Search Results (140)

Sulfur dioxide not only affects the ecological environment and endangers health but also restricts economic development. The reasonable prediction of sulfur dioxide emissions is beneficial for formulating more comprehensive energy use strategies and guiding social policies. To this end, this article uses a multiparameter combination optimization gray prediction model (PGM(1, N)), which not only defines the difference between the sequences represented by variables but also optimizes the order of all variables. To this end, this article proposes an improved algorithm for the Dung Beetle Optimization (DBO) algorithm, namely, CSLDDBO, to optimize two important parameters in the model, namely, the smoothing generation coefficient and the order of the gray generation operators. In order to overcome the shortcomings of DBO, four improvement strategies have been introduced. Firstly, the use of a chain foraging strategy is introduced to guide the ball-rolling beetle to update its position. Secondly, the rolling foraging strategy is adopted to fully conduct adaptive searches in the search space. Then, learning strategies are adopted to improve the global search capabilities. Finally, based on the idea of differential evolution, the convergence speed of the algorithm was improved, and the ability to escape from local optima was enhanced. The superiority of CSLDDBO was verified on the CEC2022 test set. Finally, the optimized PGM(1, N) model was used to predict China’s sulfur dioxide emissions. From the results, it can be seen that the error of the PGM(1, N) model is the smallest at 0.1117%, and the prediction accuracy is significantly higher than that of other prediction models. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

This study aims to address the issues of full-coverage path planning in single fields and optimal traversal order in multi-fields in hilly, mountainous areas. To this end, it proposes a full-coverage path planning method based on an improved DBO algorithm. Using the digital elevation model to construct the farmland model, the energy consumption model is introduced into single-field planning to determine the optimal operating direction angle for full-coverage path planning with optimal energy consumption. To address the issues of the traditional DBO algorithm easily falling into a local optimum and the lack of information interaction among populations, a multi-strategy improved DBO algorithm is proposed to determine the optimal traversal sequence for multiple fields. Tent chaotic mapping is used to initialize the population and the Osprey optimization algorithm and adaptive T-perturbation distribution strategy are integrated to enhance the foraging behavior of small dung beetles. This gives the algorithm good global exploration capabilities in the initial stage and strong local exploitation capabilities in the later stage. The simulation results show that the total energy consumption of energy-optimal path planning is 5.62 × 10⁴ J, which is 19.93% less than the optimal path length. The traversal order solved by the improved DBO algorithm saves 9.2% more energy than the original algorithm, demonstrating a significant energy-saving effect. Full article

As the core device for protecting the safety of the pressure-bearing system, the spring full-open safety valve is prone to various forms of valve seat sealing surface damage after long-term opening and closing impact, corrosion, and medium erosion, which may lead to internal leakage. In view of the problems that the high-frequency acoustic emission signal of the internal leakage of the safety valve has, namely, a large number of energy-overlapping areas in the frequency domain, the overall signal presents broadband characteristics, large noise content, and no obvious time–frequency characteristics. A composite denoising method, IWTD, improved wavelet threshold function with dual adjustable factors, and the improved VMD algorithm is proposed. In view of the problem that the optimal values of the dual adjustment factors a and b of the function are difficult to determine manually, an improved dung beetle optimization algorithm is proposed, with the maximum Pearson coefficient as the optimization target; the optimization is performed within the value range of the dual adjustable factors a and b, so as to obtain the optimal value. In view of the problem that the key parameters K and α in VMD decomposition are difficult to determine manually, the maximum Pearson coefficient is taken as the optimization target, and the improved dung beetle algorithm is used to optimize within the value range of K and α, so as to obtain the IVMD algorithm. Based on the IVMD algorithm, the characteristic decomposition of the internal leakage acoustic emission signal occurs after the denoising of the IWTD function is performed to further improve the denoising effect. The results show that the Pearson coefficients of all types of internal leakage acoustic emission signals after IWTD-IVMD composite noise reduction are greater than 0.9, which is much higher than traditional noise reduction methods such as soft and hard threshold functions. Therefore, the IWTD-IVMD composite noise reduction method can extract more main features out of the measured spring full-open safety valve internal leakage acoustic emission signals, and has a good noise reduction effect. Feature recognition after noise reduction can provide a good evaluation for the safe operation of the safety valve. Full article

The safety associated with the bearing capacity of pile foundations is intrinsically linked to the overall safety, stability, and economic viability of structural systems. In response to the need for rapid and precise predictions of pile bearing capacity, this study introduces a kernel extreme learning machine (KELM) prediction model optimized through a multi-strategy improved beetle optimization algorithm (IDBO), referred to as the IDBO-KELM model. The model utilizes the pile length, pile diameter, average effective vertical stress, and undrained shear strength as input variables, with the bearing capacity serving as the output variable. Initially, experimental data on pile bearing capacity was gathered from the existing literature and subsequently normalized to facilitate effective integration into the model training process. A detailed introduction of the multi-strategy improved beetle optimization algorithm (IDBO) is provided, with its superior performance validated through 23 benchmark functions. Furthermore, the Wilcoxon rank sum test was employed to statistically assess the experimental outcomes, confirming the IDBO algorithm’s superiority over other prevalent metaheuristic algorithms. The IDBO algorithm was then utilized to optimize the hyperparameters of the KELM model for predicting pile bearing capacity. In conclusion, the statistical metrics for the IDBO-KELM model demonstrated a root mean square error (RMSE) of 4.7875, a coefficient of determination (R²) of 0.9313, and a mean absolute percentage error (MAPE) of 10.71%. In comparison, the baseline KELM model exhibited an RMSE of 6.7357, an R² of 0.8639, and an MAPE of 18.47%. This represents an improvement exceeding 35%. These findings suggest that the IDBO-KELM model surpasses the KELM model across all evaluation metrics, thereby confirming its superior accuracy in predicting pile bearing capacity. Full article

The vibration and noise of friction pairs are severe when cutting titanium alloy with cemented carbide tools, and the surface micro-texture can significantly reduce noise and friction. Therefore, it is very important to clarify the correlation mechanism between friction noise and friction force for processing quality control. Consequently, investigating the underlying mechanisms that link friction noise and friction is of considerable importance. This study focuses on the friction and wear acoustic signals generated by micro-textured cemented carbide–titanium alloy. A friction testing platform specifically designed for the micro-textured cemented carbide grinding of titanium alloy has been established. Acoustic sensors are employed to capture the acoustic signals, while ultra-depth-of-field microscopy and scanning electron microscopy are utilized for surface analysis. A novel approach utilizing the dung beetle algorithm (DBO) is proposed to optimize the parameters of variational mode decomposition (VMD), which is subsequently combined with wavelet packet threshold denoising (WPT) to enhance the quality of the original signal. Continuous wavelet transform (CWT) is applied for time–frequency analysis, facilitating a discussion on the underlying mechanisms of micro-texture. Additionally, features are extracted from the time domain, frequency domain, wavelet packet, and entropy. The Relief-F algorithm is employed to identify 19 significant features, leading to the development of a hybrid model that integrates Bayesian optimization (BO) and Transformer-LSTM for predicting friction. Experimental results indicate that the model achieves an R² value of 0.9835, a root mean square error (RMSE) of 0.2271, a mean absolute error (MAE) of 0.1880, and a mean bias error (MBE) of 0.1410 on the test dataset. The predictive performance and stability of this model are markedly superior to those of the BO-LSTM, LSTM–Attention, and CNN–LSTM–Attention models. This research presents a robust methodology for predicting friction in the context of friction and wear of cemented carbide–titanium alloys. Full article

During the service life of automotive panel stamping dies, the surface is often subjected to high loads and repeated friction, resulting in excessive wear. This leads to die failure, reduced machining accuracy, and decreased production efficiency. To enhance the anti-friction and wear-resistant performance of die steel surfaces, this study introduces the concept of biomimetic engineering in surface science. By mimicking microstructural configurations found in nature with outstanding wear resistance, biomimetic functional surfaces were designed and fabricated. Specifically, quadrilateral dimples inspired by the back of dung beetles, pentagonal scales from armadillo skin, and hexagonal scales from the belly of desert vipers were selected as biological prototypes. These surface textures were fabricated on Cr12MoV die steel using high-speed ball-end milling. Finite element simulations and dry sliding wear tests were conducted to systematically investigate the tribological behavior of surfaces with different dimple geometries. The results showed that the quadrilateral dimple surface derived from the dung beetle exhibited the best performance in reducing friction and wear. Furthermore, the milling parameters for this surface were optimized using response surface methodology. After optimization, the friction coefficient was reduced by 21.3%, and the wear volume decreased by 38.6% compared to a smooth surface. This study confirms the feasibility of fabricating biomimetic functional surfaces via high-speed ball-end milling and establishes an integrated surface engineering approach combining biomimetic design, efficient manufacturing, and parameter optimization. The results provide both theoretical and methodological support for improving the service life and surface performance of large automotive panel dies. Full article

Carbon emission estimation for power systems is essential for identifying emission responsibilities and formulating effective mitigation measures. Current carbon emission prediction methods for power systems exhibit limited computational efficiency and inadequate noise immunity under complex operating conditions. In this study, we address these limitations by improving population initialization, search mechanisms, and iteration strategies and developing a hybrid strategy Modified Dung Beetle Optimization (MDBO) algorithm. This led to the development of an MDBO-enhanced Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) network hybrid prediction model for carbon emission prediction. Firstly, the theoretical calculation mechanism of carbon emission flow in power systems is analyzed. Subsequently, an MDBO-CNN-LSTM deep network architecture is constructed, with detailed explanations of its fundamental structure and operational principles. Then, the proposed MDBO-CNN-LSTM model is utilized to predict the nodal carbon emission factor of power systems with the integration of renewable energy sources. Comparative experiments with conventional CNN-LSTM models are conducted on modified IEEE 30-, 118-, and 300-bus test systems. The results show that the maximum mean squared error of the proposed method does not exceed 0.5734% in the strong-noise scenario for the 300-bus system, which is reduced by half compared with the traditional method. The proposed method exhibits enhanced robustness under strong noise interference, providing a novel technical approach for precise carbon accounting in power systems. Full article

In this study, we propose a novel improved Dung Beetle Optimizer called Environment-aware Chaotic Force-field Dung Beetle Optimizer (ECFDBO). To address DBO’s existing tendency toward premature convergence and insufficient precision in high-dimensional, complex search spaces, ECFDBO integrates three key improvements: a chaotic perturbation-based nonlinear contraction strategy, an intelligent boundary-handling mechanism, and a dynamic attraction–repulsion force-field mutation. These improvements reinforce both the algorithm’s global exploration capability and its local exploitation accuracy. We conducted 30 independent runs of ECFDBO on the CEC2017 benchmark suite. Compared with seven classical and novel metaheuristic algorithms, ECFDBO achieved statistically significant improvements in multiple performance metrics. Moreover, by varying problem dimensionality, we demonstrated its robust global optimization capability for increasingly challenging tasks. We further conducted the Wilcoxon and Friedman tests to assess the significance of performance differences of the algorithms and to establish an overall ranking. Finally, ECFDBO was applied to a 3D path planning simulation in UAVs for safe path planning in complex environments. Against both the Dung Beetle Optimizer and a multi-strategy DBO (GODBO) algorithm, ECFDBO met the global optimality requirements for cooperative UAV planning and showed strong potential for high-dimensional global optimization applications. Full article

With the development of modern power systems, AC distribution networks face increasing demands for supply flexibility and reliability. Energy storage-based soft open points (E-SOPs), which integrate energy storage systems into the DC side of traditional SOP connecting AC distribution networks, not only maintain power flow control capabilities but also enhance system supply performance, providing a novel approach to AC distribution network fault recovery. To fully leverage the advantages of E-SOPs in handling faults in flexible interconnected AC distribution networks (FIDNs), this paper proposes an E-SOP-based FIDN islanding recovery method. First, the basic structure and control modes of SOPs for AC distribution networks are elaborated, and the E-SOP-based AC distribution network structure is analyzed. Second, with maximizing total load recovery as the objective function, the constraints of E-SOPs are comprehensively considered, and recovery priorities are established based on load importance classification. Then, a multi-dimensional improvement of the dung beetle optimizer (DBO) algorithm is implemented through Logistic chaotic mapping, adaptive parameter adjustment, elite learning mechanisms, and local search strategies, resulting in an efficient solution for AC distribution network power supply restoration. Finally, the proposed FIDN islanding partitioning and fault recovery methods are validated on a double-ended AC distribution network structure. Simulation results demonstrate that the improved DBO (IDBO) algorithm exhibits a superior optimization performance and the proposed method effectively enhances the load recovery capability of AC distribution networks, significantly improving the self-healing ability and operational reliability of AC distribution systems. Full article

During the dry machining of 6061 aluminum alloy, cemented carbide tools often suffer from severe wear and built-up edge (BUE) formation, which significantly shortens tool life. Inspired by the non-smooth surface structure of dung beetles, this study proposes an elliptical dimple–groove composite bionic micro-texture, applied to the rake face of cemented carbide tools to enhance their cutting performance. Four types of tools with different surface textures were designed: non-textured (NT), single-groove texture (PT), circular dimple–groove composite texture (AKGC), and elliptical dimple–groove composite texture (TYGC). The cutting performance of these tools was analyzed through three-dimensional finite element simulations using the Deform-3D (version 11.0, Scientific Forming Technologies Corporation, Columbus, OH, USA) software program. The results showed that, compared to the NT tool, the TYGC tool exhibited the best performance, with a reduction in the main cutting force of approximately 30%, decreased tool wear, and significantly improved chip-breaking behavior. Based on the simulation results, a response surface model was constructed to optimize key texture parameters, and the optimal texture configuration was obtained. In addition, a theoretical model was developed to reveal the mechanism by which the micro-texture reduces interfacial friction and temperature rises by shortening the effective contact length. To verify the accuracy of the simulation and theoretical analysis, cutting experiments were further conducted. The experimental results were consistent with the simulation trends, and the TYGC tool demonstrated superior performance in terms of cutting force reduction, smaller adhesion area, and more stable cutting behavior, validating both the simulation model and the proposed texture design. This study provides a theoretical foundation for the structural optimization of bionic micro-textured cutting tools and offers an in-depth exploration of their friction-reducing and wear-resistant mechanisms, showing promising potential for practical engineering applications. Full article

During the cantilever casting construction process of continuous girder bridges, it is crucial to accurately predict the pre-camber of each cantilever segment to ensure smooth closure of the bridge, structural safety, and construction quality. However, traditional methods for predicting pre-camber have limited accuracy and primarily handle linear relationships. Therefore, this paper proposes a pre-camber prediction model based on a Convolutional-Bidirectional Long Short-Term Memory network with a fusion attention mechanism (CNN-BiLSTM-Attention) and utilizes the Dung Beetle Optimizer (DBO) algorithm to optimize the hyperparameters of the CNN-BiLSTM-Attention model to enhance its predictive performance. The research results indicate that compared to several other prediction models, the model proposed in this paper demonstrates superior performance in predicting the pre-camber of continuous girder bridges. Compared to other prediction models, the evaluation metrics MAE, RMSE, and MAPE of the model proposed in this paper are minimized to 2.76 mm, 3.47 mm, and 0.70%, respectively. Applying the model proposed in this paper to the cantilever casting stage of the elevated continuous girder bridges in Shenyang Metro, China, enables pre-camber prediction with an accuracy of an average absolute error of less than 2 mm, providing a new efficient method for pre-camber prediction in cantilever casting construction. Full article

To improve the consumption rate of distributed energy and enhance the self-healing performance of distribution networks, this paper proposes a distribution network optimization method considering carbon emissions and dynamic reconfiguration. Firstly, various measures such as dynamic reconfiguration and distributed energy scheduling are used in upper-level optimization to reduce the network loss and solar curtailment cost of the system and to realize the optimal economic operation of the distribution network. Secondly, based on carbon emission flow theory in lower-level optimization, a low-carbon demand response model with a dynamic carbon emission factor as the guiding signal is established to promote carbon emission reduction on the user side. Then, the second-order cone planning and improved dung beetle optimization algorithm are used to solve the model. Finally, it is verified on the test system that the method can effectively reduce the risk of voltage overruns and enhance the low-carbonization and economy of distribution network operation. Full article

With the increasing global focus on sustainable development, circulating fluidized bed (CFB) boilers, as highly efficient and low-pollution combustion equipment, play an important role in energy production and environmental protection. However, the combustion efficiency and emission control of CFB boilers still face challenges, and there is an urgent need for multi-objective optimization through advanced technologies to support the goal of sustainable development. This study proposes an intelligent framework integrating Bidirectional Temporal Convolutional Network (BiTCN) and Hybrid Dung Beetle Optimizer (HDBO) for multi-objective optimization of combustion efficiency and NOx/SO₂ emissions in CFB boilers. The BiTCN model captures bidirectional temporal dependencies between dynamic parameters (e.g., air-coal ratio, bed temperature) and target variables through operational data analysis. Three key improvements are implemented in DBO: (1) Chaotic initialization via sequential pattern mining (SPM) enhances population diversity and spatial coverage; (2) The osprey optimization algorithm (OOA) hunting mechanism replaces the original rolling update strategy, improving global exploration; (3) t-Distribution perturbation is applied to foraging beetles in later iterations, leveraging its “sharp peak and thick tail” characteristics to dynamically balance exploitation and exploration. Experimental results demonstrate 0.5–1% combustion efficiency improvement and 15.1%/30% reductions in NOx/SO₂ emissions for a typical CFB boiler. Full article

In complex environments, three-dimensional path planning for agricultural UAVs involves the comprehensive consideration of multiple factors, including obstacle avoidance, path optimization, and computational efficiency, which significantly complicates the achievement of safe and efficient flight. As environmental complexity increases, the search space expands exponentially, thereby making the problem more challenging to solve and categorizing it as an NP-hard problem. To obtain an optimal or near-optimal path within this vast search space, it is essential to balance the path length, safety, and computational cost. This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. Lastly, a new hybrid adaptive weighting strategy is employed to improve the algorithm’s stability and reliability. To validate the effectiveness of HMSDBO, this study compares its performance with that of the Adaptive Chaotic Gray Wolf Optimization Algorithm (ACGWO), Primitive Dung Beetle Optimization Algorithm (DBO), Whale Optimization Algorithm (WOA), Crayfish Optimization Algorithm (COA), and Hyper-Heuristic Whale Optimization Algorithm (HHWOA) in complex agricultural UAV environments. Experimental results show that the path lengths calculated by HMSDBO are reduced by 21.3%, 7.88%, 19.95%, 8.09%, and 4.2%, respectively, compared to the aforementioned algorithms. This reduction significantly enhances both the optimization effectiveness and the smoothness of three-dimensional path planning for agricultural UAVs. Full article