Search Results (2,183)

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping. Full article

This work explores the principle of utilizing gallium nitride devices as a gate driver for silicon carbide power devices. As silicon has long reached its performance limits, Wide Bandgap semiconductors such as gallium nitride and silicon carbide have emerged as promising alternatives due to their superior characteristics. However, few publications suggest using a gallium nitride-based gate driver for silicon carbide, high-voltage power devices. Unlike standard voltage source gate drivers, this paper proposes a novel bi-polar current source resonant gate driver topology using gallium nitride transistors as a gate drive circuit for silicon carbide power switching. The driver receives a single input supply and pulsed width modulation signal, producing a high current bi-polar gate driving signal. The gate driver is validated by employing the proposed gate driver to a high-power silicon carbide transistor in a resonant boost converter. The experimental results show that the new gate driver recovers the gate charge wasted energy and provides high performances in varying high voltage loads at a 2.5 MHz switching frequency while reducing the gate losses by 26%. Full article

This study seeks to improve the performance of a low-temperature differential free-piston Stirling air conditioner (FPSAC). To achieve this, a novel approach is proposed, which replaces the conventional simple harmonic drive with a multi-harmonic drive. This modification aims to optimize the motion of the driving piston, bringing it closer to the ideal movement pattern. The research involves both thermodynamic and dynamic coupling simulations of the FPSAC, complemented by experimental verification of its key performance parameters. A thermodynamic model for the gas medium, employing a quasi-one-dimensional dynamic approach for compressible fluids, and a nonlinear two-dimensional vibration dynamic model for the solid piston are developed, focusing on the low-temperature differential FPSAC physical model. The finite difference method is employed to numerically simulate the entire system, including the electromagnetic thrust of the multi-harmonic-driven linear oscillating motor, fluid transport equations, and the nonlinear dynamic equations of the power and gas control pistons. Variations in displacement, velocity, and pressure for each control volume at any given time are obtained, along with the indicator and temperature–entropy diagrams after the system stabilizes. The simulation results show that, in cooling mode, assuming no heat loss or mechanical friction, the Stirling cooler operates at a frequency of 80 Hz. Using the COP_sin value for the simple harmonic drive as a baseline, performance is improved by altering the driving method. Under the multi-harmonic drive, the COP_c5 increased by 10.03% and COP_c7 by 14.23%. In heating mode, the COP under the multi-harmonic drive improved by 0.51% for COP_h5 and 2.61% for COP_h7. Performance experiments were conducted on the low-temperature differential FPSAC, and the key parameter test results showed good agreement with the simulation outcomes. The maximum deviation at the trough was found to be less than 2.45%, while at the peak, the maximum error did not exceed 3.61%. When compared to the simple harmonic drive, the application of the multi-harmonic drive significantly enhances the overall efficiency of the FPSAC, demonstrating its superior performance. The simulation analysis and experimental results indicate a significant improvement in the coefficient of performance of the Stirling cooler under the multi-harmonic drive at the same power level, demonstrating that the multi-harmonic drive is an effective approach for enhancing FPSAC performance. Furthermore, it should be noted that the method proposed in this study is applicable to other types of low-temperature differential free-piston Stirling air conditioners. Full article

Cell lysis is the starting step of many biomedical assays. Electric field-based cell lysis is widely used in many applications, including point-of-care (POC) applications, because it provides an easy one-step solution. Many electric field-based lysis methods utilize micro-electrodes to apply short electric pulses across cells. Unfortunately, these cell lysis devices produce relatively low cell lysis efficiency as electric fields do not reach a significant portion of cells in the sample. Additionally, the utility of syringe pumps for flow cells in and out of the microfluidics channel causes cell loss and low throughput cell lysis. To address these critical issues, we suspended the cells in a sessile drop and concentrated on the electrodes. We used low-frequency AC electric fields (1 Vpp, 0–100 kHz) to drive the cells effectively towards electrodes and lysed using a short pulse of 10 V. A post-lysis analysis was performed using a hemocytometer, UV-vis spectroscopy, and fluorescence imaging. The results show that the pre-electric polarization of cells, prior to applying short electrical pulses, enhances the cell lysis efficiency. Additionally, the application of AC electric fields to concentrate cells on the electrodes reduces the assay time to about 4 min. In this study, we demonstrated that low-frequency AC electric fields can be used to pre-polarize and concentrate cells near micro-electrodes and improve cell lysis efficiency. Due to the simplicity and rapid cell lysis, this method may be suitable for POC assay development. Full article

This study investigates the modeling and dynamic analysis of three coupled electromechanical systems, emphasizing interactions between a magnetic linear drive and frictional contact with flat springs. The experimental setup includes a table driven by a three-phase permanent magnet linear synchronous motor (PMLSM) using an LMCA4 inductor, LMCAS3 magnetic track, and Xenus XTL controller. Mechanical phenomena such as stick-slip friction and impulsive loads are observed, particularly due to the rapid buckling of flat springs. These springs transition between sliding friction and fixation, impacting the motor’s operation during reciprocating velocity trajectories and generating acoustic emissions. Numerical simulations using COMSOL Multiphysics evaluate the magnetic field and system geometry in two- and three-dimensional spaces. Key findings include mechanical stick-slip vibrations, numerical modeling of the linear drive, and comparative analysis of experimental and simulated inductor current variations. Additionally, energy loss mechanisms under irregular loading conditions are assessed. The results highlight the coupling between friction-induced current changes and magnetic field variations, elucidating their impact on motor efficiency, vibration propagation, and acoustic emissions. The study provides insights into optimizing the design and reliability of coreless linear motors for precision applications under discontinuous loading. Full article

Retinitis pigmentosa (RP) is a hereditary disease characterized by progressive vision loss ultimately leading to blindness. This condition is initiated by mutations in genes expressed in retinal cells, resulting in the degeneration of rod photoreceptors, which is subsequently followed by the loss of cone photoreceptors. Mutations in various genes expressed in the retina are associated with RP. Among them, mutations in the rhodopsin gene (RHO) are the most common cause of this condition. Due to the involvement of numerous genes and multiple mutations in a single gene, RP is a highly heterogeneous disease making the development of effective treatments particularly challenging. The progression of this disease involves complex cellular responses to restore cellular homeostasis, including the unfolded protein response (UPR) signaling, autophagy, and various cell death pathways. These mechanisms, however, often fail to prevent photoreceptor cell degradation and instead contribute to cell death under certain conditions. Current research focuses on the pharmacological modulation of the components of these pathways and the direct stabilization of mutated receptors as potential treatment strategies. Despite these efforts, the intricate interplay between these mechanisms and the diverse causative mutations involved has hindered the development of effective treatments. Advancing our understanding of the interactions between photoreceptor cell death mechanisms and the specific genetic mutations driving RP is critical to accelerate the discovery and development of therapeutic strategies for this currently incurable disease. Full article

Diabetic retinopathy (DR) is a complication of diabetes, characterized by progressive microvascular dysfunction that can result in vision loss. Chronic hyperglycemia drives oxidative stress, endothelial dysfunction, and inflammation, leading to retinal damage and complications such as neovascularization. Current treatments, including anti-VEGF agents, have limitations, necessitating the exploration of alternative therapeutic strategies. Fractalkine (CX3CL1), a chemokine with dual roles as a membrane-bound adhesion molecule and a soluble chemoattractant, has emerged as a potential therapeutic target. Its receptor, CX3CR1, is expressed on immune cells and mediates processes such as immune cell recruitment and microglial activation through intracellular signaling pathways. In DR, soluble fractalkine plays critical roles in retinal inflammation, angiogenesis, and neuroprotection, balancing tissue damage and repair. In DR, elevated fractalkine levels are associated with retinal inflammation and endothelial dysfunction. Experimental studies suggest that fractalkine deficiency exacerbates the severity of diabetic retinopathy (DR), whereas exogenous fractalkine appears to reduce inflammation, oxidative stress, and neuronal damage. However, its role in pathological angiogenesis within DR remains unclear and warrants further investigation. Preclinical evidence indicates that fractalkine may hold therapeutic potential, particularly in mitigating tissue injury and inflammation associated with early-stage DR. Full article

Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the geographical location, road conditions, and environment. The data are divided into accident-related categories, weather-related categories, and road- and environment-related categories. The machine learning method is improved through integration for the accident level prediction. In the experiment, effective preprocessing measures were taken for problems such as data imbalance, missing values, the encoding of categorical variables, and the standardization of numerical features. The unbalanced distribution of “Severity” was improved through under-sampling and over-sampling techniques. Firstly, we adopted a multi-stage fusion strategy. A multi-layer perceptron (MLP) was used for the preliminary prediction, and then its result was combined with the original features to form a new feature. Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. The analysis results show that the improved machine learning model is significantly superior to a single model in the overall performance. The “MLP + random forest” model performs well in evaluation indicators such as the accuracy, recall rate, and F1 value. The accuracy rate is as high as 94%. In the prediction of different traffic accident severity levels (minor, moderate, and severe), the improved machine learning model also generally shows better performance and stability. The research results of this study have broad prospects in the field of intelligent driving. It can realize real-time accident prediction and early warnings, and provide decision support for drivers and autonomous driving systems. The research also provides a scientific basis for traffic planning and management departments to improve driving conditions and reduce the probability and losses of traffic accidents. Full article

The integrity of habitat quality is a pivotal cornerstone for the sustainable advancement of local ecological systems. Rapid urbanization has led to habitat degradation and loss of biodiversity, posing severe threats to regional sustainability, particularly in extremely vulnerable arid zones. However, systematic research on the assessment indicators, limiting factors, and driving mechanisms of habitat quality in arid regions is notably lacking. This study takes Urumqi, an oasis city in China’s arid region, as a case study and employs the InVEST and PLUS models to conduct a dynamic evaluation of habitat quality in Urumqi from 2000 to 2022 against the backdrop of land use changes. It also simulates habitat quality under different scenarios for the year 2035, exploring the temporal and spatial dynamics of habitat quality and its driving mechanisms. The results indicate a decline in habitat quality. The habitat quality in the southern mountainous areas is significantly superior to that surrounding the northern Gurbantunggut Desert, and it exhibits greater stability. The simulation and prediction results suggest that from 2020 to 2035, habitat degradation will be mitigated under Ecological Protection scenarios, while the decline in habitat quality will be most pronounced under Business-As-Usual scenarios. The spatial distribution of habitat quality changes in Urumqi exhibits significant autocorrelation and clustering, with these patterns intensifying over time. The observed decline in habitat quality in Urumqi is primarily driven by anthropogenic activities, urban expansion, and climate change. These factors have collectively contributed to significant alterations in the landscape, leading to the degradation of ecological conditions. To mitigate further habitat quality loss and support sustainable development, it is essential to implement rigorous ecological protection policies, adopt effective ecological risk management strategies, and promote the expansion of ecological land use. These actions are crucial for stabilizing and improving regional habitat quality in the long term. Full article

Motor efficiency presents a trade-off between low-speed and high-speed regions. Additionally, the cross-sectional area of hairpin motors employing rectangular wires is larger than that of round wires, thereby amplifying AC copper losses. As the operating speed increases, the AC copper loss also becomes more pronounced; therefore, efficiently determining the optimal design point considering these characteristics is essential. This study optimizes the efficiency of an electric vehicle (EV) simulation is conducted using MATLAB 2024, and the main operating points according to the driving cycle are selected. For the EV simulation to select the main operating points, the driving cycle of the multi-cycle test method, which is used for measuring domestic driving range, is considered to enhance the validity of the operating points. The efficiency optimization of the main operating points was performed considering the AC copper loss, and essential parameters such as the torque ripple and total harmonic distortion of the back-electromotive force were incorporated as constraints. Furthermore, the predictive performances of the 11 metamodels were compared to identify the most suitable metamodel for the output and design variables. Subsequently, the selected metamodel was integrated with four optimization algorithms to optimize the design. Full article

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid. Full article

To decrease the complexity and increase the efficiency of wireless power transfer (WPT) systems, this paper proposes a novel self-excited invert rectification method for the design of the invert rectifier of the receiver (Rx). The self-excited invert rectifier can perform the self-driving and soft-switching of the MOSFETs as well as the frequency-tracking function without a microcontroller. This allows us to greatly simplify the structure of the invert rectifier and increase the transfer efficiency (TE) of the WPT system. Firstly, a self-excited invert rectifier circuit is designed, and a self-excited invert rectification method is studied. Additionally, the power loss of the self-excited invert rectifier is analyzed. Finally, the self-excited invert rectifier of the WPT experimental system is designed. The self-excited invert rectification method is then verified. The key component parameters of the self-excited invert rectifier are provided and optimized. The TE of the WPT system that includes the self-excited invert rectifier is improved by more than 5% without a microcontroller. The self-excited invert rectifier of the Rx provides a practical solution for decreasing the complexity and increasing the TE of the WPT system. Full article

Lane detection is a pivotal technology of the intelligent driving system. By identifying the position and shape of the lane, the vehicle can stay in the correct lane and avoid accidents. Image-based deep learning is currently the most advanced method for lane detection. Models using this method already have a very good recognition ability in general daytime scenes, and can almost achieve real-time detection. However, these models often fail to accurately identify lanes in challenging scenarios such as night, dazzle, or shadows. Furthermore, the lack of diversity in the training data restricts the capacity of the models to handle different environments. This paper proposes a novel method to train CycleGAN with existing daytime and nighttime datasets. This method can extract features of different styles and multi-scales, thereby increasing the richness of model input. We use CycleGAN as a domain adaptation model combined with an image segmentation model to boost the model’s performance in different styles of scenes. The proposed consistent loss function is employed to mitigate performance disparities of the model in different scenarios. Experimental results indicate that our method enhances the detection performance of original lane detection models in challenging scenarios. This research helps improve the dependability and robustness of intelligent driving systems, ultimately making roads safer and enhancing the driving experience. Full article

Hydraulic motors have been widely used in large-scale machinery such as ground heavy equipment and heavy-duty vehicles, ships, and so on because of their high-power drive capability. However, the driving device is confronted with constraints related to its size and weight. Typically, the hydraulic axial piston motor is preferred for its simplicity and efficiency. However, the oil distributor in traditional hydraulic motors faces significant challenges, such as evident oil leakage and power loss from the mating surfaces of the fixed oil distributor and rotating cylinder block. To enhance the reliability and performance of hydraulic motors employed in paper driving applications, this paper introduces a digital radial hydraulic motor used for heavy-duty vehicle traction. The motor is powered by an on-board pump station from which several on/off valves can distribute the hydraulic oil. This design effectively mitigates the performance degradation issues associated with friction and wear in traditional hydraulic motor oil distributors. The drive characteristics of the motor can be flexibly adjusted through the combination of valves. Our investigation into the motor’s design principles and parameter analysis is poised to make an indirect yet significant contribution to the optimization of heavy-duty vehicle traction systems. This paper delineates the application conditions and operational principles of the digital hydraulic motor, thoroughly analyzes the intricate topological interrelationships of its parameters, and meticulously develops a detailed component-level model. Through comprehensive calculations, it reveals the impact of configuration and flow valve parameters on motor efficiency. A simulation model is established for the purpose of verification. Furthermore, the influence of the flow allocation method on efficiency and pressure pulsation is examined, leading to the proposal of a novel flow allocation strategy, the efficacy of which is substantiated through simulation. In conclusion, this paper formulates critical insights to inform the design and selection of components for digital hydraulic motors. These findings may provide a feasible solution for heavy-duty vehicle traction application scenarios. Full article

Alpine wetlands are highly vulnerable to changes caused by global warming. Rapidly and accurately mapping alpine wetlands and analyzing the driving factors of their spatiotemporal changes are crucial for protecting and managing these resources. However, few studies have investigated classification methods and attribution analyses for alpine wetlands. To address this gap, a novel classification method has been developed, integrating the Google Earth Engine, alpine wetland features, and a random forest classifier, named GAWRF, to delineate wetlands in alpine regions. Additionally, an improved Partial Least Squares Structural Equation Model (PLS-SEM) was utilized to explore the mechanisms of spatiotemporal changes in wetlands of the Source Region of Three Rivers (SRTR) from 1990 to 2020. The results indicate (1) the high accuracy of the SRTR land cover maps from 1990 to 2020, with an overall accuracy of above 92.48% and a Kappa coefficient of over 0.91, satisfying the subsequent analysis of wetland spatiotemporal changes; (2) a net loss of 3.8% in the SRTR alpine wetlands, with a notable 7.9% net loss in marsh wetlands and nearly 32,010 km² lost by 2015; and (3) topography and permafrost change as key drivers (as identified by the PLS-SEM), with permafrost contributing 52% to the significant marsh wetland loss from 2010 to 2015. This study aims to provide fundamental information that is essential for the monitoring and conservation of alpine wetlands. Full article