Search Results (115)

In response to the need for precise blossom identification and optimization of key operational parameters in intelligent cherry spraying pollination, the SMD-YOLO (You Only Look Once with spatial and channel reconstruction convolution, multi-scale channel attention, and dual convolution modules) cherry blossom detection model is proposed, along with a pollination experiment platform for parameter optimization. The SMD-YOLO model, built upon YOLOv11, enhances feature extraction through the ScConvC3k2 (spatial and channel reconstruction convolution C3k2) module, incorporates the MSCA (multi-scale channel attention) attention mechanism, and employs the DualConv module for a lightweight design, ensuring both detection accuracy and operational efficiency. Tested on a self-constructed cherry blossom dataset, the model delivered a precision of 87.6%, a recall rate of 86.1%, and an mAP (mean average precision) reaching 93.1% with a compact size of 4765 KB, 2.28 × 10⁶ parameters, a computational cost of 5.8 G, and a detection speed of 75.76 FPS, demonstrating strong practicality and potential for embedded real-time detection in edge devices, such as cherry pollination robots. To further enhance pollination effectiveness, a dedicated pollination experiment bench was designed, and a second-order orthogonal rotational combination experiment method was employed to systematically optimize three key parameters: spraying distance, spraying time, and liquid flow rate. Experimental results indicate that the optimal pollination effect occurs when the spraying distance is 3.4 cm, spraying time is 1.9 s, and liquid flow rate is 339 mL/min, with a deposition amount of 0.18 g and a coverage rate of 97.25%. This study provides a high-precision image detection algorithm and operational parameter optimization basis for intelligent and precise cherry blossom pollination. Full article

The stability of the intermediate rock wall in the blasting construction of bifurcated small-spacing tunnels directly affects the construction safety of the tunnel structure. Clarifying the damage characteristics of the intermediate rock wall has significant engineering value for ensuring the safe and efficient construction of bifurcated tunnels. Based on the Tashan North Road Expressway Tunnel Project, this paper investigated the damage characteristics of the intermediate rock wall in bifurcated tunnels under different blasting construction schemes, using numerical simulation methods to account for the combined effects of in situ stress and blasting loads. The results were validated using comparisons with the measured damage depth of the surrounding rock in the ramp tunnels. The results indicate that the closer the location is to the starting point of the bifurcated tunnel, the thinner the intermediate rock wall and the more severe the damage to the surrounding rock. When the thickness of the intermediate rock wall exceeds 4.2 m, the damage zone does not penetrate through the wall. The damage to the intermediate rock wall exhibits an asymmetric “U”-shaped distribution, with greater damage on the side of the trailing tunnel at the section of the haunch and sidewall, while the opposite is true at the section of the springing. During each excavation step of the ramp and main-line tunnels, the damage to the intermediate rock wall is primarily induced by blasting loads. As construction progresses, the damage to the rock wall increases progressively under the combined effects of blasting loads and the excavation space effect. In the construction of bifurcated tunnels, the greater the distance between the headings of the leading and trailing tunnels is, the less damage will be inflicted on the intermediate rock wall. Constructing the tunnel with a larger cross-sectional area first will cause more damage to the intermediate rock wall. When the bench method is employed, an increase in the bench length leads to a reduction in the damage to the intermediate rock wall. The findings provide valuable insights for the selection of construction schemes and the protection of the intermediate rock wall when applying the bench method in the construction of bifurcated small-spacing tunnels. Full article

Background/Objectives: Prostate cancer (PCa) remains a major global health issue, associated with significant mortality and morbidity. Despite advances in diagnosis and treatment, predicting biochemical recurrence (BCR) after radical prostatectomy remains challenging, highlighting the need for reliable biomarkers to guide prognosis and therapy. The study aimed to evaluate the prognostic significance of the PTEN and ERG biomarkers in predicting BCR and tumor progression in PCa patients who underwent radical prostatectomy. Methods: This study consisted of a cohort of 91 patients with localized PCa who underwent radical prostatectomy between 2016 and 2022. From this cohort, 77 patients were selected for final analysis. Tissue microarrays (TMAs) were constructed from paraffin blocks, and immunohistochemical (IHC) staining for PTEN and ERG was performed using specific antibodies on the Ventana BenchMark ULTRA system (Roche Diagnostics, Indianapolis, IN, USA). Stained sections were evaluated and correlated with clinical and pathological data. Results: PTEN expression showed a significant negative correlation with BCR (r = −0.301, p = 0.014), indicating that reduced PTEN expression is associated with increased recurrence risk. PTEN was not significantly linked to PSA levels, tumor stage, or lymph node involvement. ERG expression correlated positively with advanced pathological tumor stage (r = 0.315, p = 0.005) but was not associated with BCR or other clinical parameters. Conclusions: PTEN appears to be a valuable prognostic marker for recurrence in PCa, while ERG may indicate tumor progression. These findings support the potential integration of PTEN and ERG into clinical practice to enhance risk stratification and personalized treatment, warranting further validation in larger patient cohorts. Full article

Caragana korshinskii Kom. (CKB), widely cultivated in Inner Mongolia, China, has potential for silage feed development due to its favorable nutritional characteristics, including a crude protein content of 14.2% and a neutral detergent fiber content below 55%. However, its vascular bundle fiber structure limits the efficiency of lactic acid conversion and negatively impacts silage quality, which can be improved through mechanical crushing. Currently, conventional crushing equipment generally suffers from uneven particle size distribution, high energy consumption, and low processing efficiency. In this study, a layered aggregate model was constructed using the discrete element method (DEM), and the Hertz–Mindlin with Bonding contact model was employed to characterize the heterogeneous mechanical properties between the epidermis and the core. Model accuracy was enhanced through reverse engineering and a multi-particle-size filling strategy. Key parameters were optimized via a Box–Behnken experimental design, with a core normal stiffness of 7.37 × 10¹¹ N·m⁻¹, a core shear stiffness of 9.46 × 10¹⁰ N·m⁻¹, a core shear stress of 2.52 × 10⁸ Pa, and a skin normal stiffness of 4.01 × 10⁹ N·m⁻¹. The simulated values for bending, tensile, and compressive failure forces had relative errors of less than 10% compared to experimental results. The results showed that rectangular hammers, due to their larger contact area and more uniform stress distribution, reduced the number of residual bonded contacts by 28.9% and 26.5% compared to stepped and blade-type hammers, respectively. Optimized rotational speed improved dynamic crushing efficiency by 41.3%. The material exhibited spatial heterogeneity, with the mass proportion in the tooth plate impact area reaching 43.91%, which was 23.01% higher than that in the primary hammer crushing area. The relative error between the simulation and bench test results for the crushing rate was 6.18%, and the spatial distribution consistency reached 93.6%, verifying the reliability of the DEM parameter calibration method. This study provides a theoretical basis for the structural optimization of crushing equipment, suppression of circulation layer effects, and the realization of low-energy, high-efficiency processing. Full article

Neural Architecture Search (NAS) has made significant advancements in autonomously constructing high-performance network architectures, capturing extensive attention. However, a key challenge of existing NAS approaches is the intensive performance evaluation, leading to significant time and computational resource consumption. In this paper, we propose an efficient Evolutionary Neural Architecture Search (ENAS) method to address this issue. Specifically, in order to accelerate the convergence speed of the algorithm and shorten the search time, thereby avoiding blind searching in the early stages of the algorithm, we drew on the principles of biometrics to redesign the interaction between individuals in the evolutionary algorithm. By making full use of the information carried by individuals, we promoted information exchange and optimization between individuals and their neighbors, thereby improving local search capabilities while maintaining global search capabilities. Furthermore, to accelerate the evaluation process and minimize computational resource consumption, a multi-metric training-free evaluator is introduced to assess network performance, bypassing the resource-intensive training phase, and the adopted multi-metric combination method further solves the ranking offset problem. To evaluate the performance of the proposed method, we conduct experiments on two widely adopted benchmarks, NAS-Bench-101 and NAS-Bench-201. Comparative analysis with state-of-the-art algorithms shows that our proposed method identifies network architectures with comparable or better performance while requiring significantly less time. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

Aiming at the difficult problem of predicting the running state of the rotor of a Control Rod Drive Mechanism (CRDM) in a floating nuclear reactor, this paper proposes a Remaining Useful Life (RUL) prediction method based on Variational Mode Decomposition and Bidirectional Long Short-Term Memory (VMD-BiLSTM). Firstly, a bench experiment of the CRDM is carried out to collect the full operational cycle (full-stroke) vibration signals of the CRDM. Secondly, the collected data are decomposed based on the VMD, and the typical vibration signals at different stages of the experiment are used to verify this method and comprehensively mine the degradation characteristics. At the same time, the time-frequency domain feature analysis is carried out on the original vibration data, and the changing trends of the extracted features are carefully analyzed. Five feature quantities closely related to the degradation trend of the rotor of the CRDM are screened out, and the corresponding health indicators are constructed in combination with the stroke. Finally, the life prediction of the rotor of the CRDM is realized through the BiLSTM method. Then, the comparison experiments with other methods are carried out, and the experimental results show that the method proposed in this paper has high accuracy and reliability and can effectively solve the RUL prediction problem of CRDM, which provides a strong support to ensure the safe and stable operation of floating nuclear reactors. Full article

The upright state of pot seedlings in the process of rice mechanized throwing operations has an important influence on the growth rate and yield of rice, and pot seedling uprightness is affected by the influence of soil backfilling during trenching. Due to the complexity of the furrow opener–soil–pot seedling interaction mechanism in the rice pot seedling planting process, the soil backfilling process is difficult to observe. In order to improve the uprightness of pot seedling planting, this paper constructs a soil model and a soil–pot seedling model step by step, based on the discrete element method (DEM), as well as a coupled model of the pot seedling planting system to study the process of furrow opener–soil–pot seedling planting, the reliability of which is then verified. The results showed that the simulation results of the constructed soil model and soil–pot seedling model deviated from the actual calibration results by <6%, and the model could accurately simulate the pot seedling throwing process. The simulation analysis of the trenching process revealed that the soil backfilling process during trenching showed a three-stage evolution pattern of “backfilling-covering-stabilizing”; in addition, the forward speed of the machine was 0.8 m/s, and the falling speed of the seedling discharge cylinder was 3.5 m/s, which made it possible for the model to simulate the pot seedling throwing process accurately. In addition, when the pot seedling with a forward speed of 0.8 m/s and a drop speed of 3.5 m/s fell into the trench after 0.15 s of trenching, its lateral and longitudinal uprightness were 67.0 ± 1.2° and 65.2 ± 1.5°, respectively. After optimization of the structure of the trenchers, the width, depth, and length of the main body were 40 mm, 37.87 mm, and 32.32 mm, respectively, and the lateral and longitudinal uprightness of the pot seedlings increased to 70.0 ± 1.0° and 69.4 ± 0.8, respectively. The coupled model bench validation test showed that its reliability error was <5%. The coupled model provides technical support for the design and parameter optimization of rice planting equipment. Full article

The electric drive is strategically placed in the power industry. It is exposed to wear and tear, defects, and constructional damage, as is any technical device. An information–analytical system is presented in this work. It performs the tasks of monitoring, diagnostics, general assessment of technical condition, and continuous assessment of energy and mechanical efficiency of the electric drive based on the analysis of immediate values of currents and voltages. The system modules are finished products with practical application, which are supported by experimental validation. This article contains a detailed description of the methods implemented in the system development, as well as a description of the laboratory bench and equipment used in our experiments. The information–analytical system is shown and proved on the basis of a fault reconstruction example with electric drive misalignment. According to the obtained results, recommendations for preventive control and proposals for development in this direction are formulated. Full article

Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industries, have been used for a long time in complex and harsh environments such as boiler plants and gas turbines. Therefore, the vibration signals they generate show complex and diverse characteristics, which brings great challenges to the monitoring of centrifugal fan operation status. To solve this problem, this paper proposes a centrifugal fan blade fault diagnosis method based on a modulational depthwise convolution (DWconv)–one-dimensional convolution neural network (MDC-1DCNN). Specifically, firstly, a convolutional modulation module (CMM) with strong local perception and global modeling capability is designed by drawing on the Transformer self-attention mechanism and global context modeling idea. Second, multiple DWconv layers of different sizes are introduced to capture high-frequency shocks and low-frequency fluctuation information of different frequencies and durations in the signal. Next, a DWconv layer of size 11 is embedded in the multilayer perceptron to enhance spatial information representation while saving computational resources. Finally, to verify the effectiveness of the method, this paper simulates and analyzes the actual working state of centrifugal fan blades, constructs a simulation dataset, and builds a centrifugal fan experimental bench to obtain a real dataset. The experimental results show that the MDC-1DCNN framework significantly outperforms the existing methods in both simulation and experimental bench datasets, fully proving its versatility and effectiveness in centrifugal fan blade fault diagnosis. Full article

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, the ultrasonic sensor beamwidth and detection height were determined through calibration tests. Next, a test bench was used to explore the influence of the number of ultrasonic sensors and conveying speed on the detected grape pile height. Data-based regression and hopper configuration-based geometric models correlating grape load volume with detected pile height were subsequently constructed; their accuracies were compared using test bench experiments to identify the optimal detection scheme. The regression model was more accurate than the geometric model under the considered conveying speeds with a maximum relative error of 8.0% for the former. Finally, field tests determined that the average grape load volume detection error during actual harvesting was 14.4%. Therefore, this study provides an effective solution for the detection of grape load volume in the hopper during mechanized harvesting and establishes a theoretical basis for the development of intelligent grape harvesting methods. Full article

A fault diagnosis method for reciprocating compressor valves suitable for variable operating conditions is presented in this paper. Firstly, a test bench is independently constructed to simulate fault scenarios under diverse operating conditions and with various faults. The two types of p-V diagrams are gathered, and the improved logarithmic p-V diagram acquisition method is used for logarithmic transformation to obtain the multi-conditional logarithmic p-V diagram dataset and the fault logarithmic p-V diagram dataset. Subsequently, to predict the fault-free logarithmic p-V diagram under different operating conditions, a BP neural network is trained with the multi-condition logarithmic p-V diagram dataset. Next, the fault sequence is derived by subtracting the fault logarithmic p-V diagram from the fault-free logarithmic p-V diagram acquired under the same operating condition. Ultimately, the feature extraction of the fault sequence and the fault classification are accomplished by the employment of a triplet Siamese neural network (SNN). The results indicate that the fault classification accuracy of the method presented in this paper can attain 100%, which confirms that differential processing on the logarithmic p-V diagram is effective for fault feature preprocessing. This study not only improves the accuracy and efficiency of valve fault diagnosis in reciprocating compressors but also provides technical support for maintenance and fault prevention. Full article

To reduce harvest losses of a pepper harvester with a drum of elastic tooth type picking mechanism, this paper proposes an optimization method using AHP (Analytic Hierarchy Process) and RSM (Response Surface Methodology), thereby identifying the optimal harvesting parameters. Based on Hertz’s contact theory and projectile motion theory, dynamic and kinematic models were established for the picking and casting stage. Key parameters influencing harvest loss were identified as drum rotational speed, operating speed, and tooth spacing. A simulation model was constructed, and solved within LS-DYNA of ANSYS Workbench. A Box–Behnken design in RSM was employed to investigate the effects of drum rotational speed, operating speed, and tooth spacing on the picking rate, breakage rate, and loss rate. The optimal parameters, obtained through RSM optimization after AHP weighting, were determined to be a drum rotational speed of 182 r/min, an operating speed of 0.42 m/s, and a tooth spacing of 40 mm. A test bench was designed for validation, with simulation results deviating from experimental results by less than 5%. With optimized parameters, the picking rate increases from 89.73% to 95.13%, the breakage rate decreases from 3.21% to 2.66%, and the loss rate decreases from 5.16% to 3.95%. This study provides a theoretical foundation and practical reference for optimizing the drum with elastic tooth type picking mechanism in pepper harvesters. Full article

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the equipment, but also effectively reduce energy consumption and emissions. Particle swarm optimization (PSO) fuzzy PID control algorithms have been widely used in many complex engineering problems due to their powerful global optimization capability and excellent adaptability. Currently, PSO-based fuzzy PID control research mainly integrates hybrid algorithmic strategies to avoid the local optimum problem, and lacks optimization of the dynamic noise suppression of the input error and the rate of change of the error. This makes the algorithm susceptible to the coupling of the system uncertainty and measurement disturbances during the parameter optimization process, leading to performance degradation. For this reason, this study proposes a new framework based on the synergistic optimization of the untraceable Kalman filter (UKF) and PSO fuzzy PID control for the speed control system of a single-cylinder diesel engine. A PSO-optimized fuzzy PID controller is designed by obtaining accurate speed estimation data using the UKF. The PSO is capable of quickly adjusting the fuzzy PID parameters so as to effectively alleviate the nonlinearity and uncertainty problems during the operation of diesel engines. By establishing a Matlab/Simulink simulation model, the diesel engine speed step response experiments (i.e., startup experiments) and load mutation experiments were carried out, and the measurement noise and process noise were imposed. The simulation results show that the optimized diesel engine speed control system is able to reduce the overshoot by 76%, shorten the regulation time by 58%, and improve the noise reduction by 25% compared with the conventional PID control. Compared with the PSO fuzzy PID control algorithm without UKF noise reduction, the optimized scheme reduces the overshoot by 20%, shortens the regulation time by 48%, and improves the noise reduction effect by 23%. The results show that the PSO fuzzy PID control method with integrated UKF has superior control performance in terms of system stability and accuracy. The algorithm significantly improves the responsiveness and stability of diesel engine speed, achieves better control effect in the optimization of diesel engine speed control, and provides a useful reference for the optimization of other diesel engine control systems. In addition, this study establishes the GT-POWER model of a 168 F single-cylinder diesel engine, and compares the cylinder pressure and fuel consumption under four operating conditions through bench tests to ensure the physical reasonableness of the kinetic input parameters and avoid algorithmic optimization on the distorted front-end model. Full article

The shape of particles is a critical determinant that significantly influences the accuracy of discrete element simulations. To reduce the discrepancies between the discrete element model of wheat seeds and the actual particle shapes, and to enhance the accuracy of Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) coupling simulations in gas–solid two-phase flow studies, We employed laser scanning and inverse modeling techniques to develop a three-dimensional (3D) reconstruction of the wheat seed. Subsequently, we employed Rocky DEM simulation software to develop a polyhedron model and an Angle of Repose (AOR) test model. The interval range of material parameters was determined through a series of physical experiments and subsequently employed to delineate the high and low levels of parameters for the simulation tests. The simulation parameters were calibrated using data from AOR simulation tests. The Plackett–Burman test, Steepest-Ascent test, and Box–Behnken test were conducted sequentially to determine the optimal parameter configuration. A test bench for wheat gas-assisted seeding was constructed, and a semi-resolved CFD-DEM coupling simulation model was developed to perform comparative analysis. The results demonstrated that the optimal parameters were as follows: the static friction coefficient of wheat seed was 0.15, the dynamic friction coefficient of wheat seed was 0.11694, and the dynamic friction coefficient between wheat seed and resin was 0.0797. In this scenario, the relative error of AOR was 2.3% and the maximum relative error of ejection velocity observed was 4.1%. The reliability of the polyhedron model and its calibration parameters was rigorously validated, thereby providing a robust reference for studies on gas–solid two-phase flows. Full article