Search Results (46)

Aiming at the problems of voltage distortion caused by voltage drop nonlinearity of voltage source inverter (VSI) tubes, poor recognition accuracy of traditional grey wolf optimization (GWO) in identifying parameters of permanent magnet synchronous motor (PMSM), and slow convergence speed at the later stage, a memory self-learning grey wolf optimization (MSLGWO) algorithm with voltage error compensation is proposed. First, the output voltage error caused by switching tube voltage drop in different conduction states of the inverter is compensated to mitigate its impact on parameter identification. Then, cat mapping is employed to generate the initial position of the grey wolves, combined with an inverse learning strategy to find and select the superior solution among them to secure the variety of the initial population. In addition, the rate of convergence is accelerated by using a cosine-varying convergence factor to maintain a balance between global and local search capabilities. Lastly, inspired by the particle swarm optimization algorithm, a memory-based self-learning mechanism is incorporated to leverage the past experiences of individual wolves. Compared with traditional GWO, the proposed MSLGWO with voltage compensation reduces the identification error by at least 50.0% and completes the process within 0.11 s. Full article

In this study, the economic dispatch problems, which are indispensable in electrical engineering, are addressed utilizing Grey Wolf Optimization (GWO). Conventional mathematical methods struggle to provide quick, reliable solutions to nonlinear problems in power systems with many generation units. An economic dispatch solution operates by allocating generation sets with the lowest fuel costs to meet predetermined power balance constraints. GWO is a meta-heuristic set of rules that has garnered significant attention in the literature due to its suitable exploratory and exploitative properties, rapid and mature convergence rate, and straightforward architecture. When dealing with a nonlinear constraints problem, such as ED, it has gained significant recognition for its balance of exploration and exploitation, reliable convergence characteristics, and simple implementation framework. The proposed Grey Wolf Optimization algorithm is evaluated using real-world generation case benchmark comparisons for 3-unit, 6-unit, and 15-unit systems. Results demonstrate the impact of incorporating renewable energy source (RES) uncertainty; fuel costs increase significantly from USD 7598 to USD 21,240 for the 3-unit system, USD 13,397 to USD 46,216,658 for the 6-unit system, and USD 32,622.55 to USD 33,723.11 for the 15-unit system, highlighting that RES integration is more economically viable in larger systems. The paper’s significant contribution is its essential mechanism for power systems, which enables lower global energy costs, improved operational efficiency, and enhanced grid reliability through strategic resource allocation in a constrained economic dispatch energy management system. Full article

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR. Full article

As a critical precision transmission element in numerical control (NC) machines, the linear rolling guide (LRG) suffers from surface wear degradation, which significantly impairs machining accuracy and operational reliability. Despite its importance, effective identification methods for LRG degradation remain limited. In this study, a hybrid approach combining multi-scale fuzzy entropy (MFE) with a gray wolf-optimized random forest (GWO-RF) algorithm was proposed to identify the surface wear state of the LRG. Preload degradation and vibration signals were collected at three surface wear stages throughout the LGR’s service life. The vibration signals were decomposed and reconstructed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by multi-scale fuzzy entropy analysis of the reconstructed signals. After dimensionality reduction via kernel principal component analysis (KPCA), the processed features were fed into the GWO-RF model for classification. Experimental results demonstrated a recognition accuracy of 97.9%. Full article

Location-based services have important economic and social values. The positioning accuracy and cost have a crucial impact on the quality, promotion, and market competitiveness of location services. Dead reckoning can provide accurate location information in a short time. However, it suffers from motion pattern diversity and cumulative error. To address these issues, we propose a PCA-GWO-KELM optimization gait recognition indoor fusion localization method. In this method, 30-dimensional motion features for different motion patterns are extracted from inertial measurement units. Then, constructing PCA-GWO-KELM optimization gait recognition algorithms to obtain important features, the model parameters of the kernel-limit learning machine are optimized by the gray wolf optimization algorithm. Meanwhile, adaptive upper thresholds and adaptive dynamic time thresholds are constructed to void pseudo peaks and valleys. Finally, fusion localization is achieved by combining with acoustic localization. Comprehensive experiments have been conducted using different devices in two different scenarios. Experimental results demonstrated that the proposed method can effectively recognize motion patterns and mitigate cumulative error. It achieves higher localization performance and universality than state-of-the-art methods. Full article

The energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid energy storage systems by proposing an adaptive EMS based on Dynamic Programming-Optimized Control Rules (DP-OCR). Dynamic programming is employed to optimize the rule-based control strategy, while the grey wolf optimizer (GWO) is utilized to enhance the least squares support vector machine (LSSVM) driving cycle recognition model. The optimized driving cycle recognition model is integrated with the improved rule-based control strategy, facilitating adaptive adjustment of control parameters based on driving cycle identification results. This integration enables optimal power distribution between lithium batteries and supercapacitors, thereby improving the EMS’s adaptability to varying driving conditions and extending battery lifespan. Simulation results under complex driving cycles indicate that, compared to conventional deterministic rule-based EMS and single-battery vehicles, the proposed DP-OCR-based adaptive EMS reduces overall energy consumption by 8.29% and 17.48%, respectively. Full article

The oil field enters a low-production liquid and high-water-cut stage, where the oil–water two-phase flow becomes increasingly complex and diverse. Traditional production profile logging interpretation methods often face significant errors and limitations. To improve interpretation accuracy, this study begins by examining the impact of flow rate and water cut on the oil–water two-phase flow pattern (defined as the characteristic distribution and movement of oil and water phases in the flow, which varies depending on flow conditions such as flow rate and water cut) through numerical simulations and surface experimental observations. The flow characteristics of the oil–water two-phase flow are clarified. Next, the data from surface experiments are collected using a multi-component logging tool, and artificial intelligence algorithms are employed to identify flow patterns and provide data support for production profile interpretation. The genetic algorithm–backpropagation (GA-BP) algorithm is used for flow type classification, with the flow pattern recognition accuracy reaching 93.75% when compared to the experimental results. Finally, the surface experimental data and flow patterns are input into the grey wolf and falcon optimization algorithm–radial basis function (GHOA-RBF) algorithm for training and prediction. The results show that the GHOA-RBF algorithm, incorporating flow patterns, exhibits superior prediction accuracy. Specifically, the coefficient of determination (R²) for oil flow is 0.996, and for water flow, it is 0.993, outperforming traditional RBF neural networks and the GHOA-RBF algorithm without flow pattern incorporation. This demonstrates that this study provides new theoretical support for production profile logging interpretation, with significant practical implications. However, limitations include the reliance on experimental data, which may not fully capture all field conditions, and the computational efficiency of the algorithm, which may need optimization for large-scale applications. Full article

Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis. Full article

This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features. Feature fusion was employed to enhance the discriminative power of the extracted data and the physical parameters calculated by different feature extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. Compared to state-of-the-art methods, our approach achieved better performance in event recognition. Full article

Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions. Full article

With the continuous improvement in production efficiency and quality of life, the requirements of electrical equipment for power quality are also increasing. Accurate detection of various power quality disturbances is an effective measure to improve power quality. However, in practical applications, the dataset is often contaminated by noise, and when the dataset is not sufficient, the computational complexity is too high. Similarly, in the recognition process of artificial neural networks, the local optimum often occurs, which ultimately leads to low recognition accuracy for the trained model. Therefore, this article proposes a power quality disturbance classification strategy based on the variational mode decomposition (VMD) and improved support vector machine (SVM) algorithms. Firstly, the VMD algorithm is used for preprocessing disturbance denoising. Next, based on the analysis of typical fault characteristics, a multi-SVM model is used for disturbance classification identification. In order to improve the recognition accuracy, the improved Grey Wolf Optimization (IGWO) algorithm is used to optimize the penalty factor and kernel function parameters of the SVM model. The results of the final case study show that the classification accuracy of the proposed method can reach over 98%, and the recognition accuracy is higher than that of the other models. Full article

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices. Full article

Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition rate. For this, we propose a novel version of the Binary Grey Wolf Optimizer, designed to reduce the number of sensors, and a Ternary Grey Wolf Optimizer. Together with the Continuous Grey Wolf Optimizer, they yield the CBTGWO (Continuous Binary Ternary Grey Wolf Optimizer). Working with 7 frequencies and starting with 37 sensors, the CBTGWO selects a single sensor and yields a 0-valued false recognition rate. In a single-frequency scenario, starting with 217 sensors, the CBTGWO selects 2 sensors. The false recognition rate is 2%. The acquisition time is 3.2 s, outperforming the GWO and adaptive mixed GWO, which yield 86.4 and 396.6 s. Full article

Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows computers to identify and understand human emotions from speech. These technologies contribute to the creation of innovative human–computer interaction (HCI) applications. Deep learning algorithms, capable of learning high-level features directly from raw data, have given rise to new emotion recognition approaches employing models trained on advanced speech representations like spectrograms and time–frequency representations. This study introduces CNN and LSTM models with GWO optimization, aiming to determine optimal parameters for achieving enhanced accuracy within a specified parameter set. The proposed CNN and LSTM models with GWO optimization underwent performance testing on four diverse datasets—RAVDESS, SAVEE, TESS, and EMODB. The results indicated superior performance of the models compared to linear and kernelized SVM, with or without GWO optimizers. Full article

Human emotions trigger reflective transformations within the brain, leading to unique patterns of neural activity and behaviour. This study connects the power of electroencephalogram (EEG) data to investigate the intricate impacts of emotions, considering their reflective significance in our daily lives, in depth. The versatile applications of EEG signals encompass an array of domains, from the categorisation of motor imagery activities to the control of advanced prosthetic devices. However, EEG data present a difficult challenge due to their inherent noisiness and non-stationary nature, making it imperative to extract salient features for classification purposes. In this paper, we introduce a novel and effective framework reinforced by Grey Wolf Optimisation (GWO) for the recognition and interpretation of EEG signals of emotion dataset. The core objective of our research is to unravel the intricate neural signatures that underlie emotional experiences and pave the way for more nuanced emotion recognition systems. To measure the efficacy of our proposed framework, we conducted experiments utilising EEG recordings from a unit of 32 participants. During the experiments, participants were exposed to emotionally charged video stimuli, each lasting one minute. Subsequently, the collected EEG data of emotion were meticulously analysed, and a support vector machine (SVM) classifier was employed for the robust categorisation of the extracted EEG features. Our results underscore the potential of the GWO-based framework, achieving an impressive accuracy rate of 93.32% in accurately identifying and categorising emotional states. This research not only provides valuable insights into the neural underpinnings of emotions but also lays a solid foundation for the development of more sophisticated and emotionally intelligent human–computer interaction systems. Full article