustering and Application with Noise (DBSCAN) is used to cluster the dataset. After these processes are carried out, forecast models are developed with RF. The dataset used includes the daily revenue of a seller with several other features. Mean Absolute Percent Error (MAPE) is used for evaluating the performance of the forecast models.

PSO-based Machine Learning Methods for Predicting Ground Surface Displacement Induced by Shallow Underground Excavation Method

Four hybrid intelligent methods are developed to predict the maximum ground surface settlement (Smax) induced by shallow underground excavation method (SUEM). Particle swarm optimization (PSO) algorithm with k-fold cross validation is used to determine the optimal hyperparameters or random parameters in the four machine learning (ML) methods, namely that, back-propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR) and random forest (RF). 100 field engineering samples are collected from published papers. In each data sample, the effect of stratum mechanical conditions, tunnel geometric parameters and construction parameters on Smax is considered. Correlation laws among parameters are investigated through Pearson correlation coefficient, data distribution histogram and correlation confidence ellipse. The performance of four PSO-based ML methods is comprehensively compared by fitness function, time cost and prediction accuracy in the training and test processes. PSO-RF outperforms PSO-SVR, PSO-ELM and PSO-BPNN in the prediction accuracy for Smax owing to larger R2, smaller MAE and RMSE. Calculation time that the optimal hyperparameters are determined is the fastest for PSO-RF, and PSO-ELM has the smallest fitness function. The prediction performance of PSO-RF method for construction parameters is also discussed. This work can provide theoretical guidance for design and construction of SUEM.

Intelligent multiobjective optimization for high-performance concrete mix proportion design: A hybrid machine learning approach

The concrete mix proportion design process is complex but important, especially in cold, ocean, underground and other complex engineering environments. In this study, a hybrid intelligent optimization method based on the random forest (RF), recursive feature elimination (RFE), Bayesian optimization (BO), least squares support vector machine (LSSVM) and nondominated sorting genetic algorithm (NGSA)-III was proposed to optimize the concrete mix proportion and rapidly and accurately predict the frost resistance, chloride ion penetration resistance and concrete strength (CS). Adopting a key project in Jilin Province as an example, the RF-RFE-BO-LSSVM-NSGA-III algorithm achieved a significant optimization effect in terms of the chloride ion permeability coefficient (CIPC), relative dynamic elastic modulus (RDEM) and 28-day CS. After optimization, the chloride ion penetration resistance, frost resistance and CS increased by 34.6%, 4.1% and 3.7%, respectively, over the average levels of the sample data. This study can provide basis for concrete mix proportion design in complex environment.

A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism

Fault diagnosis of rolling bearings helps ensure mechanical systems’ safety. The characteristics of temporal and interleaved noise in the bearing fault diagnosis data collected in the industrial field are addressed. This paper proposes a hybrid intelligent fault diagnosis method (WKN-BiLSTM-AM) based on WaveletKernelNetwork (WKN) and bidirectional long-short term memory (BiLSTM) network with attention mechanism (AM). The WKN model is introduced to extract the relevant impact components of defects in the vibration signals, reduce the model training parameters and facilitate the processing of signals containing noise. Then, the fusion of spatial-temporal features is achieved by combining BiLSTM networks to compensate for the lack of individual networks that ignore the dependent information between discontinuous sequences. Finally, the AM module is introduced to improve the feature coding performance of BiLSTM and fault diagnosis accuracy. Comparison and validation between the proposed WKN-BiLSTM-AM method and other state-of-the-art models are given on the Case Western Reserve University and Paderborn University datasets. The experimental results verify the effectiveness of the proposed model in bearing fault diagnosis, and the model’s generalization capability.

Analytical Assessment of the Structural Behavior of a Specific Composite Floor System at Elevated Temperatures Using a Newly Developed Hybrid Intelligence Method

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures.

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

A Vision and Semantics-Jointly Driven Hybrid Intelligence Method for Automatic Pairwise Language Conversion

In modern society where connections among nations have been more and more frequent, it remains important to realize automatic language conversion methods for the public. Currently, most the existing research works were conducted upon the basis of semantics analysis. But from the perspective of linguistics, the vision characteristics is also a kind of concomitant existence. To deal with such challenge, this paper proposes a vision and semantics-jointly driven hybrid intelligence method for automatic pairwise language conversion. The whole technical framework can be divided into two components: vision sensing part and semantics sensing part. For the former, the virtual reality is introduced for use to capture the visual feature representation for language contents. For the latter, the recurrent neural network model is utilized to capture semantic feature representation for language texts. They are then integrated into a jointly driving framework, so as to improve the conversion efficiency. Taking two dialects (Sichuan dialect and Chongqing dialect) in China as the example, the simulative experiments are conducted on massive real-world training corpus to evaluate the proposal. The results can reflect feasibility of it.

Introduction to the Special Issue on Hybrid Intelligent Methods for Forecasting in Resources and Energy Field

Cite This Article Hong, W., Liang, Y. (2023). Introduction to the Special Issue on Hybrid Intelligent Methods for Forecasting in Resources and Energy Field. CMES-Computer Modeling in Engineering & Sciences, 134(2), 763–766.

Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems

The load requirement of a microgrid system fluctuates on an hourly manner. As the load demand rises and falls, utilities set various rates at different hours of the day, which is known as the time-of-usage (TOU) based electricity pricing. The two types of load that make up the microgrid system's hourly demand are elastic and inelastic. Demand-side management (DSM) restructures the entire demand model based on demand-price elasticity by moving elastic loads from peak load hours to those hours when the utility charges less. This research employs a hybrid intelligence method to reduce the total cost of three microgrid systems taking into account the implementation of DSM. Practical complexities like unit-commitment of the dispatchable fossil fuelled generators were considered. Numerical results project noticeable savings ranging from 8% to 18% for all the test systems when the DSM strategy was incorporated. A detailed comparative analysis with the results available in the literature corroborates the effectiveness of the proposed hybrid approach. Measures of central tendencies, non-parametric statistical analysis and algorithm execution time show better results of the hybrid optimization tool presented in this paper.

Enhancement and extraction of periodic transient based on hybrid intelligent fusion algorithm and its application in rolling bearing fault detection

Weak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature frequency of rolling bearing fault by comprehensively using a variety of intelligent signal processing technologies, possessing the advantage of each technology. Considering the rolling bearing often construct a transmission device combination with gear and shaft, its vibration signal is often vulnerable to other multi-morphology components, such as harmonic modulation, noise. Thus, how to identify the fault frequency in repetitive transients is crucial to accurately identify rolling bearing fault detection. To address this issue, a novel hybrid intelligent method is proposed to effectively apply on periodic transients extraction, enhancement and rolling bearing fault diagnosis. The innovation of this method is to solve three problems, namely, the separation of multi-morphology components, noise reduction without periodic transients distortion, weak fault frequency enhancement. The proposed method is tested and validated on simulated signal, rolling bearing fault signal from accelerated rolling bearing degradation rig. In addition, comparisons with other classical rolling bearing fault detection methods have been conducted to highlight the superiority of the proposed methodology.

Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods

Difficult laryngoscopy is associated with airway injury, and asphyxia. There are no guidelines or gold standards for detecting difficult laryngoscopy. There are many opinions on which predictors to use to detect difficult laryngoscopy exposure, and no comprehensively unified comparative analysis has been conducted. The efficacy and accuracy of deep learning (DL)-based models and machine learning (ML)-based models for predicting difficult laryngoscopy need to be evaluated and compared, under the circumstance that the flourishing of deep neural networks (DNN) has increasingly left ML less concentrated and uncreative. For the first time, the performance of difficult laryngoscopy prediction for a dataset of 671 patients, under single index and integrated multiple indicators was consistently verified under seven ML-based models and four DL-based approaches. The top dog was a simple traditional machine learning model, Naïve Bayes, outperforming DL-based models, the best test accuracy is 86.6%, the F1 score is 0.908, and the average precision score is 0.837. Three radiological variables of difficult laryngoscopy were all valuable separately and combinedly and the ranking was presented. There is no significant difference in performance among the three radiological indicators individually (83.06% vs. 83.20% vs. 83.33%) and comprehensively (83.74%), suggesting that anesthesiologists can flexibly choose appropriate measurement indicators according to the actual situation to predict difficult laryngoscopy. Adaptive spatial interaction was imposed to the model to boost the performance of difficult laryngoscopy prediction with preoperative cervical spine X-ray.

Transformer Fault Diagnosis Based on an Improved Sine Cosine Algorithm and BP Neural Network

Background: The operation state evaluation and fault location of the transformer is one of the technical bottlenecks restricting the safe power grid operation. Methods: A hybrid intelligent method based on the Improved Sine Cosine Algorithm and BP neural network (ISCA-BP) is developed to improve the accuracy of transformer fault diagnosis. First, the cloud model is introduced into the Sine Cosine Algorithm (SCA) to determine the conversion parameter of each individual to balance the global search and local exploitation capabilities. After that, six popular benchmark functions are used to evaluate the effectiveness of the proposed algorithm. Finally, based on the dissolved gas analysis technology, the improved SCA algorithm is employed to find the optimal weight and threshold parameters of the BP neural network, and the transformer fault classification model is established. Results: Simulation results indicate that the improved SCA algorithm exhibits strong competitiveness. Furthermore, compared with the BP neural network optimized by the Sine Cosine Algorithm (SCA-BP) and BP neural network, the ISCA-BP method can significantly improve the diagnostic accuracy of transformer faults. Conclusion: The proposed intelligent method can provide a valuable reference idea for transformer fault classification.

Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods

Spinel ferrite exhibits large magnetocaloric effect that promotes its candidature for solid state magnetic refrigeration technology. This method of cooling is promising and can conveniently replace the gas-compression method of refrigeration due to its energy saving capacity and ecological cleanliness. Among the available magnetocaloric materials, spinel ferrite metal oxide enjoys low production cost, non-toxicity, ease of preparation and non-oxidative. However, effective utilization of this magnetocaloric material for cooling technology requires determination of its magnetic cooling efficiency which is subjected to time consuming, intensive and laborious experimental procedures. This work employs the applied magnetic field, ionic radii and the concentrations of spinel ferrite constituents to develop a genetically hybridized support vector regression model (G-SVR) and extreme learning machine (ELM) intelligent model for determining magnetic cooling efficiency of spinel ferrite compounds. The developed G-SVR model outperforms ELM model for the testing set of spinel ferrite samples with percentage improvement of 60.57%, 19.69% and 64.73% using root mean square error, correlation coefficient and mean absolute error respectively, as performance measuring parameters. Influence of metal ions such as zinc, nickel and cadmium on magnetic cooling efficiency of various kinds of spinel ferrite compounds was investigated using the developed models. The presented intelligent approaches for spinel ferrite magnetic cooling efficiency determination in this work, with characteristic lower cost, quickness, effectiveness and precision would definitely facilitate and strengthen practical implementation of magnetic refrigeration technology for energy conservation and environmental friendliness.

A hybrid GBPSO algorithm for permeability estimation using particle size distribution and porosity

Particle size distribution measurements can be used for permeability estimation, and it is widely accepted that there exhibits a certain degree of correlation between permeability and porosity. In this paper, an efficient, low-cost, and reliable approach is used to develop an empirical correlation for estimating permeability based on particle size distribution characteristics and porosity in two modes: mode #1 includes 5% (D5), 10% (D10) and 60% (D60) of the cumulative passing particle size distribution curve and porosity for situations where porosity is known, and mode #2 where porosity is unknown. To optimize the coefficient of the proposed relationships, genetic-binary particle swarm optimization algorithm is used. A database consisting of 50 samples collected from four wells drilled in two neighboring pads in Western Canada were used, and their permeability values were predicted successfully. A validation based on a reference study and an application of sand completion design based on the finding of this study are also discussed. The novelties of the proposed approach are examining the effect of fines content, investigating the full range of particle size distribution curve, and using a hybrid intelligent method to optimize the coefficients of the correlations. In addition to sand completion deigns purposes, the proposed method can be used in enhanced oil recovery studies, reservoir management, and reservoir simulation applications.

Bismuth oxychloride photocatalytic wide band gap adjustment through oxygen vacancy regulation using a hybrid intelligent computational method

Bismuth oxychloride is a lamellar semiconductor with fascinating features in photocatalysis due to its excellent charge storage capacity, sufficient reduction/oxidation potentials, stable chemical properties, wide distribution of elements, and low toxicity. The wider application of this semiconductor for many technological and industrial applications is hindered by the resistive factors of wide band gap and the ease of recombination of photo-generated electrons and holes. Oxygen vacancy creation in bismuth oxychloride semiconductor controls and adjusts the band gap to the desired spectrum region and subsequently acts like electron trapping centers for the prevention of carrier's re-union. The formation of oxygen vacancy peaks for the lattice and adsorbed oxygen species in the de-convoluted O 1s spectra of x-ray photoelectron spectroscopy (XPS) at a particular value of binding energy experimentally confirms oxygen vacancy creation for photocatalytic activity enhancement. This work focuses on modeling the energy gap (Eg) of bismuth oxychloride semiconductor with oxygen vacancies using the binding energy (BE) of the de-convoluted O 1s lattice oxygen and oxygen vacancy as descriptors through hybrid particle swarm-based support vector regression (PSVR) model as well as stepwise regression (STR) model. The developed PSVR-Eg model shows performance improvement of 23.38%, 55.96%, and 66.54% over the STR-Eg model on the basis of the coefficient of correlation, root means square error, and mean absolute error. The developed PSVR-Eg further predicts the influence of lattice oxygen and oxygen vacancy on the energy gap of the semiconductor. The characteristic precision, accuracy, and quickness of the developed models are highly meritorious in determining the level of oxygen vacancy that adjusts the semiconductor energy gap to the desired value for photocatalytic activity enhancement at a reduced cost and without experimental stress.