Search Results (550)

As a key technology for future decarbonization, storage batteries are widely used in areas such as electric vehicles and power systems. However, battery aging inevitably limits their broader application. To address the low accuracy in predicting discharge voltage under different aging states, this paper proposes the IWOA-ATCN method based on a TCN model with a sliding window mechanism. First, the improved whale optimization algorithm (IWOA) is employed to optimize the hyperparameters in the TCN model, including window size and sliding step, to obtain the optimal sample structure. Then, the temporal attention mechanism is introduced into the TCN model to accurately capture the temporal correlation of discharge voltage, thereby improving the prediction accuracy of long time series data. Finally, the model is tested on the NASA dataset with an RMSE of 0.0072, MAE of 0.0046, and R² of 0.9984. The test results on the PL Sample dataset showed RMSE of 0.0081, MAE of 0.0040, and R² of 0.9983. It is indicated that the prediction accuracy and stability of the IWOA-ATCN model are higher than other models, such as BP, RNN, and LSTM. Full article

In the present study, to address the issue of flow rate instability in the flow boiling experimental system, a flow rate adaptive control system is developed using a single-neuron PID adaptive algorithm, enhanced with the whale optimization algorithm (WOA) for parameter tuning. A recursive least-squares online identification method is integrated to adapt to varying operating conditions. The simulation results demonstrate that in step response the WOA-improved single-neuron PID significantly mitigates the overshoot, with a mere 0.31% overshoot observed, marking a reduction of 98.27% compared to the traditional PID control. The output curve of the WOA-improved single-neuron PID closely aligns with the sinusoidal signal, exhibiting an average absolute error of 0.120, which is lower than that of the traditional PID (0.209) and fuzzy PID (0.296). The WOA-improved single-neuron PID (1.01 s) exhibited a faster return to a stable state compared to the traditional PID (2.46 s) and fuzzy PID (1.28 s). Finally, the effectiveness of the algorithm is validated through practical application. The results demonstrate that, compared to traditional PID and single-neuron PID algorithms, the WOA-improved single-neuron PID algorithm achieves an average flow stability of 9.9848 with a standard error of 0.0914394. It exhibits superior performance, including faster rise and settling times, and higher stability. Full article

An enhanced whale optimization algorithm (WOA) through the implementation of a generalized type-2 fuzzy logic system (GT2FLS) is outlined. The initial idea is to improve the efficacy of the original WOA using a GT2FLS to find the optimal values of the ${\overrightarrow{\mathit{r}}}_{\mathbf{1}}$ and ${\overrightarrow{\mathit{r}}}_{\mathbf{2}}$ parameters of the WOA, for the case of optimizing mathematical functions. In the WOA algorithm, ${\overrightarrow{\mathit{r}}}_{\mathbf{1}}$ is a variable that affects the new position of the whale in the search space, in this case, affecting the exploration, and ${\overrightarrow{\mathit{r}}}_{\mathbf{2}}$ is a variable that has an effect on finding the local optima, which is an important factor for the exploration. The efficiency of a fuzzy WOA with a GT2FLS (FWOA-GT2FLS) is highlighted by presenting the excellent results of the case study of the benchmark function optimization. A relevant analysis and comparison with a bio-inspired algorithm based on artificial bees is also presented. Statistical tests and comparisons with other bio-inspired algorithms and the initial WOA, with type-1 FLS (FWOA-T1FLS) and interval type-2 FLS (FWOA-IT2FLS), are presented. For each of the methodologies, the metric for evaluation is the average of the minimum squared errors. Full article

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium battery electrode (LBE) coatings, this study proposes a method for detection and identification of coatings defects in LBEs based on an improved Binary Tree Support Vector Machine (BT-SVM). Firstly, adaptive Gamma correction is applied to enhance an image, and an improved Canny algorithm combined with morphological processing is used to accurately detect the defect regions. Secondly, the shape and grayscale features of the defects are extracted from the connected defect areas, and these features are then fused and normalized. Finally, a BT-SVM multi-class classification model is constructed, with the Whale Optimization Algorithm (WOA) employed to assist in hyperparameter tuning. The experimental results show that the proposed method can effectively detect and identify five common types of defects in the coating of LBEs, including scratches, bubbles, metal leakage, particles, and decarbonization, with an average detection accuracy of 94.4% and an average detection time of less than 0.2 s, meeting the real-time detection requirements for online defect inspection. After Whale Optimization, the BT-SVM defect recognition model achieves an average recognition accuracy of 98.7%, significantly enhancing the performance of current defect detection technologies for LBE coatings. Full article

To effectively predict the water richness of weathered bedrock aquifers, the West First Plate area of the Hongliulin coal mine was taken as the study area, and 42 sets of pumping test borehole data from the weathered bedrock in the study area were used as training and testing samples. A total of five indicators related to the water richness of weathered bedrock, namely, the aquifer thickness, sand–base ratio, core take rate, degree of weathering, and lithological structure index, were selected. A prediction model for the water richness of weathered bedrock aquifers (WOA-RF) was subsequently proposed by combining the whale optimisation algorithm (WOA) and random forest (RF). This model can predict the water-richness level of weathered bedrock in an area with no pumping test data. The geological information from 98 sets of exploration boreholes in the study area was comprehensively used to achieve water-richness zoning of the weathered bedrock. The results indicated that the WOA is effective in optimising parameters and improving model performance. The accuracies of the optimal WOA-RF model in the training set and the test set were 93.1% and 92.3%, respectively. Compared with those of the single RF model, the accuracy, recall, and F₁ value of the optimal WOA-RF model were increased by 11.3%, 18.2%, and 11%, respectively, and the differences before and after optimisation were obvious. A comparison and analysis of the predictive performance of each model revealed that the overall performance of the WOA-RF model was better than that of the other models. The weathered bedrock in the study area as a whole is weakly to moderately rich in water, and the predicted results are in good agreement with reality, which can provide a reference for future safe production in the West First Plate area. Full article

Ideally, the vibration signal of a rolling bearing should be symmetrical. However, in practical operation, the vibration signals in both time and frequency domains often exhibit asymmetry due to factors such as load, speed, and wear. The relatively weak composite fault characteristics are easily masked. Although the Feature Modal Decomposition (FMD) method is outstanding in diagnosing composite faults in bearings, its effectiveness is easily constrained by parameter selection. To address this, this paper proposes a stepwise parameter adaptive FMD method combined with a clustering algorithm, specifically designed for diagnosing composite faults in rolling bearings. Firstly, this study employs the Density Peak Clustering algorithm to determine the number of modes n in the composite fault vibration signal. Subsequently, considering the signal spectral energy and modal characteristics, a new composite fault index is formulated, namely, the adaptive weighted frequency domain kurtosis-to-information entropy ratio, as the fitness function. The Whale Optimization Algorithm determines the filter length L and the number of segments K, thereby achieving step-wise signal decomposition. Through in-depth analysis of signal symmetry and asymmetry, simulation and experimental verification confirm the effectiveness of this method. Compared with four other index-optimized FMD methods and traditional techniques, this method significantly reduces the influence of parameters on FMD, is capable of separating the characteristic frequencies related to composite faults, and performs excellently in the diagnosis of composite faults in rolling bearings. Full article

Even while living circumstances and construction techniques have generally improved, occupants of these spaces frequently feel unsatisfied with the sense of security they provide, which leads to looking for and eventually enacting ever-more-effective safety precautions. The continuous uncertainty that contemporary individuals experience, particularly with regard to their protection in places like cities, prompted the field of computing to design smart devices that attempt to reduce threats and ultimately strengthen people’s sense of protection. Intelligent apps were developed to provide protection and make a residence a smart and safe home. The proliferation of technology for smart homes necessitates the implementation of rigorous safety precautions to protect users’ personal information and avoid illegal access. The importance of establishing cyber security has been recognized by academic and business institutions all around the globe. Providing reliable computation for the Internet of Things (IoT) is also crucial. A new method for enhancing safety in smart home environments’ sustainability using IoT devices is presented in this paper, combining the Whale Optimization Algorithm (WOA) with Deep Convolutional Neural Networks (DCNNs). WOA-DCNN hybridization seeks to enhance safety measures by efficiently identifying and averting possible attacks in real time. We show how effective the proposed approach is in defending smart home systems from a range of safety risks via in-depth testing and analysis. By providing a potential path for protecting smart home surroundings in a world that is growing more linked, this research advances the state of the art in IoT security. Full article

The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and unbalanced distribution networks. At the stage of selecting the location of energy storage, a comprehensive power flow sensitivity variance (CPFSV) is defined to determine the location of the energy storage. At the energy storage capacity configuration stage, the energy storage capacity is optimized by considering the benefits of peak shaving and valley filling, energy storage costs, and distribution network voltage deviations. Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. The maximum CPFSV of the system decreased by 52% and 75.1%, respectively. In addition, the engineering value of this method is verified through a real-machine system with 199 nodes in a district of Kunming. Therefore, the energy storage configuration method proposed in this article can provide a reference for solving the outstanding problems caused by the large-scale access of EVs and PVs to the distribution network. Full article

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller’s impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency. Full article

Image enhancement is an important step in image processing to improve contrast and information quality. Intelligent enhancement algorithms are gaining popularity due to the limitations of traditional methods. This paper utilizes a transformation function to enhance the global and local information of grayscale images, but the parameters of this function can produce significant changes in the processed images. To address this, the whale optimization algorithm (WOA) is employed for parameter optimization. New equations are incorporated into WOA to improve its global optimization capability, and exemplars and advanced spiral updates improve the convergence of the algorithm. Its performance is validated on four different types of images. The algorithm not only outperforms comparison algorithms in the objective function but also excels in other image enhancement metrics, including peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and patch-based contrast quality index (PCQI). It is superior to the comparison algorithms in 11, 6, 11, 13, and 7 images in these metrics, respectively. The results demonstrate that the algorithm is suitable for image enhancement both subjectively and statistically. Full article

Wave prediction is crucial for ensuring the safety and disaster mitigation of coastal areas, helping to support marine economic activities. Currently, many deep learning models, such as the temporal convolutional network (TCN), have been applied to wave prediction. In this study, a prediction model based on improved TCN-Attention (ITCN-A) is proposed. This model incorporates improvements in two aspects. Firstly, to address the difficulty of calibrating hyperparameters in traditional TCN models, a whale optimization algorithm (WOA) has been introduced to achieve global optimization of hyperparameters. Secondly, we integrate dynamic ReLU to implement an adaptive activation function. The improved TCN is then combined with the attention mechanism to further enhance the extraction of long-term features of wave height. We conducted experiments using data from three buoy stations with varying water depths and geographical locations, covering prediction lead times ranging from 1 h to 24 h. The results demonstrate that the proposed integrated model reduces the $RMSE$ of prediction by 12.1% and $MAE$ by an 18.6% compared with the long short-term memory (LSTM) model. Consequently, this model effectively improves the accuracy of wave height predictions at different stations, verifying its effectiveness and general applicability. Full article

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems. Full article

Amidst the accelerating pace of automation in sheet metal bending, the need for small-batch, multi-varietal, efficient, and adaptable production modalities has become increasingly pronounced. To address this need and to enhance the efficacy of the bending process, this study presents the design and development of an embedded soft PLC (Programmable Logic Controller) rooted in the Codesys development platform and leveraging the ARM Cortex-A55 architecture. This controller employs the EtherCAT communication protocol to facilitate seamless and efficient interactions with fully electric servo-driven CNC (Computerized Numerical Control) bending machinery. To mitigate the challenge of bending springback errors, a finite element simulation model is constructed and refined through the application of ALE (Arbitrary Lagrangian-Eulerian) adaptive grid technology, thereby bolstering simulation precision. Subsequently, an enhanced WOA-BP (Whale Optimization Algorithm—Backpropagation) model, integrating Latin hypercube sampling and neural network techniques, is deployed to anticipate and counteract these springback errors. Experimental outcomes demonstrate that the proposed methodology effectively constrains the final forming angle deviation to within 0.3°, significantly enhancing the reliability and precision of the bending system. This achievement not only underscores the technical feasibility but also contributes to advancing the frontier of sheet metal bending automation. Full article

In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a comprehensive, livable, and disaster-resistant underground ecosystem. This shift has brought increasing attention to the safety of personnel flow in deep spaces. In addressing challenges in deep space passenger flow prediction, such as irregular flow patterns, surges in extreme conditions, large data dimensions, and redundant features complicating the model, this paper proposes a deep space passenger flow prediction model that integrates a Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) network. The model first employs a dual-layer LSTM network structure with a Dropout layer to capture complex temporal dynamics while preventing overfitting. Then, a Self-Attention mechanism and TCN network are introduced to reduce redundant feature data and enhance the model’s performance and speed. Finally, the Beluga Whale Optimization (BWO) algorithm is used to optimize hyperparameters, further improving the prediction accuracy of the network. Experimental results demonstrate that the BWO-TCLS-Self-Attention model proposed in this paper achieves an R2 value of 96.94%, with MAE and RMSE values of 118.464 and 218.118, respectively. Compared with some mainstream prediction models, the R2 value has increased, while both MAE and RMSE values have decreased, indicating its ability to accurately predict passenger flow in deep underground spaces. Full article

Monitoring anthocyanins is essential for assessing nutritional value and the growth status of plants. This study aimed to utilize hyperspectral technology to non-destructively monitor anthocyanin levels. Spectral data were preprocessed using standard normal variate (SNV) and first-derivative (FD) spectral processing. Feature wavelengths were selected using uninformative variable elimination (UVE) and UVE combined with competitive adaptive reweighted sampling (UVE + CARS). The optimal two-band vegetation index (VI2) and three-band vegetation index (VI3) were then calculated. Finally, dung beetle optimization (DBO), subtraction-average-based optimization (SABO), and the whale optimization algorithm (WOA) optimized the extreme learning machine (ELM) for modeling. The results indicated the following: (1) For the feature band selection methods, the UVE-CARS-SNV-DBO-ELM model achieved an R_m² of 0.8623, an RMSE_m of 0.0098, an R_v² of 0.8617, and an RMSE_v of 0.0095, resulting in an RPD of 2.7192, further demonstrating that UVE-CARS enhances feature band extraction based on UVE and indicating a strong model performance. (2) For the vegetation index, VI3 showed a better predictive accuracy than VI2. The VI3-WOA-ELM model achieved an R_m² of 0.8348, an RMSE_m of 0.0109 mg/g, an R_v² of 0.812, an RMSE_v of 0.011 mg/g, and an RPD of 2.3323, demonstrating good performance. (3) For the optimization algorithms, the DBO, SABO, and WOA all performed well in optimizing the ELM model. The R² of the DBO model increased by 5.8% to 27.82%, that of the SABO model by 2.92% to 26.84%, and that of the WOA model by 3.75% to 27.51%. These findings offer valuable insights for future anthocyanin monitoring using hyperspectral technology, highlighting the effectiveness of feature selection and optimization algorithms for accurate detection. Full article