Browse Topic: Logistics
The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes refined class-level alignment, strengthens fault states representation, and shows notable superiority in both accuracy and robustness.
Cu-Fe alloys exhibit excellent performance, and increasing Fe content reduces costs. However, high-Fe Cu-Fe alloys exhibit limited formability at room temperature, while warm rolling improves their processability. This research investigates the effect of rolling temperature on the microstructural transformations and texture development in the Cu-10Fe alloys. The experimental result is that during cold rolling, the morphology of Fe phases is mainly fibrous. With the rolling temperature rising, the microstructural morphology of Fe phases becomes globular / elliptical. The difference is that with the increase in rolling temperature, the average grain size of the Cu-10Fe alloy first decreases and then increases. The matrix microstructure remains dominated by deformed grains, with significant recovery observed at 500 °C. The Fe-phase microstructure primarily consists of substructured grains, and dynamic recovery intensifies with rising temperature. During cold rolling, high dislocation density and localized strain heterogeneity lead to dispersed texture orientations and low strength. In contrast, warm rolling promotes dislocation climb and dynamic recovery, triggering partial recrystallization. This reduces randomly oriented grains, enhances the strength and continuity of specific textures, and establishes a more concentrated texture distribution. This forms a structure with fine recrystallized grains embedded within deformed grains, exhibiting slightly lower strength than cold-rolled samples but better elongation. These findings reveal the crucial role of deformation temperature in controlling microstructure transformation and texture evolution, especially by regulating the recovery and recrystallization behaviors of Cu-10Fe alloys. The above results hope to provide some theoretical and experimental basis for a new approach to improve processing technologies for new Cu-Fe-based composites.
To meet the requirements for efficient evacuation during tunnel navigation, the pontoon of the tunnel bank wall evacuation channel in a large-scale navigation building is taken as the research object. The water body and water wave are simulated using the coupled Euler-Lagrangian method and the push-plate wave method, respectively. The water boundary is processed using the viscoelastic artificial boundary method, and a simulation analysis model of the pontoon under the combined action of water waves and load is established. The results show that the average relative vertical displacement of the pontoon is basically the same under the condition of water wave and no water waves, but the fluctuation range of the pontoon is larger under the condition of water waves. When there are water waves and different loads, the maximum Mises stress distribution of the pontoon is essentially the same, and both are less than 80 MPa, meeting the strength requirements and demonstrating the rationality of the pontoon design.
With the rapid development of China’s logistics and transportation industry, how to reasonably and efficiently use drones to carry out logistics and distribution business under the background of UAM has become a focus of attention for social enterprises. It is urgent to propose a feasible logistics drone take-off and landing sites layout planning method based on reality. The article first explains the principles and connotations of the layout of logistics unmanned aerial vehicle take-off and landing sites, dividing logistics distribution scenarios into three sub scenarios: urban end of pipe distribution, medical material distribution, and short distance logistics transportation. Then, based on strong constraint conditions, preliminary site selection is carried out for each sub scenario. The HRW-Kmedoids algorithm is used to optimize the site selection points for each sub scenario, and the final site selection and alternative points are obtained. The Huangpu District of Guangzhou is taken as a case study for field application. The results show that compared with the traditional method, this method can effectively avoid the actual undeployable area, and it is helpful to reasonably plan the location of the vertical take-off and landing field according to the actual demand, so as to provide some reference for the future vertical take-off and landing field layout.
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
The turbine hybrid electric propulsion system is an important form of green aviation. Unlike the single form of aviation power scheme, the hybrid energy system is flexible in architecture, uses two or more energy forms, and has diverse energy sources. Under different mission requirements, it needs to meet the requirements of mass balance, energy balance, and power demand, etc. Therefore, The control and distribution management between different energy systems have become the key to hybrid power, and power management technology is one of the key challenges in the development of aviation hybrid power control systems. This paper reviews the current structural forms of aviation turbine hybrid electric propulsion systems, analyzes the current research status of power management technology for aviation hybrid systems, and points out that the online power management method based on optimization is the best power management technology solution for turbine hybrid electric propulsion systems. Establishing a high-precision and realtime on-board power calculation model, breaking through the power management method based on the integrated flight and engine, and improving the applicability of the power management method throughout the service life are important directions for promoting the development of online power management technology.
As acoustic requirements for NVH trim components become increasingly constrained by mass, cost, and sustainability targets, traditional approaches to inner dash design based on spatially averaged Transmission Loss (TL) metrics are reaching their practical limits. In fully built vehicles, the acoustic performance of the inner dash is governed by its global insulation capability but also by strong spatial heterogeneity and its interaction with spatially distributed noise sources such as the power unit, gearbox, and tyre-road excitation. This paper presents a test-based methodology for the spatial optimisation of inner dash acoustic performance using reciprocal holography. By applying a calibrated sound power source within the vehicle cabin and measuring the reciprocal response in the engine bay and wheel-arch regions, a high-resolution spatial Transmission Loss “hologram” of the inner dash is obtained under in-situ conditions. The resulting spatial data enables the identification of localised acoustic weak points that are not observable using conventional testing methods. To bridge the gap between passive component characterisation and real-world vehicle operation, the spatial TL hologram is subsequently evaluated using representative operational source sound power data to prioritise acoustically relevant regions. This enables the transmitted acoustic energy to be evaluated under realistic driving conditions. The holographic data is then coupled with a parametric acoustic model of the inner dash system, allowing localised mass redistribution to be optimised using a genetic algorithm while respecting packaging and manufacturing constraints.
There's a well-known video from San Francisco in 1906 that comes up repeatedly in mobility discussions here in the 21st Century. If you haven't seen A Trip Down Market Street, it depicts the absolute bonkers variety of transportation methods used on Market Street back then: cable cars, horsecars, streetcars, pedestrians, automobiles and more. Past is prologue in a world that is adding scooters, delivery robots and other last-minute delivery vehicles to our streets. At the 2026 New York International Auto Show in April, Honda displayed its latest option in the form of the Fastport eQuad Prototype. The eQuad was originally unveiled at Eurobike 2025 and technically comes from Fastport, a micromobility venture from the Honda New Business Innovation Lab that was established to work on projects with global logistics companies. Jamie Davies, chief of operations for Fastport, called the group a kind of startup within Honda. “Three years ago,” Davies told SAE Media in New York, “a small group of Honda associates [came] together and [said], Okay, how can we create a new value for the company, a new business vertical? And so we've run the project in an agile way, working with customers all along the way to understand what their needs are, what the requirements are, and to bring to market something that fits.”
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