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The Manson-Halford (M-H) nonlinear cumulative damage model is widely applied for fatigue life analysis problems under multi-level loading. In this model, the influence of loading sequence on the fatigue life can be better considerer, but the loading interaction effect is ignored. An improved whale optimization algorithm (IWOA) by integrating multiple strategies is proposed. The ability of global search and local exploitation is balanced and improved through nonlinear convergence factor, adaptive weighting factors and the Cauchy reverse learning strategies. In order to fully account for loading interaction effect, loading weighting factors are introduced to modify the M-H model, and the parameters are optimized through the global search properties of IWOA. The model is evaluated on multi-level loading fatigue experimental data from five metal materials and two aluminum alloy welded joints. The results suggest that the proposed IWOA has better optimization accuracy compared to the standard whale optimization algorithm (WOA). The proposed modified M-H model has better prediction performance compared to the four traditional cumulative damage models, which can be effectively applied to multi-level loading fatigue life analysis problems under actual working conditions. The proposed model is useful for the study of fatigue life evaluation methods. [ABSTRACT FROM AUTHOR]

Accurately estimating the State of Health (SOH) of batteries is of great significance for the stable operation and safety of lithium batteries. This article proposes a method based on the combination of Capacity Incremental Curve Analysis (ICA) and Whale Optimization Algorithm-Radial Basis Function (WOA-RBF) neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries. Firstly, preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage (Q-V) curve, convert the Q-V curve into an IC curve and denoise it, analyze the parameters in the IC curve that may serve as health features; Then, extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features, and perform correlation analysis using Pearson correlation coefficient method; Finally, the WOA-RBF algorithm was used to estimate the battery SOH, and the training results of LSTM, RBF, and PSO-RBF algorithms were compared. The conclusion was drawn that the WOA-RBF algorithm has high accuracy, fast convergence speed, and the best linearity in estimating SOH. The absolute error of its SOH estimation can be controlled within 1%, and the relative error can be controlled within 2%. [ABSTRACT FROM AUTHOR]

The increasing prevalence of cyber threats has created a critical need for robust defense against such incidents. Many Cyber Intrusion Detection Systems (CIDSs), utilizing machine learning have been developed for this purpose. Although, these recent CIDSs have provided the capability to analyze vast amounts of data and identify malicious activities, there are still challenges to be tackled to enhance their effectiveness. The exponential growth of the search space is one of these challenges which makes finding an optimal solution computationally infeasible for large datasets. Furthermore, the weight space while searching for optimal weight is highly nonlinear. Motivated by the observed characteristics, complexities, and challenges in the field, this paper presents an innovative (CIDS) named ANWO-MLP (Adaptive Nonlinear Whale Optimization Multi-layer Perceptron). A novel feature selection method called ANWO-FS (Adaptive Nonlinear Whale Optimization-Feature Selection) is employed in the proposed CIDS to identify the most predictive features enabling robust MLP training even in the highly nonlinear weight spaces. The insider threat detection process is improved by investigating vital aspects of CIDS, including data processing, initiation, and output handling. We adopt ANWOA (previously proposed by us) to mitigate local stagnation, enable rapid convergence, optimize control parameters, and handle multiple objectives by initializing the weight vector in the ANWO-MLP training with minimal mean square error. Experiments conducted on three highly imbalanced datasets demonstrate an average efficacy rate of 98.33%. The details of the results below show the robustness, stability, and efficiency of the proposed ANWO-MLP compared to existing approaches. [ABSTRACT FROM AUTHOR]

Since many years ago, walnuts have been extensively available around the world and come in various quality varieties. The proper variety of walnut can be grown in the right area and is vital to human health. This fruit's production is time-consuming and expensive. However, even specialists find it challenging to differentiate distinct kinds since walnut leaves are so similar in color and feel. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The classification process can now be carried out automatically from leaf photos thanks to technological advancements. The walnut data set was applied to the suggested deep learning model. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The walnut data set, which consists of 18 different types of 1751 photos, was used to test the suggested deep learning model. The three most successful algorithms among the commonly utilized CNN algorithms in the literature were first selected for the suggested model. From the Vgg16, Vgg19, and AlexNet CNN algorithms, many features were retrieved. Utilizing the Whale Optimization Algorithm (WOA), a new feature set was produced by choosing the top extracted features. KNN is used to categorize this feature set. An accuracy rating of 92.59% was attained as a consequence of the tests. [ABSTRACT FROM AUTHOR]

Design-optimization of power transmitted elements such as gears is a complicated process to accomplish as the mathematical model acquires to be in compliance with the optimization algorithm used. Utilization of technical standards and handbooks assisted engineers and designers with designing the gears, however the results were inefficient, thus meta-heuristic algorithms were approved for optimization, for example, genetic algorithm. In this paper, optimization of single stage spur gear pair made of polyoxymethylene material was carried out using genetic algorithm (GA), artificial bee colony (ABC), JAYA, grey wolf optimizer (GWO), and whale optimization algorithm (WOA) in MATLAB. Three single functions were used as objectives, those are, weight, power loss, and center distance. Module (m), face width (b), number of pinion teeth (z 1), and finally profile shifts (x 1, x 2) served as design variables. Unlike steel gears which were constrained by handling root bending stress and flank pressure, more constraints were added to the problem with the increase of complexity such as root and flank temperature, tooth deformation, and wear abrasion. The results showed a significant decrease in all three objectives when optimizing each objective solely with variation of the variables. It is also observed that JAYA has the superiority in decreasing the three objectives in comparison to GA and ABC nevertheless, in agreement with GWO, and WOA. [ABSTRACT FROM AUTHOR]

The article discusses the analysis of the whale optimization algorithm with the aim of applying it to optimize the coefficients of digital filters with linear phase. Digital filters play an important role in signal processing, which are used in many tasks: control and measurement systems, audio and video processing systems, noise reduction tasks, etc. FIR filters are more preferable for some tasks than others because they have the following advantages: the group delay of the filter is constant; FIR filters are always stable. Nowadays, algorithms based on swarm intelligence are widely used. In the theory of artificial intelligence, these algorithms are considered as optimization methods. The existing methods for solving the problem are analyzed. The whale optimization algorithm has appeared recently. This algorithm has advantages over other algorithms: it does not require information about the gradient; it can bypass local optima; it can be used in a wide range of problems. On the basis of this algorithm, a method for optimizing the characteristics of FIR filters has been developed. The root mean square deviation between the amplitude-frequency response of the prototype and the amplitude-frequency response of the FIR filter to be designed is used as a fitness function. The modeling was carried out on the example of a 24th-order FIR filter of the first type using the Python programming language. The simulation results showed the effectiveness of this algorithm for the synthesis of FIR filters. This method can be successfully used in the design of FIR filters with a linear phase in the creation of various technical means. However, it should be noted that the efficiency of the whale optimization algorithm is lower than the genetic algorithm by almost an order of magnitude in time. Also, the disadvantages include the need to set the boundaries of the search space.

Foam mixers are classified as low-pressure and high-pressure types. Low-pressure mixers rely on agitator rotation, facing cleaning challenges and complex designs. High-pressure mixers are simple and require no cleaning but struggle with uneven mixing for high-viscosity substances. Traditionally, increasing the working pressure resolved this, but material quality limits it at higher pressures. To address the issues faced by high-pressure mixers when handling high-viscosity materials and to further improve the mixing performance of the mixer, this study focuses on a polyimide high-pressure mixer, identifying four design variables: impingement angle, inlet and outlet diameters, and impingement pressure. Using a Full Factorial Design of Experiments (DOE), the study investigates the impacts of these variables on mixing unevenness. Sample points were generated using Optimal Latin Hypercube Sampling—OLH. Combining the Sparrow Search Algorithm (SSA), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), the SSA-CNN-LSTM model was constructed for predictive analysis. The Whale Optimization Algorithm (WOA) optimized the model, to find an optimal design variable combination. The Computational Fluid Dynamics (CFD) simulation results indicate a 70% reduction in mixing unevenness through algorithmic optimization, significantly improving the mixer's performance. [ABSTRACT FROM AUTHOR]

In the context of workflows, scheduling involves the systematic arrangement of tasks, allocation of resources, and coordination of dependencies to achieve desired outcomes. Effective scheduling is paramount in cloud–fog computing environments to harness the full potential of distributed resources while meeting application requirements. However, finding the most efficient solution often involves the quest for an optimal algorithm. An optimal algorithm is one that produces the best possible outcome in terms of a specified objective. While metaheuristic algorithms have been proposed by several researchers to address this issue, the solutions often converge at the local level, limiting their effectiveness. This study proposes the hybrid particle whale optimization algorithm as a solution for workflow scheduling problems. It is a combination of particle swarm optimization (PSO) and the whale optimization algorithm (WOA). By integrating the features of both algorithms, this algorithm tries to avoid becoming trapped in local optima. This algorithm aims to minimize the total execution cost, makespan, and energy consumption of tasks in a cloud–fog computing environment. The performance of the proposed algorithm is compared with that of the round robin, ant colony optimization, PSO, and WOA algorithms using five different scientific workflows, namely, the Inspiral, Montage, Epigenomics, Cybershake, and Sipht algorithms, which deploy various numbers of tasks and VMs. The proposed algorithm outperformed the other existing scheduling strategies. Moreover, the approach provides users with the flexibility to customize the scheduling strategy based on their preferences. Overall, this paper presents a novel approach for workflow scheduling in cloud–fog computing environments that improves the performance and efficiency of applications while also providing users with greater control over the scheduling process. [ABSTRACT FROM AUTHOR]

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems. [ABSTRACT FROM AUTHOR]

In a diesel engine, piston slap commonly occurs concurrently with fuel combustion and serves as the main source of excitation. Although combustion pressure can be measured using sensors, determining the slap force is difficult without conducting tests. In this study, we propose a method to identify the slap force of the piston to solve this difficult problem. The traditional VMD algorithm easily receives noise interference, which affects the value of parameter combination [k, α] and thus affects the extraction accuracy of the algorithm. First, we obtain the transfer function between the incentive and vibration response through percussion tests. Secondly, a variational modal decomposition method based on whale algorithm optimization is used to separate the slap response from the surface acceleration of the block. Finally, we calculated the slap force using the deconvolution method. Deconvolution is a typical inverse problem of mathematics, often prone to ill-conditioning, and the singular value decomposition and regularization method is used to overcome this flaw and improve accuracy. The proposed method provides an important means to evaluate the angular distribution of the slap force, identify the shock positions on the piston liner, and determine the peak value of the waveform which helps us analyze the vibration characteristics of the piston and optimize the structural design of the engine. [ABSTRACT FROM AUTHOR]

The photovoltaic/thermal (PV/T) flat-panel technology has numerous advantages over PV modules and separately mounted solar thermal collectors regarding overall effectiveness and space-saving. Hybrid PV/T solar collectors' thermal and electrical performance is influenced by design parameters like mass flow rate, tube diameter, tube spacing, packing factor, and absorber conductivity. This paper focused on using several meta-heuristic optimization techniques, incorporating the following: multiverse algorithm, dragonfly algorithm, sine–cosine algorithm, moth-flame algorithm, whale algorithm, particle swarm algorithm, ant-lion algorithm, grey wolf algorithm, and particle swarm optimization algorithm in PV/T collector optimal design according to maximum total efficiency obtained. The outcomes of the various algorithms revealed that the maximum electrical efficiency of the PV/T collector ranged from 13.85 to 14.28%, while the maximum thermal efficiencies ranged from 41.41 to 52.08% under standard test conditions (1000 W/m2 and 25 °C). The optimized values for the design parameters of the PV/T collector were as follows: the absorber conductivity was determined to be 356.6 W/m K, the packing factor was optimized to 0.7, the mass flow rate was set at 0.019 kg/s, the tube width was determined to be 0.035 m, and the tube spacing was optimized to 0.0524 m. The results indicated that the grey wolf optimizer (GWO) algorithm proved to be highly effective in optimizing the design parameters of PV/T collectors. Furthermore, the study examined the relationship between the temperature of PV modules and PV/T collectors by considering variations in mass flow rate, packing factor, and tube width at different solar radiation levels. The results confirmed that the PV/T collector temperature exhibited improvements compared to the PV module temperature. As a result, this led to higher electrical efficiency and an overall increase in the total efficiency of the PV/T collector. [ABSTRACT FROM AUTHOR]

Due to the intermittence of renewable sources, reliable customer service is not guaranteed unless hybrid-energy systems emerged with energy storage is employed together with an appropriate energy management system (EMS). Usually integrating different renewable energy sources can able to supply a reliable power to consumers. In this paper, an efficient EMS for a four-wire, 1000 kW Microgrid system is presented. The studied system consists of three wind farms and three solar plants with a central link to battery banks, an electrolyzer, and a fuel-cell system. A hybrid optimization algorithm is adapted to track the maximum power of PV arrays under variable weather and partial shading conditions. The tracking algorithm merges the benefits of the Whale Optimization and Perturb-and-Observe (P&O) technique. To track the maximum available power of wind plants, a P&O algorithm is established by calculating the proper duty cycle of a boost dc/dc converter that regulates the load current. A TS-Fuzzy-based controller for the inverter is applied to provide an acceptable power quality level associated with the battery bank, electrolyzer, and fuel cell. The suggested inverter controllers provide multi tasks such as reactive power compensator, voltage regulator under unbalanced loads, and active filter. The validity of the proposed system is verified via Extensive Hardware-in-Loop (HIL). [ABSTRACT FROM AUTHOR]

Extensive use of groundwater is a result of the growing population; in relation to this, studies have focused on groundwater conservation measures. This study identified groundwater artificial recharge zones (GWARZs) in the upper Manimuktha sub-basin through the application of remote sensing and GIS. A spatial analysis using the analytical hierarchical process (AHP) and weighted overlay analysis (WOA) was employed by integrating several spatial thematic layers such as geology, geomorphology, aquifer thickness, lineament density (LD), drainage density (DD), soil, slope, rainfall, and land use/land cover (LULC) in order to classify the GWARZs. The geomorphology along with lithology, higher aquifer thickness, low lineament densities, higher drainage densities, and gentle slope regions, were identified as suitable areas for artificial recharge zones. The study area was divided up into five classifications based on the integration analysis: excellent (41.1 km2), good (150.6 km2), moderate (123.9 km2), bad (125.5 km2), and very poor (57.7 km2). Excellent and good GWARZs were identified in the eastern and central regions of the study area. The final outcomes of this research were evaluated with seasonal electrical conductivity (EC) variations. The majority of samples with minor seasonal EC variations were observed in the excellent and good GWARZ categories. The results showed that the spatial analysis tool is useful for GWARZ delineation and sustainably managing groundwater resources. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy is an effective method for identifying wool and cashmere fibers, with high spectral data providing a wealth of information. However, a key issue is that the accuracy and robustness of subsequent estimates can be reduced by redundant and interfering wavelengths. For this reason, a novel interval-wavelength cascaded optimization method is proposed. Initially, the collected spectral data are preprocessed by standard normal variate transformation (SNV) to eliminate the scattering effect. Then, the backward interval partial least squares (BiPLS) algorithm is applied for the preliminary selection of spectral intervals, followed by the application of three different variable selection algorithms, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and whale optimization algorithm (WOA), for secondary wavelength optimization, respectively. Finally, both support vector machine (SVM) and random forest (RF) discriminant models are built to identify the extracted subset of wavelengths. In the experimental stage, the cascade method BiPLS-WOA selects 36 wavelengths, in SVM, the accuracy of the validation set reaches 96.9%, and the area under the ROC curve (AUC) can reach 99.3%. The results demonstrate that the proposed method can eliminate redundant and collinear variables, thereby validating the effectiveness of distinguishing wool and cashmere fibers.

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9. [ABSTRACT FROM AUTHOR]

The coronavirus diseases (COVID-19) are transmittable diseases which are caused by Severe Acute Respiratory Syndrome human coronavirus (SARS-CoV). This paper describes the identification of coronavirus disease infections and better treatments based on recent technology. The categorization and projection of COVID-19 from the dataset of the most significant Computed tomography (CT) image features. The CT image databases are collected from the online access Kaggle database. The features are extracted by the CT images with the Gray-Level Co-occurrence Matrix (GLCM) feature extraction techniques. The selected features are then segmented using the Improved Whale Optimization and Moth Flame Optimization (IWOMFO) algorithm. Improved Whale Optimization and Moth Flame Optimization (IWOMFO) algorithm are utilized to calculate the feature selection for image segmentation, which increases the objective function. Accuracy, F1-score, sensitivity, and precision are the various parameters utilized for evaluating performance. The segmented features were classified using Black widow optimization with a faster recurrent neural network (BWOFRCNN) method. The proposed BWOFRCNN classifier achieves a maximum accuracy of about 98.78%, a sensitivity of about 97.58%, and a precision of about 96.95% when compared to other methods. [ABSTRACT FROM AUTHOR]

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%. [ABSTRACT FROM AUTHOR]

Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet, etc. Textual records frequently contain non-discriminative (noisy and irrelevant) terms that are also high-dimensional, resulting in higher computing costs and poorer learning performance in Text Classification (TC). Feature selection (FS), which tries to discover discriminate terms or features from the textual data, is one of the most effective tasks for this issue. This paper introduces a novel multi-stage term-weighting scheme-based FS model designed for the single-label TC system to obtain the optimal set of features. We have also developed a hybrid deep learning fine-tuning network based on Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for the classification stage. The FS approach is worked on two-stage criteria. The filter model is used in the first stage, and the multi-objective wrapper model, an upgraded version of the Whale Optimization Algorithm (WOA) with Particle Swarm Optimization (PSO), is used in the second stage. The objective function in the above wrapper model is based on a tri-objective principle. It uses the Pareto front technique to discover the optimal set of features. Here in the wrapper model, a novel selection strategy has been introduced to select the whale instead of the random whale. The proposed work is evaluated on four popular benchmark text corpora, of which two are binary class, and two are multi-class. The suggested FS technique is compared against classic Machine Learning (ML) and deep learning classifiers. The results of the experiments reveal that the recommended FS technique is more effective in obtaining better results than the other results. [ABSTRACT FROM AUTHOR]

The solution of ship dynamic positioning thrust allocation is a complex multi-constraint and multi-objective optimization problem, the Whale Optimization Algorithm (WOA) is more convenient and accurate in handling this type of problem compared to traditional thrust allocation algorithms, but at the cost of time, and needs to solve the problem of real-time reliable convergence. Aiming at the above problems, this paper proposed an improved whale thrust allocation algorithm, this algorithm used the solution coefficients of the particular solutions of the equality constraints as individual variables, reducing the search space dimension and range, laying the foundation for obtaining reliable solutions and fast convergence of the algorithm. In addition, the population initialization adopted space block and "elite" screening methods to increase the initial population dispersion and the number of "elite" individuals, improving optimization efficiency and convergence stability. It introduced dynamic population boundaries to obtain a reasonable search space and improve algorithm convergence speed. Finally, it proposed a "Gradient method" population updating method to simulate region traversal search and achieve less iterations to obtain reliable solutions. It takes the Cybership III ship model as an example for simulation verification, the simulation results show that the thrust allocation method is feasible, has good real-time performance and stable convergence, and improves thrust allocation performance. [ABSTRACT FROM AUTHOR]

The study into Wireless Sensor Network (WSN) has grown more crucial as a result of the many Internet of Things (IoT) applications. Energy – Harvesting (EH) technology can extend the lifespan of WSN; however, because the nodes would be difficult to get to during energy harvesting, an energy-efficient routing protocol should be developed. The use of clustering in this study balances energy consumption across all Sensor Node (SN) and reduces traffic and overhead throughout the data transmission phases of WSN. Cluster Head (CH) selection step of the Optimized Energy Efficient-Hierarchical Clustering Based Routing (OEE-HCR) technique involves sending data to the closest CH. In order to analyse and transmit each cluster data, CH will need to use more energy, which will hasten and asymmetrically deplete the network. Whale Optimization Algorithm (WOA) algorithm is introduced for the best number of clusters formation with dynamically selecting the CH. Experimentation analysis, results are measured using First Node Dead (FND), the Half Node Dead (HND), Last Node Dead (LND), and Maximum Lifetime Coverage (MLC) at the time of number of data transmission rounds performed in routing algorithms.

Currently， it is challenging to rapidly and precisely locate faults in the secondary systems of intelligent substations through manual analysis of extensive operational data， making it inadequate for meeting the high reliability requirements of smart grids. To address this issue， a fault parameter mapping model for the secondary systems of intelligent substations is proposed. Firstly， a knowledge base for fault localization reasoning is established based on characteristic information to encode fault types. Subsequently， utilizing historical operational data from intelligent substations， a training set for the model is constructed. The support vector machine （SVM） is enhanced by introducing a multi-classifier approach， and its parameters are optimized using the whale optimization algorithm （WOA）. By taking equipment status as input and secondary system fault types as output， a mapping relationship is established between the parameters of secondary system equipment of intelligent substations and the operational state. Finally， the proposed model is validated using actual data as a test set， demonstrating the effectiveness of the proposed model.

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. [ABSTRACT FROM AUTHOR]

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Indoor mobile robots can be localized by using scene classification methods. Recently, two-dimensional (2D) LiDAR has achieved good results in semantic classification with target categories such as room and corridor. However, it is difficult to achieve the classification of different rooms owing to the lack of feature extraction methods in complex environments. To address this issue, a scene classification method based on a multi-scale convolutional neural network (CNN) with long short-term memory (LSTM) and a whale optimization algorithm (WOA) is proposed. Firstly, the distance data obtained from the original LiDAR are converted into a data sequence. Secondly, a scene classification method integrating multi-scale CNN and LSTM is constructed. Finally, WOA is used to tune critical training parameters and optimize network performance. The actual scene data containing eight rooms are collected to conduct ablation experiments, highlighting the performance with the proposed algorithm with 98.87% classification accuracy. Furthermore, experiments with the FR079 public dataset are conducted to demonstrate that compared with advanced algorithms, the classification accuracy of the proposed algorithm achieves the highest of 94.35%. The proposed method can provide technical support for the precise positioning of robots. [ABSTRACT FROM AUTHOR]

ABSTRAK Pembangkit Listrik Tenaga Uap Batu bara menghasilkan zat SO2, CO2, NO2, dan partikel yang merupakan emisi dari proses pembakarannya. Emisi tersebut menyebabkan peningkatan efek pemanasan global, kerusakan lingkungan, dan merusak kesehatan. Dikarenakan pembangkit batu bara masih banyak digunakan di Indonesia maka Economic Emission Dispatch yang dikombinasikan dengan algoritma Whale Optimization Algorithm menjadi solusi dalam pengurangan emisi pembangkit listrik. Dengan cara memasukkan nilai weighted sum pada fungsi biaya dan emisi agar dapat dilihat pembangkit mana yang memiliki kadar emisi lebih rendah untuk memenuhi beban listrik yang dibutuhkan. Pada simulasi tersebut didapatkan pengurangan kadar emisi sebesar 12,73% dibandingkan tanpa menggunakan weighted sum dengan algortima WOA. Hal ini mampu untuk membantu program pemerintah dalam mengurangi emisi karbon agar tidak melebihi standar emisi yang telah ditetapkan sesuai dengan Peraturan Menteri Nomor P.15/MENLHK/SETJEN/KUM.1/4/2019. Kata kunci: PLTU batu bara, emisi, EED, WOA, weighted sum ABSTRACT Coal-fired power plants produce SO2, CO2, NO2, and particulate matter as emissions from the combustion process. These emissions cause increased global warming effects, environmental damage, and damage to health. coal plants are still widely used in Indonesia, Economic Emission Dispatch combined with the Whale Optimization Algorithm is a solution for reducing power plant emissions. By entering the weighted sum value in the cost and emission functions, it can be seen which plants have lower emission levels to meet the required electricity load. The simulation obtained a reduction in emission levels of 12.73% compared to not using weighted sum with the WOA algorithm. This method is able to help government programs in reducing carbon emissions so as not to exceed the emission standards that have been set in accordance with Peraturan Menteri Nomor P.15/MENLHK/SETJEN/ KUM.1/4/2019. Keywords: coal power plant, emission, EED, WOA, weighted sum

Daily increases in electricity prices accompany daily increases in energy consumption and use. An effective load-balancing scheduling system is necessary for the lowest cost of use and the lowest cost. Despite these devices having a significant capacity for power consumption, they must find a means to balance the load at a low price. Even if lowering the voltage is challenging, it is possible to do it at the lowest cost. Hybrid Whale Differential Evolution (HWDE) is a new optimization method that combines the well-known approaches of the Whale Optimization Algorithm (WOA) and Enhanced Differential Evolution (EDE). By balancing the required Real-Time Price (RTP) and Critical Peak Price (CPP) loads, WOA and EDE capabilities can save costs and ensure the device receives sufficient voltage. The three most recent performance indicators are kWh per charge, energy usage, and the maximum-average ratio. Existing models are evaluated according to their expenses (in rupees), energy consumption, cost per kilowatt-hour, and total cost. All simulation results indicate that HWDE is the optimal solution in every circumstance. In MATLAB simulations, HWDE consistently outperforms its rivals.

Foam mixers are classified as low-pressure and high-pressure types. Low-pressure mixers rely on agitator rotation, facing cleaning challenges and complex designs. High-pressure mixers are simple and require no cleaning but struggle with uneven mixing for high-viscosity substances. Traditionally, increasing the working pressure resolved this, but material quality limits it at higher pressures. To address the issues faced by high-pressure mixers when handling high-viscosity materials and to further improve the mixing performance of the mixer, this study focuses on a polyimide high-pressure mixer, identifying four design variables: impingement angle, inlet and outlet diameters, and impingement pressure. Using a Full Factorial Design of Experiments (DOE), the study investigates the impacts of these variables on mixing unevenness. Sample points were generated using Optimal Latin Hypercube Sampling—OLH. Combining the Sparrow Search Algorithm (SSA), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), the SSA-CNN-LSTM model was constructed for predictive analysis. The Whale Optimization Algorithm (WOA) optimized the model, to find an optimal design variable combination. The Computational Fluid Dynamics (CFD) simulation results indicate a 70% reduction in mixing unevenness through algorithmic optimization, significantly improving the mixer’s performance.

In a diesel engine, piston slap commonly occurs concurrently with fuel combustion and serves as the main source of excitation. Although combustion pressure can be measured using sensors, determining the slap force is difficult without conducting tests. In this study, we propose a method to identify the slap force of the piston to solve this difficult problem. The traditional VMD algorithm easily receives noise interference, which affects the value of parameter combination [k, α] and thus affects the extraction accuracy of the algorithm. First, we obtain the transfer function between the incentive and vibration response through percussion tests. Secondly, a variational modal decomposition method based on whale algorithm optimization is used to separate the slap response from the surface acceleration of the block. Finally, we calculated the slap force using the deconvolution method. Deconvolution is a typical inverse problem of mathematics, often prone to ill-conditioning, and the singular value decomposition and regularization method is used to overcome this flaw and improve accuracy. The proposed method provides an important means to evaluate the angular distribution of the slap force, identify the shock positions on the piston liner, and determine the peak value of the waveform which helps us analyze the vibration characteristics of the piston and optimize the structural design of the engine.

Summary: In the upcoming era, one of the emergent technologies is "Wireless Sensor Networks (WSNs)," which has provided various solutions in the field of defense, surveillance, and so on. But the main problems in the WSNs are security and energy‐efficient routing to the researchers. The deployment of security algorithms can monitor only the unauthorized signals to ensure security, but it lacks and leads to higher energy consumption, whereas the deployment of energy‐efficient routing algorithms is used only for the selection of routing paths, but it fails to ensure security. Hence, the main intent of this research work is to solve both problems by the following proposed models. Model 1 is "Identity‐Based Aggregate Signatures (IBAS)" that is the most effective security‐based framework protocol in this research work. Model 2 is the integration of IBAS and "Fuzzy Logic System (FLS)" that acts as an energy‐efficient algorithm, and here, FLS is used to find the optimal path from source to destination to transmit the data. Model 3 is the combination of IBAS and the "Whale Optimization Algorithm (WOA)" for higher energy efficiency and auto localization by analyzing the entire route in WSN. Model 4 is the combination of IBAS, WOA, and the "Reinforcement Theory of WOA (RTWOA)" energy‐efficiency model with a twofold and weight factor strategy for better speed and accuracy. These models ensure security, energy efficiency, and auto localization. The simulation parameters such as "Energy Efficiency," "Packet Delivery Ratio," "Average delay," "Throughput," and "Energy Consumption" are evaluated, and results show that the performance of energy efficiency and security is higher in Model 4 when compared to the other three models. [ABSTRACT FROM AUTHOR]

The partial discharge caused by the insulation defect of the transformer winding endangers the safe operation of the power supply system, and the current detection of partial discharge is greatly affected by factors such as electromagnetic interference and sensor arrangement. Therefore, this study proposes an arrangement of built-in sensors at the transformer inspection port, and uses the whale optimization algorithm (WOA) to estimate the location of partial discharge points. By establishing a three-phase transformer winding model through COMSOL, the propagation law of ultrasonic waves in the transformer winding is simulated and analyzed, and the time delay information required by the WOA algorithm for positioning is obtained. The simulation results show that for the partial discharge points of the B-phase high and low voltage windings, the layout of the asymmetric quadrilateral array using the WOA algorithm has a smaller positioning error than the symmetrical quadrilateral array, and the average error is 3.9 cm. Additionally, for A-phase, two built-in detection points at the mouth can effectively locate the partial discharge point, and the average error is 5.1 cm. The obtained results can provide a certain reference value for the partial discharge positioning of the on-site transformer. [ABSTRACT FROM AUTHOR]

Variational mode decomposition (VMD) is widely used in the fault diagnosis of rotating equipment. The kurtosis index is typically utilized as the objective function to determine the optimal decomposition mode in conventional VMD. However, kurtosis is easily interfered with by abnormal impact signals, seriously affecting the accuracy of VMD in the fault extraction process. Therefore, the instability of the kurtosis index for anomalous disturbances should be addressed. In addition, the number of modal decompositions K and the penalty factor α are not wisely selected, exerting a significant influence on the decomposition effect of VMD. In this study, we propose an enhanced method for fault diagnosis of rotating machineries under abnormal impact interference, namely the improved nonlinear VMD method based on the l 1/2 norm (INVL). Initially, we employ the whale optimization algorithm to optimize the combination of parameters [ K , α ] with the aim of achieving the most favorable decomposition outcomes. Subsequently, the decomposed modes undergo preprocessing using the nonlinear tanh function to mitigate the impact of amplitude shocks. Furthermore, we utilize the sparse l 1/2 norm to select fault feature components, thereby evaluating the sparse distribution of modes. The modes exhibiting the lowest l 1/2-norm values are chosen for information reconstruction and envelope analysis. To assess the effectiveness of the proposed method, we conduct simulations and experimental verifications. The results demonstrate that the INVL method effectively suppresses abnormal shocks and successfully extracts bearing fault features in the presence of anomalous collision disturbances. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features. [ABSTRACT FROM AUTHOR]

Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives. [ABSTRACT FROM AUTHOR]

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images. [ABSTRACT FROM AUTHOR]

Daily increases in electricity prices accompany daily increases in energy consumption and use. An effective load-balancing scheduling system is necessary for the lowest cost of use and the lowest cost. Despite these devices having a significant capacity for power consumption, they must find a means to balance the load at a low price. Even if lowering the voltage is challenging, it is possible to do it at the lowest cost. Hybrid Whale Differential Evolution (HWDE) is a new optimization method that combines the well-known approaches of the Whale Optimization Algorithm (WOA) and Enhanced Differential Evolution (EDE). By balancing the required Real-Time Price (RTP) and Critical Peak Price (CPP) loads, WOA and EDE capabilities can save costs and ensure the device receives sufficient voltage. The three most recent performance indicators are kWh per charge, energy usage, and the maximum-average ratio. Existing models are evaluated according to their expenses (in rupees), energy consumption, cost per kilowatt-hour, and total cost. All simulation results indicate that HWDE is the optimal solution in every circumstance. In MATLAB simulations, HWDE consistently outperforms its rivals. [ABSTRACT FROM AUTHOR]

The accurate calculation of the height of fractured water-conducting zone (FWCZ) is of great significance for mine optimization design, water disaster prevention, and safety production of the coal mines. In this article, a height-prediction model of FWCZ based on extreme learning machine (ELM) is proposed. To address the issues of low prediction accuracy and challenging parameter optimization, we optimized the ELM model using the gray-wolf optimization algorithm (GOA), whale optimization algorithm (WOA), and salp optimization algorithm (SOA). These optimization algorithms mitigate the issues of slow convergence, poor stability, and local optimality associated with traditional neural networks. The mining depth, mining height, overburden strata structure, working face length, and coal seam dip angle are selected as the main controlling factors for the height of FWCZ. A total of 42 fields-measured samples are collected and divided into 2 subsets for training and validating with a ratio of 36/6. The prediction capability of GOA-ELM, WOA-ELM, and SOA-ELM models are evaluated and compared, and the results show that the calculation results of the three models are optimized compared with the ELM model. The prediction capability of GOA and WOA are similar, while the prediction results of SOA-ELM are better than the other two models, and the relative errors of the test sets are all less than 10%. Therefore, the SOA-ELM model is finally applied to predict the height of FWCZ formed after the mining of No.15 coal seam in Xinjian Coal Mine. Finally, we verified the prediction results using measured data from the borehole television detection instrument, which showed good consistency. This provides further evidence of the effectiveness of the swarm intelligence optimization algorithm in predicting the height of FWCZ. [ABSTRACT FROM AUTHOR]

In this work, the technique of spin arc welding has been used to weld HSLA steel of grade 15CDV6. Experiments were performed according to the L9 Taguchi matrix using current, travel speed, wire feed rate, and spin rotation speed as input parameters. The quality of the weld was analysed by measuring the bead geometry and microhardness. The Whale Optimization Algorithm (WOA) was used to identify the optimized welding parameters. The optimised parameters were identified through WOA as follows: current 230 A, voltage 24 V, travel speed 720 mm/min, and spin rotation speed 1500 rpm. The post-weld heat treatment (PWHT) was performed on the optimised welded sample to enhance the weld properties. The microhardness was increased by 79% after PWHT. Ferrite content was found to have reduced after welding and PWHT the grain size and structure of the BM, HAZ, and WM were analysed by EBSD technique. The decrease in ferrite content had a significant effect on the microhardness. The Energy Dispersive Spectroscopy results showed the presence of Vanadium carbide (VC) precipitates which helped in increasing the microhardness. [ABSTRACT FROM AUTHOR]

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9.