Search Results (476)

Light detection and ranging (LiDAR) provides three-dimensional environmental information that is critical for maintaining the safety and reliability of autonomous driving systems. However, dust accumulation on the LiDAR window can cause detection errors and degrade performance. This study determined the optimal spray conditions for accumulating dust to evaluate LiDAR sensor cleaning performance. A primary optimization experiment using spray pressure, spray speed, spray distance, and the number of sprays as variables showed that spray pressure and number of sprays had the most significant influence on the kinetic energy and distribution of dust particles. Notably, the interaction between spray distance and number of sprays—related to curvature effects—was identified as a key variable increasing process sensitivity. A supplementary experiment, which added spray angle as a variable, indicated that while spray pressure remained the most significant factor, spray angle and number of sprays had an indirect influence through interaction terms. Both experiments used the same response variable (point cloud data) interactions to stepwise analyze particle transfer and spatial diffusion. The resulting optimal conditions offer a standard basis for evaluating LiDAR cleaning performance and may help improve cleaning efficiency and maintenance strategies. Full article

Background/Objectives: In digital radiography imaging, dual-layer flat-panel detectors (DFDs), in which two flat-panel detector layers are stacked with a minimal distance between the layers and appropriate alignment, are commonly used in material decompositions as dual-energy applications with a single x-ray exposure. DFDs also enable more efficient use of incident photons, resulting in x-ray images with improved noise power spectrum (NPS) and detection quantum efficiency (DQE) performances as single-energy applications. Purpose: Although the development of DFD systems for material decomposition applications is actively underway, there is a lack of research on whether single-energy applications of DFD can achieve better performance than the single-layer case. In this paper, we experimentally observe the DFD performance in terms of the modulation transfer function (MTF), NPS, and DQE with discussions. Methods: Using prototypes of DFD, we experimentally measure the MTF, NPS, and DQE of the convex combination of the images acquired from the upper and lower detector layers of DFD. To optimize DFD performance, a two-step image registration is performed, where subpixel registration based on the maximum amplitude response to the transform based on the Fourier shift theorem and an affine transformation using cubic interpolation are adopted. The DFD performance is analyzed and discussed through extensive experiments for various scintillator thicknesses, x-ray beam conditions, and incident doses. Results: Under the RQA 9 beam conditions of 2.7 μGy dose, the DFD with the upper and lower scintillator thicknesses of 0.5 mm could achieve a zero-frequency DQE of 75%, compared to 56% when using a single-layer detector. This implies that the DFD using 75 % of the incident dose of a single-layer detector can provide the same signal-to-noise ratio as a single-layer detector. Conclusions: In single-energy radiography imaging, DFD can provide better NPS and DQE performances than the case of the single-layer detector, especially at relatively high x-ray energies, which enables low-dose imaging. Full article

Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational fluid dynamics (CFD) and dynamic mesh technology. By implementing the volume of fluid (VOF) multiphase flow model and the standard k-ω turbulence model, the study simulates the dynamic distribution of lubricant in gear meshing zones and analyzes critical parameters such as the oil volume fraction, eddy viscosity, and turbulent kinetic energy. The results show that reducing the oil injection distance significantly enhances lubricant coverage and continuity: as the injection distance increases from 4.8 mm to 24 mm, the lubricant shifts from discrete droplets to a dense wedge-shaped film, mitigating lubrication failure risks from secondary atomization and energy loss. The optimized injection distance also improves the spatial stability of eddy viscosity and suppresses excessive dissipation of turbulent kinetic energy, enhancing both the shear-load capacity and thermal management. Dynamic data from monitoring point P show that reducing the injection distance stabilizes lubricant velocity and promotes more consistent oil film formation and heat transfer. Through multiphysics simulations and parametric analysis, this study elucidates the interaction between geometric parameters and hydrodynamic behaviors in jet lubrication systems. The findings provide quantitative evaluation methods for structural optimization and energy control in gear lubrication systems, offering theoretical insights for thermal management and reliability enhancement in high-speed transmission. These results contribute to the lightweight design and sustainable development of industrial equipment. Full article

Building energy prediction faces challenges such as data scarcity while Transfer Learning (TL) demonstrates significant potential by leveraging source building energy data to enhance target building energy prediction. However, the accuracy of TL heavily relies on selecting appropriate source buildings as the source data. This study proposes a novel, easy-to-understand, statistics-based visualization method that combines the Euclidean distance and Pearson correlation coefficient for selecting source buildings in TL for target building electricity prediction. Long Short-Term Memory, the Gated Recurrent Unit, and the Convolutional Neural Network were applied to verify the appropriateness of the source domain buildings. The results showed the source building, selected via the method proposed by this research, could reduce 65% of computational costs, while the RMSE was approximately 6.5 kWh, and the R² was around 0.92. The method proposed in this study is well suited for scenes requiring rapid response times and exhibiting low tolerance for prediction errors. Full article

Penta-graphene, a novel two-dimensional carbon allotrope entirely composed of pentagonal carbon rings, has attracted increasing attention due to its unique geometric structure, mechanical robustness, and intrinsic semiconducting nature. In this study, we systematically investigate the adsorption behavior of three typical dissolved gases in transformer oil (CO, C₂H₂, and C₂H₄) on penta-graphene using first-principles calculations based on density functional theory. The optimized adsorption configuration, adsorption energy, charge transfer, adsorption distance, band structure, density of states, charge density difference, and desorption time are analyzed to evaluate the sensing capability of penta-graphene. Results reveal that penta-graphene exhibits moderate chemical interactions with CO and C₂H₂, accompanied by noticeable charge transfer and band structure changes, whereas C₂H₄ shows weaker physisorption characteristics. The projected density of states analysis further confirms the orbital hybridization between gas molecules and the substrate. Additionally, the desorption time calculations suggest that penta-graphene possesses good sensing and recovery potential, especially under elevated temperatures. These findings indicate that penta-graphene is a promising candidate for use in gas sensing applications related to the monitoring of dissolved gases in transformer oils. Full article

Dye-sensitized solar cells (DSSCs) are promising alternatives for power generation due to their environmental friendliness, cost effectiveness, and strong performance under diffused light. Conversely, their low spectral response in the ultraviolet (UV) region significantly obliterates their overall performance. The so-called luminescent down-shifting (LDS) presents a practical solution by converting high-energy UV photons into visible light that can be efficiently absorbed by sensitizer dyes. Herein, a conventional solid-state technique was applied for the synthesis of an LDS, europium (II)-doped barium orthosilicate (BaSiO₃:Eu²⁺) material. The material exhibited strong UV absorption, with prominent peaks near 400 nm and within the 200–300 nm range, despite a weaker response in the visible region. The estimated optical bandgap was 3.47 eV, making it well-suited for UV absorbers. Analysis of the energy transfer mechanism from the LDS material to the N719 dye sensitizer depicted a strong spectral overlap of $2\times {10}^{10}{\mathrm{M}}^{-1}{\mathrm{c}\mathrm{m}}^{-1}{\mathrm{n}\mathrm{m}}^4$, suggesting efficient energy transfer from the donor to the acceptor. The estimated Förster distance was approximately $6.83\mathrm{n}\mathrm{m}$, which matches the absorption profile of the dye-sensitizer. Our findings demonstrate the potential of BaSiO₃:Eu²⁺ as an effective LDS material for enhancing UV light absorption and improving DSSC performance through increased spectral utilization and reduced UV-induced degradation. Full article

Frost heave in seasonally frozen soil surrounding natural gas pipelines (NGPs) can cause severe damage to adjacent infrastructure, including road surfaces and buildings. Based on the stratigraphic characteristics of seasonal frozen soil in Beijing, a soil–natural gas pipeline–heat pipe heat transfer model was developed to investigate the mitigation effect of the soil-freezing phenomenon by transferring shallow geothermal energy utilizing heat pipes. Results reveal that heat pipe configurations (distance, inclination angle, etc.) significantly affect soil temperature distribution and the soil frost heave mitigation effect. When the distance between the heat pipe wall and the NGP wall reaches 200 mm, or when the inclined angle between the heat pipe axis and the model centerline is 15°, the soil temperature above the NGP increases by 9.7 K and 17.7 K, respectively, demonstrating effective mitigation of the soil frost heave problem. In the range of 2500–40,000 W/(m·K), the thermal conductivity of heat pipes substantially impacts heat transfer efficiency, but the efficiency improvement plateaus beyond 20,000 W/(m·K). Furthermore, adding fins to the heat pipe condensation sections elevates local soil temperature peaks above the NGP to 274.2 K, which is 5.5 K higher than that without fins, indicating enhanced heat transfer performance. These findings show that utilizing heat pipes to transfer shallow geothermal energy can significantly raise soil temperatures above the NGP and effectively mitigate the soil frost heave problem, providing theoretical support for the practical applications of heat pipes in soil frost heave management. Full article

Infrared video satellites have the characteristics of wide-area long-duration surveillance, enabling continuous operation day and night compared to visible light imaging methods. Therefore, they are widely used for continuous monitoring and tracking of important targets. However, energy attenuation caused by long-distance radiation transmission reduces imaging contrast and leads to the loss of edge contours and texture details, posing significant challenges to target tracking algorithm design. This paper proposes an infrared small-target tracking method, the UIMM-Tracker, based on the tracking-by-detection (TbD) paradigm. First, detection uncertainty is measured and injected into the multi-model observation noise, transferring the distribution knowledge of the detection process to the tracking process. Second, a dynamic modulation mechanism is introduced into the Markov transition process of multi-model fusion, enabling the tracking model to autonomously adapt to targets with varying maneuvering states. Additionally, detection uncertainty is incorporated into the data association method, and a distance cost matrix between trajectories and detections is constructed based on scale and energy invariance assumptions, improving tracking accuracy. Finally, the proposed method achieves average performance scores of 68.5%, 45.6%, 56.2%, and 0.41 in IDF1, MOTA, HOTA, and precision metrics, respectively, across 20 challenging sequences, outperforming classical methods and demonstrating its effectiveness. Full article

Limited battery life compromises the usability of inspection and operation robots in hazardous environments such as nuclear sites under decommissioning. Both manually replacing the batteries and installing charging bays may be infeasible. Inductive wireless power transfer is a possible solution to deliver power through barriers such as reinforced concrete walls without physical contact. However, when requiring decent power (e.g., 100 W) to be transmitted over longer distances, the exaggerated dimensions of transmitting and receiving coils restrain the integrations with mobile robots. In this paper, a novel retractable design of the coil used in an inductive wireless power charging system is proposed, proving the minor deformation of the winding shape does not affect the transmission efficiency. A prototype with 5× size compression is implemented and tested. It successfully transmits 116.5 W over a distance of 1 m with 68.72% energy efficiency. The principle can be applied to a wide range of mobile platforms with a limited payload area where remote power is needed. Full article

This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained on either impedance measurements or scattering parameters acquired from the transmitter side, this NN effectively predicts the inter-coil distance and identifies optimal capacitance values for maximizing power transfer. Validation using both simulated and experimental data demonstrates consistently low prediction error rates. Second, a separate NN is employed to predict the optimal transmission frequency for minimizing the phase angle between voltage and current, thereby maximizing the power factor. This NN, validated on experimental data spanning various load conditions and inter-coil distances, achieves performance comparable to traditional PI control, but with significantly faster prediction speeds. This speed advantage is crucial for real-time applications and directly contributes to improved power efficiency. The results presented in this study, including the high accuracy of distance and capacitance prediction and the rapid determination of optimal frequencies for power factor maximization, showcase the significant potential of NNs for optimizing WPT systems. These findings open the way for more efficient, adaptable, and intelligent wireless energy transfer solutions, with potential applications ranging from dynamic charging of electric vehicles to real-time optimization of implantable medical devices. Full article

The hydrodynamics characteristics of artificial water diversion canals with long-distance and inter-basin multi-stage sluice gate regulations are prone to sudden increases and decreases, and sluice gate discharge differs from that of natural rivers. Research on the change characteristics of hydrological elements in artificial canals under the control of sluice gates is lacking, as are scientifically accurate calculations of sluice gate discharge. Therefore, addressing these gaps in long-distance artificial water transfer is of great importance. In this study, real-time operation data of 61 sluice gates, pertaining to the period from May 2019 to July 2021, including data on water levels, flow discharge, velocity, and sluice gate openings in the main canal of the Middle Route of the South-to-North Water Diversion Project of China, were analyzed. The discharge coefficient of each sluice gate was calculated by the dimensional analysis method, and the unit-width discharge was modeled as a function of gate opening ($e$), gravity acceleration ($g$), and energy difference ($H$). Through logarithmic transformation of the Buckingham Pi theorem-derived equation, a linear regression model was used. Data within the relative opening orifice flow regime were selected for fitting, yielding the discharge coefficients and stage–discharge relationships. The results demonstrate that during the study period, the water level, discharge, and velocity of the main canal showed an increasing trend year by year. The dimensional analysis results indicate that the stage–discharge response relationship followed a power function ($Q\propto {(\frac{H}{e})}^{constant}$) and that there was a good linear relationship between $l{g}^{(\frac{H}{e})}$ and $l{g}^{(\frac{K}{e})}$ (R² > 0.95, $K={(q^2/g)}^{1/3}$). By integrating geometric, operational, and hydraulic parameters, the proposed method provides a practical tool and a scientific reference for analyzing sluice gates’ regulation and hydrological response characteristics, optimizing water allocation, enhancing ecological management, and improving operational safety in long-distance inter-basin water diversion projects. Full article

Solar interface evaporation is a promising technology for sustainable freshwater acquisition. Regulating the hydrophilicity/hydrophobicity of the evaporator can optimize the water transport, heat transfer, and evaporation enthalpy during the evaporation process, thereby significantly improving the evaporation performance. The CoSe/Co-SeC nanoflower was prepared by high-temperature selenization of ZIF-67. Each petal of the nanoflower is loaded with a density-gradient distribution CoSe/Co, forming an uneven hydrophilic and hydrophobic surface that transitions from bottom hydrophilicity to top hydrophobicity. During the evaporation process, the hydrophilic bottom of the petals promotes rapid water supply, while the hydrophobic top of the petals protrudes from the water surface to form a large number of solid–liquid–gas three-phase interfaces. Therefore, water clusters activated by the strong hydrophilic sites at the bottom of the petals can reach the gas–liquid interface after a very short transmission distance and achieve water cluster evaporation. In addition, the nanoflower optimized the heat transfer at the solid–liquid interface and further promoted the increase in evaporation rate through micro-meniscus evaporation (MME). As a result, the evaporation rate and energy efficiency of the CoSe/Co-SeC evaporator are as high as 2.44 kg m⁻² h⁻¹ and 95.5%. This work passes controllable preparation of the gradient CoSe/Co-SeC and shows the enormous potential of micro-hydrophobic and hydrophilic regulation for improving solar interface evaporation performance. Full article

Light detection and ranging (LiDAR) sensor technology for people detection offers a significant advantage in data protection. However, to design these systems cost- and energy-efficiently, the relationship between the measurement data and final object detection output with deep neural networks (DNNs) has to be elaborated. Therefore, this paper presents augmentation methods to analyze the influence of the distance, resolution, noise, and shading parameters of a LiDAR sensor in real point clouds for people detection. Furthermore, their influence on object detection using DNNs was investigated. A significant reduction in the quality requirements for the point clouds was possible for the measurement setup with only minor degradation on the object list level. The DNNs PointVoxel-Region-based Convolutional Neural Network (PV-RCNN) and Sparsely Embedded Convolutional Detection (SECOND) both only show a reduction in object detection of less than 5% with a reduced resolution of up to 32 factors, for an increase in distance of 4 factors, and with a Gaussian noise up to $\mu =0$ and $\sigma =0.07$. In addition, both networks require an unshaded height of approx. 0.5 m from a detected person’s head downwards to ensure good people detection performance without special training for these cases. The results obtained, such as shadowing information, are transferred to a software program to determine the minimum number of sensors and their orientation based on the mounting height of the sensor, the sensor parameters, and the ground area under consideration, both for detection at the point cloud level and object detection level. Full article

With the increasing penetration of renewable energy generation in energy systems, power and district heating systems (PHSs) continue to encounter challenges with wind and solar curtailment during scheduling. Further integration of renewable energy generation can be achieved by exploring the flexibility of existing systems. Therefore, this study systematically explores the deep transfer modifications of a specific thermal power plant based in Liaoning, China, and the operational characteristics of the heating supply system of a particular heating company. In addition, the overall PHS operational performance is analyzed. The results indicate that both absorption heat pumps and solid-state electric thermal storage technologies effectively improve system load regulation capabilities. The temperature decrease in the water medium in the primary network was proportional to the pipeline distance. When the pipeline lengths were 1175 and 14,665 m, the temperature decreased by 0.66 and 3.48 °C, respectively. The heat exchanger effectiveness and logarithmic mean temperature difference (LMTD) were positively correlated with the outdoor temperature. When the outdoor temperature dropped to −18 °C, the heat exchanger efficiency decreased to 60%, and the LMTD decreased to 17.5 °C. The study findings provide practical data analysis support to address the balance between power supply and heating demand. Full article

Full dependence on the main electrical grid carries risks, including high electricity costs and increased power losses due to the distance between power plants and consumers. An energy community consists of distributed generation resources and consumers within a localized area, aiming to produce electricity economically and sustainably while minimizing long-distance power transfers and promoting renewable energy integration. In this paper, a method for the optimal and sustainable operation of energy communities organized in interconnected microgrids is developed. The microgrids examined in this work consist of residential buildings, plug-in electric vehicles (PEVs), renewable energy sources (RESs), and local generators. The primary objective of this study is to minimize the operational costs of the energy community resulting from the electricity exchange with the main grid and the power production of local generators. To achieve this, microgrids efficiently share electric power, regulate local generator production, and leverage energy storage in PEVs for power management, reducing the need for traditional energy storage installation. Additionally, this work aims to achieve net-zero energy exchange with the main grid, reduce greenhouse gas (GHG) emissions, and decrease power losses in the distribution lines connecting microgrids, while adhering to numerous technical and operational constraints. Detailed simulations were conducted to prove the effectiveness of the proposed approach. Full article