Search Results (6,182)

To address the problems of high unharvested rates and header loss rates in existing plot-breeding wheat harvesters, this study presents the design of a comb-type header for plot wheat harvesters. Based on the loss suppression mechanism during wheat harvesting, the key components of the comb-type header were designed. To address the issue in which some wheat ears escape combing during the harvesting process, a multi-stage comb-tooth structure was developed. For the problem of seed retention on the bottom plate of the screw conveyor, the telescopic tooth at the feeding port of the screw conveyor was replaced with a scraper, and a rubber plate was added. To determine the optimal combing time, wheat plant posture changes under the action of the nose (hereinafter referred to as the nose) were analyzed through theoretical analysis, simulation, and bench testing. It was determined that the optimal combing moment occurs when the plants begin to rebound to the maximum reverse bending. On this basis, a numerical simulation model of the header combing system was constructed using the discrete element method, with the header loss rate as the evaluation index to explore the influence of the nose height, the machine forward speed, and the combing drum rotation speed on the header performance. A regression model of header loss was constructed using the Box–Behnken response surface method, and the optimal working parameters were determined as follows: a nose height of 554 mm, a machine forward speed of 0.65 m/s, a combing drum rotation speed of 667 r/min, and the predicted loss rate of 8.59%. To verify the operational performance of the comb-type header, a field test of the wheat-harvesting prototype was conducted. The results showed that, under the optimal working parameters, the header loss rate was 7.24%, no wheat ears escaped combing, and no seed retention occurred in the header, which meets the requirements for plot wheat-breeding harvesting. This study provides a theoretical basis for the design and development of small-sized combing harvesters. Full article

Constructing ecological networks (ENs) is an effective measure to mitigate the conflict between urban development and ecological conservation. However, existing simulating methods lack adequate consideration of human ecological demands and the spatial scale differences between these demands and natural ecological processes. This might lead to issues such as incomplete ecological process cycles or structural mismatches being overlooked during ENs simulations. To address these gaps, this study proposed an urban multi-scale nested ENs simulating framework that integrates human ecological demands with natural ecological processes. The framework first simulated an ENs focused on natural ecological process cycles at a global scale (GS). Then, it simulated an ENs centered on human ecological needs within the core urban areas at local scale (LS). Finally, it nested these multi-scale ENs by using cross-scale ecological supply sources as connecting points. This framework was applied to simulate spatio-temporal pattern changes in ENs of Jinan City, a core city in downstream of the Yellow River in China, aiming to mitigate cross-scale ecological conflicts between human–nature interactions under the background of urbanization. The study’s findings revealed that the area of demand sources increased by 8.56 times over 20 years. the area of cross-scale supply sources decreased by 15 km² relative to 2000, and the deterioration in connectivity was more pronounced in GS compared to LS, with a decline of approximately 13.8%. These changes indicate the presence of incomplete ecological process cycles and structural mismatches across the multi-scale boundaries within the study area, which have been worsening annually. We recommend optimizing Jinan City’s multi-scale ecological network through three key strategies: rectifying internal structural mismatches, protecting core ecological areas, and aligning regional ecological demands. Implementing these strategies could significantly improve the network structure, reduce cross-scale mismatches, and enhance ecological connectivity by about 9%. This study highlights the importance of addressing structural mismatches and promoting complete ecological cycles in urban multi-scale ENs simulating, providing valuable insights for formulating urban multi-scale ecological conservation and restoration policies. Full article

Multi-access edge computing (MEC) has been widely recognized as a promising solution for alleviating the computational burden on edge devices, particularly in supporting fast and real-time processing of resource-intensive applications. In this paper, we propose a decentralized offloading decision strategy based on multi-agent deep reinforcement learning (MADRL), aiming to minimize the overall task completion latency experienced by edge devices. Our proposed scheme adopts a grant-free access mechanism during the initialization of offloading in a fully decentralized manner, which serves as the key feature of our strategy. As a result, determining the optimal offloading factor becomes significantly more challenging due to the simultaneous access attempts from multiple edge devices. To resolve this problem, we consider a discrete action space-based deep reinforcement learning (DRL) approach, termed deep Q network (DQN), to enable each edge device to learn a decentralized computation offloading policy based solely on its local observation without requiring global network information. In our design, each edge device dynamically adjusts its offloading factor according to its observed channel state and the number of active users, thereby balancing local and remote computation loads adaptively. Furthermore, the proposed MADRL-based framework jointly accounts for user association and offloading decision optimization to mitigate access collisions and computation bottlenecks in a multi-user environment. We perform extensive computer simulations using MATLAB R2023b to evaluate the performance of the proposed strategy, focusing on the task completion latency under various system configurations. The numerical results demonstrate that our proposed strategy effectively reduces the overall task completion latency and achieves faster convergence of learning performance compared with conventional schemes, confirming the efficiency and scalability of the proposed decentralized approach. Full article

In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article

Connected and autonomous vehicle (CAV) platoons face the dual challenge of maintaining longitudinal formation stability while ensuring lateral safety in dynamic traffic environments, yet existing control approaches often address these objectives in isolation. This paper proposes a hierarchical cooperative control framework that integrates a differential game-based longitudinal controller with a risk potential field-driven model predictive controller (MPC) for lateral motion. At the coordination control layer, a differential game formulation models inter-vehicle interactions, with analytical solutions derived for both open-loop Nash equilibrium under predecessor-following (PF) topology and an estimated Nash equilibrium under two-predecessor-following (TPF) topology. The motion control layer employs a risk potential field model that quantifies collision threats from surrounding obstacles and road boundaries, guiding the MPC to perform real-time trajectory optimization. A comprehensive co-simulation platform integrating MATLAB/Simulink, Prescan, and CarSim validates the proposed framework across three representative scenarios: ramp merging with aggressive cut-in maneuvers, emergency braking by a preceding obstacle vehicle, and multi-lane cooperative obstacle avoidance involving multiple dynamic obstacles. Across all scenarios, the CAV platoon achieves safe obstacle avoidance through autonomous decision-making, with spacing errors converging to zero and smooth velocity adjustments that ensure both formation stability and ride comfort. The results demonstrate that the proposed framework effectively adapts to diverse and complex traffic conditions. Full article

In response to the deterioration of path-tracking accuracy and driving stability encountered by intelligent vehicles under dynamically varying operating conditions, a multi-objective optimization strategy integrating soft actor-critic (SAC) reinforcement learning with variable-parameter Model Predictive Control (MPC) is proposed in this paper to achieve online adaptive adjustment of path-tracking controller parameters. Based on a three-degree-of-freedom vehicle dynamics model, a linear time-varying (LTV) MPC controller is constructed to jointly optimize the front wheel steering angle. An SAC agent is developed utilizing the actor-critic framework, with a comprehensive reward function designed around tracking accuracy and control smoothness to enable online tuning of the MPC weighting matrices (lateral error weight, heading error weight, and steering control weight) as well as the prediction horizon parameter, thereby realizing adaptive balance between tracking accuracy and stability under different operating conditions. Based on the simulation results, it can be concluded that under normal operating conditions, the proposed integrated SAC-MPC control scheme demonstrates superior tracking performance, with the maximum absolute lateral error and mean lateral error reduced by 44.9% and 67.2%, respectively, and the maximum absolute heading error reduced by 23.5%. When the system operates under nonlinear conditions during the transitional phase, the proposed control scheme not only enhances tracking accuracy—evidenced by reductions of 43.4% and 23.8% in the maximum absolute lateral error and maximum absolute heading error, respectively—but also significantly improves system stability, as indicated by a 20.7% reduction in the sideslip angle at the center of gravity. Experimental validation further confirms these findings. The experimental results reveal that, compared with the fixed-parameter MPC, the maximum absolute value and mean value of the lateral error are reduced by approximately 36.2% and 78.1%, respectively; the maximum absolute heading angle error is decreased by 24.3%; the maximum absolute yaw rate is diminished by 19.6%; and the maximum absolute sideslip angle at the center of gravity is reduced by 30.8%. Full article

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable of operating under uncertain environmental and load conditions. This study proposes a Physics-Informed Neural Network (PINN)-based charge allocation framework that explicitly embeds physical constraints—namely charge conservation and State-of-Charge (SoC) equalization—directly into the learning process, enabling real-time adaptive control under varying irradiance and load conditions. The proposed controller exploits real-time measurements of PV voltage, current, and irradiance to achieve optimal charge distribution while ensuring converter stability and balanced battery operation. The framework is implemented and validated in MATLAB/Simulink under Standard Test Conditions of 1000 W·m⁻² irradiance and 25 °C ambient temperature. Simulation results demonstrate stable PV voltage regulation within the 230–250 V range, an average PV power output of approximately 95 kW, and effective duty-cycle control within the range of 0.35–0.45. The system maintains balanced three-phase grid voltages and currents with stable sinusoidal waveforms, indicating high power quality during steady-state operation. Compared with conventional Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC) methods, the PINN-based approach achieves faster SoC equalization, reduced transient fluctuations, and more than 6% improvement in overall system efficiency. These results confirm the strong potential of physics-informed intelligent control as a scalable and reliable solution for smart PV–battery energy systems, with direct relevance to renewable microgrids and electric vehicle charging infrastructures. Full article

Rolling bearings are critical components in industrial machinery, and their failures can lead to equipment downtime or safety hazards, making accurate fault diagnosis vital. While data-driven intelligent methods perform well with sufficient labeled data, acquiring large-scale fault data in real-world scenarios remains challenging. To address this issue, this paper proposes a fault diagnosis method combining finite element simulation and deep domain adaptation transfer learning. First, a finite element model of rolling bearings under normal, outer race, inner race, and rolling element fault conditions is developed, and ANSYS/LS-DYNA simulates motion to generate labeled synthetic fault data. The model’s reliability is validated through time-domain, frequency-domain, and time-frequency analyses. A lightweight 1D convolutional neural network (1D CNN) is then designed for fault diagnosis. When trained solely on simulated data, the model achieves only 61.4% accuracy on real data due to domain discrepancies. To bridge this gap, a transfer learning approach integrating generative adversarial networks (GANs) and multi-kernel maximum mean discrepancy (MK-MMD) is proposed: GANs synthesize data resembling real distributions, while MK-MMD minimizes domain shifts between simulated and actual data. This improves the model’s accuracy to 93.8% on real fault datasets. Performance evaluation under variable working conditions and bearing types demonstrates the method’s robustness, providing a practical solution for fault diagnosis in industrial applications with limited data. Full article

During the discharge process of a salt cavern compressed air energy storage (CAES) system, high-speed air flow may entrain salt slag from the cavern floor, posing a threat to pipeline safety. Currently, there is a lack of in-depth research into the transient mechanisms of the entrainment process, particularly the influence of particle shape. This study employs a CFD-DEM coupling approach to conduct, for the first time, a high-fidelity simulation of slag entrainment dynamics during the initial discharge phase of a salt cavern CAES system, with a focus on the motion patterns of three particle shapes: spherical, conical, and square. Results show that: (1) during the initial discharge stage, the flow field rapidly forms vortex structures that migrate toward the wellhead, which is the core mechanism driving particle mobilization; (2) particle shape significantly affects entrainment efficiency through frictional characteristics—spherical particles are most easily entrained (maximum entrainment rate of 0.42 kg/h), while non-spherical particles tend to accumulate below the wellhead; and (3) the entrainment process exhibits strong transient characteristics: the entrainment rate peaks rapidly (approximately 0.82 kg/h) within a short time and then declines sharply, and it is sensitive to particle size, with the most entrainable particle size being around 5 mm. This study reveals the coupling mechanism between transient vortices and multi-shape particle entrainment during discharge, providing a theoretical basis for the design of filtration systems, operational risk prevention, and slag removal strategies in salt cavern CAES power plants. Full article

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision fault diagnosis. A fixed-threshold wavelet denoising method with the scene-specific pre-optimized parameter a (0 < a ≤ 1.3) is proposed: the parameter a is determined via offline grid search using the feature retention rate (FRR) as the objective function for typical wind farm operating scenarios. A multi-scale depthwise separable CNN (MS-DSCNN) captures multi-scale spatial features via 3 × 1 and 5 × 1 kernels, reducing computational complexity by 73.4% versus standard CNNs. An attention-based minimal peephole LSTM (AttMPLSTM) enhances temporal feature measurement, using minimal peephole connections for long-term dependencies and channel attention to weight fault-relevant signals. Joint L1–L2 regularization mitigates overfitting and environmental interference, improving model robustness. Validated on a WT test bench, the Adams simulation dataset, and the CWRU benchmark, the model achieves a 90.2 ± 1.4% feature retention rate (FRR) in signal processing, an over 98% F1-score for fault classification, and over 99% accuracy. With 2.5 s single-epoch training and a 12.8 ± 0.5 ms single-sample inference time, the reduced parameters enable real-time deployment in embedded systems, advancing signal processing for rotating machinery fault diagnosis. Full article

This study addresses the challenges of real-time performance, safety, and trajectory smoothness in robot navigation by proposing an innovative variable-horizon model predictive control (MPC) scheme that utilizes evolutionary algorithms. To effectively adapt to the complex and dynamic conditions during navigation, a constrained multi-objective evolutionary algorithm is used to tune the control parameters precisely. The optimized parameters are then used to dynamically adjust the MPC’s prediction horizon online. To further enhance the system’s real-time performance, warm start and multiple shooting techniques are introduced, significantly improving the computational efficiency of the MPC. Finally, simulation and real-world experiments are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed control scheme exhibits excellent navigation performance in differential-drive robot models, offering a novel solution for intelligent mobile robot navigation. Full article

Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not been evaluated over long periods using real observational data, but mainly through simulations. This study addresses this gap by introducing a new methodology based on X-band polarimetric radar and by validating it against real precipitation events over an extended time period. The machine learning model is trained and tested using a four-year dataset including X-band radar, Micro Rain Radar, disdrometer, and temperature profile data from the Grenoble region (French Alps). To improve the classification accuracy, three temperature profile sources were tested: lapse rates obtained from automatic weather stations, interpolation of the temperature profile from the freezing level detected by the Micro Rain Radar, and temperature profiles from the operational AROME model forecast. Three different phase classification schemes were tested: two existing schemes based on fuzzy-logic, and the new method based on random forest. Results show that the random forest method, trained with radar polarimetric variables, AROME temperature profiles, and target labels derived from Micro Rain Radar observations, achieves the highest accuracy. Despite the overall good classification results, limitations persist in identifying mixed-phase precipitation due to its transitional nature and vertical variability. Feature importance analysis indicates that temperature is the most influential variable in the classification scheme, followed by reflectivity factor measured in the horizontal plane (Z_e) and differential reflectivity (Z_dr). This methodology demonstrates the potential of combining machine learning techniques with multi-instrument observations to improve hydrometeor classification in complex terrain. The approach offers valuable insights for operational forecasting, water resource management, and climate impact assessments in mountainous regions. Full article

This study provides a detailed technical and sustainability comparison of the conventional CNC machining and additive manufacturing routes for an aerospace bearing bracket. The work integrates material selection, process parameterization, build simulation, and environmental–economic assessment within a single framework. For the CNC route, machining of Al 7175-T7351 is characterized through process sequencing, tooling requirements, and waste generation. For the Laser Powder Bed Fusion (LPBF) route, two build strategies, single-part distortion-minimized and multi-part volume-optimized, are developed using Siemens NX for orientation optimization and Atlas3D for thermal and recoater collision simulations. The mechanical properties of Al 7175-T7351 and Scalmalloy^® are compared to justify material selection for aerospace applications. Both the experimental and simulation-derived process metrics are reported, including the build time, support mass, energy consumption, distortion tolerances, and buy-to-fly (B2F) ratio. CNC machining exhibited a B2F ratio of 1:7, with cradle-to-gate CO₂ emissions of ~11,000 g and an energy consumption exceeding 100 kWh per component. In contrast, both LPBF strategies achieved a B2F ratio of 1:1.2, reducing CO₂ emissions by over 90% and energy consumption by up to 63%. Build volume optimization further reduced the LPBF unit cost by over 50% relative to the CNC machining. Use-phase analysis in an aviation context indicated estimated lifetime fuel savings of 776,640 L and the avoidance of 2328 tons of CO₂ emissions. The study demonstrates how simulation-guided build preparation enables informed sustainability-driven decision-making for manufacturing route selection in aerospace applications. Full article

Mission-oriented supply chains involve multi-phase tasks, strong resource interdependencies, and stringent reliability requirements, which make demand planning complex and uncertain. This study develops a structured demand modeling framework to support multi-phase mission-oriented supply chains under budget and reliability constraints by integrating digital twin technology with an adaptive inertia weight particle swarm optimization (AIW-PSO) algorithm. The supply support process is decomposed into four sequential phases—storage, transportation, preparation, and execution—and phase-specific demand models are constructed based on system reliability theory, explicitly incorporating redundancy, maintainability, and repairability. In this work, digital twin technology functions as a data acquisition and virtual experimentation layer that supports parameter calibration, state-aware scenario simulation, and event-triggered re-optimization rather than continuous real-time control. Physical-state updates are mapped to model parameters such as phase durations, failure rates, repair rates, and instantaneous availability, after which the integrated optimization model is re-solved using a warm-start strategy to generate updated demand plans. The resulting multi-phase demand optimization problem is solved using AIW-PSO to enhance global search performance and mitigate premature convergence. The proposed method is validated using a representative mission-oriented supply support scenario with operational and simulated data. Simulation results demonstrate that, under identical budget constraints, the proposed approach achieves higher mission completion capability than conventional PSO-based methods, providing effective and practical decision support for multi-phase mission-oriented supply chain planning. Full article

Urban metro systems are highly sensitive to seismic disturbances, and the ability of metro stations to manage emergencies effectively has become an increasingly important component of urban resilience. This study develops a resilience-oriented evaluation framework that conceptualizes emergency management as a sequential managerial process encompassing preparedness, response, and recovery. A multi-dimensional indicator system was constructed based on the four resilience capacities—absorptive, maintaining, recovery, and adaptive—and operationalized through a multi-stage Data Envelopment Analysis (DEA) model. The framework enables both overall efficiency assessment and stage-specific diagnosis of managerial weaknesses. Methodologically, the study demonstrates how resilience theory can be operationalized into a network efficiency structure suitable for process-level diagnosis rather than aggregate scoring. A case study of a representative metro station demonstrates the applicability of the proposed method. The results reveal that while preparedness practices are relatively mature, notable inefficiencies exist in real-time response and post-event recovery due primarily to managerial factors such as communication reliability, personnel coordination, and restoration planning. Improvement simulations confirm that targeted enhancements in these management processes can substantially increase overall emergency efficiency. The findings highlight that seismic resilience is not solely determined by physical infrastructure but is heavily dependent on managerial effectiveness across the emergency cycle. The proposed framework contributes a process-oriented, data-driven tool for evaluating and improving emergency management performance and offers practical guidance for metro operators seeking to strengthen resilience under earthquake scenarios. Full article