A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

The accuracy and speed of tomato detection were increased to facilitate fully automated harvesting by improving the Real-Time Detection Transformer (RTDETR) to develop a new lightweight tomato detection model called LiteTom-RTDETR. This model employed RepViT as a lightweight backbone network instead of the original RTDETR backbone, considerably reducing both the number of parameters in and computational complexity of the model. Furthermore, a context guide fusion module was designed to enhance multiscale feature extraction efficiency, and an adaptive sliding weight mechanism was integrated into the loss function to mitigate class-imbalance issues. The proposed LiteTom-RTDETR model was shown to balance high tomato identification accuracy (88.2%) with an excellent real-time inference speed (52.2 frames per second) and computational efficiency (36.3 GFLOPs). Notably, the average detection accuracy of LiteTom-RTDETR was 0.6% higher, its detection speed was 15.5% faster, and its computational load and model size were 36.2% and 31.6% smaller, respectively, than those of the original RTDETR model. Therefore, the proposed model provides a practical approach for realizing visual recognition tasks in resource-constrained mobile automated tomato harvesting equipment.

Hybrid thermoelectric generators (HTEGs) play a pivotal role in sustainable energy conversion by harnessing waste heat through the Seebeck effect, contributing to global efforts in energy efficiency and environmental sustainability. In practical sustainable energy systems, HTEG output performance is significantly influenced by uncertainties in the operational parameters (such as temperature differences and load resistance), material properties (including Seebeck coefficient and resistance), and structural configurations (like the number of series/parallel thermoelectric components), which impact both efficiency and system stability. This study employs the Sobol-sequence-sampling method to characterize these parameter uncertainties, analyzing their effects on HTEG output power and conversion efficiency using mean values and standard deviations as evaluation metrics. The results show that higher temperature differences enhance output performance but reduce stability, a larger load resistance decreases performance while improving stability, thermoelectric materials with high Seebeck coefficients and low resistance boost efficiency at the expense of stability, increasing series-connected components elevates performance but reduces stability, parallel configurations enhance power output yet decrease efficiency and stability, and greater contact thermal resistances diminish performance while enhancing system robustness. This research provides theoretical guidance for optimizing HTEGs in sustainable energy applications, enabling the development of more reliable, efficient, and eco-friendly thermoelectric systems that balance performance with environmental resilience for long-term sustainable operation.

Fatigue life testing is a crucial method for evaluating the fatigue performance of a material or part. However, the reliability and accuracy of the analysis methods applied to the data generated by such tests remain insufficient owing to their considerable scatter. This study accordingly conducted fatigue testing on fourth-generation 2060-T8 aluminum–lithium alloy specimens before and after low- and high-intensity cast steel shot peening. The resulting fatigue test data were evaluated for normality, processed using outlier identification, and subjected to hypothesis testing and comparative analysis. Finally, the fatigue reliability of each specimen group was calculated based on the significant differences between their observed fatigue lives. This study pioneers the systematic integration of normality testing, outlier identification, and hypothesis testing, constructing a multi-tiered analytical framework specifically tailored for shot peening effect evaluation. This approach fundamentally overcomes the constraints of traditional methodologies characterized by their sole reliance on mean fatigue life analysis. The determined effects of shot peening on the specimen fatigue life and data dispersion indicated that analysis using statistical methods can effectively improve the reliability of fatigue test results and support the selection of materials for use in engineering structures.

The functionality of unmanned aerial vehicles (UAVs) in agricultural applications was improved by optimizing the parameters of the vector bracket in a vertical takeoff and landing UAV to maximize thrust and lift-to-drag ratio. First, the results of computational fluid dynamics simulations were compared with wind tunnel data to ensure an accurate model of the considered UAV, indicating a thrust coefficient error of less than 3% and a UAV lift-to-drag ratio error of less than 8%. Next, this model was applied to simulate the propeller thrust and UAV lift-to-drag ratio for 25 sample points selected using a central composite experimental design by varying the four structural parameters of the vector bracket. A kriging algorithm was subsequently applied to construct response surface models based on the results. Finally, a Multi-Objective Genetic Algorithm was employed to determine the optimal parameter values maximizing the two coefficients. The optimal structural parameters for the UAV vector bracket were determined to comprise a vector bracket height of 51 mm, fixed bracket length of 168 mm, fixed bracket width of 69 mm, and ball socket outer diameter of 31 mm. These values provided a 19% larger propeller thrust coefficient than those of the original UAV.

Vehicles exhibit complex failure modes and mechanisms because of their extreme service environments and severe external loads. The increasing level of integration in these vehicles is also driving more stringent reliability requirements, but conventional methods for reliability analysis require significant calculations, necessitating the use of surrogate models. At present, in the field of the reliability analysis of vehicle dust extraction impellers, although there are various research methods, the research on using surrogate models for relevant analysis is still not perfect. In particular, there are few studies specifically focused on dust extraction impellers. This study established a three-dimensional finite element parametric model of one such fan to simulate the impeller blade stress output for 500 parameter sets. The feedback neural network, backpropagation neural network, and quadratic polynomial response surface were subsequently used as surrogate models to learn the relationship between the parameters and output responses in these data. Comparisons of the results indicated that the feedback neural network exhibited the highest accuracy when predicting the stress responses of the dust extraction fan impeller to changes in parameter values. Through a comparative analysis of multiple surrogate models, this study determined the advantages of the feedback neural network in predicting the impeller stress response. It provides a more efficient and accurate method for reliability analysis in this field and helps to promote the development of reliability research on vehicle filtration systems.