Search Results (270)

The development of slicing equipment for Fritillariae Thunbergii Bulbus (FTB) has been constrained by the absence of precise and reliable simulation model parameters, which has hindered the optimization of structural design through simulation techniques. Taking FTB as the research object, this study aims to resolve this issue by conducting the calibration and experimental validation of the discrete element parameters for FTB. Both intrinsic and contact parameters were obtained through physical experiments, on the basis of which a discrete element model for FTB was established by using the Hertz–Mindlin with bonding model. To validate the calibrated bonding parameters of this model, the maximum shear force was selected as the evaluation index. Significant influencing factors were identified and analyzed through a single-factor test, a two-level factorial test, and the steepest ascent method. Response surface methodology was then applied for experimental design and parameter optimization. Finally, shear and compression tests were conducted to verify the accuracy of calibrated parameters. The results show that the mechanical properties of FTB are significantly affected by the normal stiffness per unit area, the tangential stiffness per unit area, and the bonding radius, with optimal values of 1.438 × 10⁸ N·m⁻³, 0.447 × 10⁸ N·m⁻³, and 1.362 mm, respectively. The relative errors in the shear and compression tests were all within 5.18%. The maximum error between the simulated and measured maximum shear force under three different types of blades was less than 5.11%. The percentages of the average shear force of the oblique blade were reduced by 52.23% and 29.55% compared with the flat and arc blades, respectively, while the force variation trends for FTB remained consistent. These findings confirm the reliability of the simulation parameters and establish a theoretical basis for optimizing the structural design of slicing equipment for FTB. Full article

This work presents a hybrid optical fiber detector by combining the sensing mechanism of the fiber Bragg grating (FBG) and the light extinction method to enable the simultaneous measurement of dust concentration and temperature. Compared with the existing dust concentration sensors, the proposed detector offers three key advantages: intrinsic safety, dual-parameter measurement capability, and potentially network-based monitoring. The critical sensing components of the proposed detector consist of two optical collimators and an FBG. Using the extinction effect of light between the two collimators, the dust concentration and temperature are simultaneously determined by monitoring the intensity and the wavelength of the FBG reflectance spectrum, respectively. The measurement feasibility has been evaluated demonstrating that the two parameters of interest can be effectively sensed with minimally coupled outputs of ±3 pm and ±0.1 mW, respectively. Calibration experiments demonstrate that the change in the intensity of light from the FBG is exponentially related to the dust concentration variation with fitting coefficients equal to 0.948, 0.946, and 0.945 for 200 meshes, 300 meshes, and 400 meshes, respectively. The detector’s relative measurement errors were validated against the weighing method, confirming low measurement deviations. Full article

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm⁻³). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article

Photovoltaic (PV) models are hard to optimize due to their intrinsic complexity and changing operation conditions. Root mean square error (RMSE) is often given precedence in classic single-objective optimization methods, limiting them to address the intricate nature of PV model calibration. To bypass these limitations, this research proposes a novel multi-objective optimization framework balancing accuracy and robustness by considering both maximum error and the L2 norm as significant objective functions. Along with that, we introduce the Random Search Around Bests (RSAB) algorithm, which is a parameterless metaheuristic designed to be effective at exploring the solution space. The primary contributions of this work are as follows: (1) an extensive performance evaluation of the proposed framework; (2) an adaptable function to adjust dynamically the trade-off between robustness and error minimization; and (3) the elimination of manual tuning of the RSAB parameters. Rigorous testing across three PV models demonstrates RSAB’s superiority over 17 state-of-the-art algorithms. By overcoming significant issues such as premature convergence and local minima entrapment, the proposed procedure provides practitioners with a reliable tool to optimize PV systems. Hence, this research supports the overarching goals of sustainable energy technology advancements by offering an organized and flexible solution enhancing the accuracy and efficiency of PV modeling, furthering research in renewable energy. Full article

Structure-from-motion (SfM) is a foundational technology that facilitates 3D scene understanding and visual localization. However, bundle adjustment (BA)-based SfM is usually very time-consuming, especially when dealing with numerous unknown focal length cameras. To address these limitations, we proposed a novel SfM system based on pose-only adjustment (PA) for intrinsic and extrinsic joint optimization to accelerate computing. Firstly, we propose a base frame selection method based on depth uncertainty, which integrates the focal length and parallax angle under a multi-camera system to provide more stable depth estimation for subsequent optimization. We explicitly derive a global PA of joint intrinsic and extrinsic parameters to reduce the high dimensionality of the parameter space and deal with cameras with unknown focal lengths, improving the efficiency of optimization. Finally, a novel pose-only re-triangulation (PORT) mechanism is proposed for enhanced reconstruction completeness by recovering failed triangulations from incomplete point tracks. The proposed framework has been demonstrated to be both faster and comparable in accuracy to state-of-the-art SfM systems, as evidenced by public benchmarking and analysis of the visitor photo dataset. Full article

Constraining neutrino mass through cosmological observations relies on precise simulations to calibrate their effects on large scale structure, while these simulations must overcome computational challenges like dealing with large velocity dispersions and small intrinsic neutrino perturbations. We present an efficient N-body implementation with semi-linear neutrino mass response which gives accurate power spectra and halo statistics. We explore the necessity of correcting the expansion history caused by massive neutrinos and the transition between relativistic and non-relativistic components. The above method of including neutrino masses is built into the memory-, scalability-, and precision-optimized parallel N-body simulation code CUBE 2.0. Through a suite of neutrino simulations, we precisely quantify the neutrino mass effects on the nonlinear matter power spectra and halo statistics. Full article

Optical oxygen sensors have attracted considerable attention owing to their high sensitivity, rapid response, and broad applicability. However, their test results may be affected by fluctuations in the pump light source and instability of the detection equipment. In this study, the intrinsic luminescence of pine wood was utilized as the reference signal, and the luminescence of platinum(II) octaethylporphyrin (PtOEP) was employed as the oxygen indication signal, to fabricate a dual-signal ratiometric oxygen sensor PtOEP/PDMS@Pine. The ratio of the luminescence of pine wood to that of PtOEP was defined as the optical parameter (OP). OP increased linearly with oxygen concentration ([O₂]) in the range of 10–100 kPa, and a calibration curve was obtained. The sensor exhibits excellent anti-interference capabilities, effectively resisting fluctuations from laser sources and detection equipment. It also displays stable hydrophobicity with a contact angle of 118.3° and maintains excellent photostability under continuous illumination. The sensor exhibited long-term stability within 90 days and robust recovery performance during cyclic tests, wherein the response time and recovery time were determined to be 1.4 s and 1.7 s, respectively. Finally, the effects of temperature fluctuations and photobleaching on the sensor’s performance have been effectively corrected, enabling accurate oxygen concentration measurements in complex environments. Full article

Current research on dosimeters based on radiation-induced attenuation (RIA) primarily focused on enhancing radiation sensitivity or reducing dependencies from interference factors. However, their intrinsic sensing performance has received limited attention. This work proposed application and analysis methods for RIA-based dosimeters, validated by a low-cost apparatus using commercial fibers. Initially, a generic protocol of high-dose detection after low-dose calibration was suggested to overcome the various dependencies of RIA, enabling repetitive monitoring of near-stable radiation by simple replacement of commercial fibers. Experiments comparing three dose-loss models demonstrated that the saturation-exponential model exhibited superior accuracy, achieving absolute errors below 4 Gy within a measurable range of up to ~300 Gy. Subsequently, the system’s RIA-based sensitivity was ~125.6 dB·Gy⁻¹·km⁻¹. The resolution and sensitivity expressed by optical power were newly defined, effectively quantifying the decline in precision and response ratio during detection. Moreover, an additional structure was introduced to extend the measurable range. Simulations and experiments under 1-MeV electron irradiation verified that adjustable ranges could be achieved through configuration of attenuation layers. In summary, these advancements provided critical guidance for component selection and operational evaluation, facilitating the commercialization and practical deployment of RIA-based dosimeters. Full article

Intrinsic parameter estimation by self-calibration is commonly used in Unmanned aerial vehicle (UAV)-based photogrammetry with Structure from Motion (SfM). However, obtaining stable estimates of these parameters from image-based SfM—which relies solely on images, without auxiliary data such as ground control points (GCPs)—remains challenging. Aerial imagery acquired with the constant-pitch (CP) flight pattern often exhibits non-linear deformations, highly unstable intrinsic parameters, and even alignment failures. We hypothesize that CP flights form a “critical configuration” that renders certain intrinsic parameters indeterminate. Through numerical experiments, we confirm that a CP flight configuration does not provide sufficient constraints to estimate focal length (f) and the principal point coordinate (cy) in image-based SfM. Real-world CP datasets further demonstrate the pronounced instability of these parameters. As a remedy, we show that by introducing intermediate strips into the CP flight plan—what we call a CP-Plus flight—can effectively mitigate the indeterminacy of f and cy in simulations and markedly improve their stability in all tested cases. This approach enables more effective image-only SfM workflows without auxiliary data, simplifies data acquisition, and improves three-dimensional reconstruction accuracy. Full article

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

Seafloor soil classification is essential for marine engineering, environmental monitoring, and geological surveys. Traditional classification methods, such as physical sampling and acoustic backscatter analysis, have inherent limitations, including spatial constraints and inconsistencies in distinguishing sediments with similar acoustic properties. This study uses frequency-dependent acoustic reflection coefficients to investigate a novel spectral-based approach to seabed soil classification. Experiments were conducted in a controlled aquatic environment to isolate the spectral characteristics of two soil types: poorly graded sand (SP) and poorly graded gravel (GP). The research employed calibrated transducers to measure reflection coefficients across the 100–400 kHz frequency range, allowing for a comparative spectral analysis between the two sediments. The results demonstrate that SP and GP exhibit distinct spectral fingerprints, with SP showing higher reflectance across all measured frequencies, while GP displays a more variable spectral response. These findings suggest that frequency-dependent reflectance provides a more sensitive and accurate classification criterion than conventional backscatter intensity analysis. By eliminating environmental variability and focusing on intrinsic soil properties, this study establishes a foundation for automated, non-invasive classification methods that could be integrated into machine learning frameworks for real-time seabed characterization. The proposed methodology enhances the precision of remote sensing techniques and presents significant advantages in offshore engineering, environmental monitoring, and hydrographic surveys. Future research should extend this approach to diverse sediment types and open marine environments to refine and validate its applicability in real-world scenarios. Full article

The calibration of mobile manipulators requires accurate estimation of both the transformations provided by the localization system and the transformations between sensors and the motion coordinate system. Current works offer limited flexibility when dealing with mobile robotic systems with many different sensor modalities. In this work, we propose a calibration approach that simultaneously estimates these transformations, enabling precise calibration even when the localization system is imprecise. This approach is integrated into Atomic Transformations Optimization Method (ATOM), a versatile calibration framework designed for multi-sensor, multi-modal robotic systems. By formulating calibration as an extended optimization problem, ATOM estimates both sensor poses and calibration pattern positions. The proposed methodology is validated through simulations and real-world case studies, demonstrating its effectiveness in improving calibration accuracy for mobile manipulators equipped with diverse sensor modalities. Full article

This paper provides a comparative analysis of calibration techniques applicable to stereo and near-infrared (NIR) camera systems, with a specific emphasis on the Intel RealSense SR300 alongside a standard 2-megapixel NIR camera. This study investigates the pivotal function of calibration within both stereo vision and NIR imaging applications, which are essential across various domains, including robotics, augmented reality, and low-light imaging. For stereo systems, we scrutinise the conventional method involving a 9 × 6 chessboard pattern utilised to ascertain the intrinsic and extrinsic camera parameters. The proposed methodology consists of three main steps: (1) real-time calibration error classification for stereo cameras, (2) NIR-specific calibration techniques, and (3) a comprehensive evaluation framework. This research introduces a novel real-time evaluation methodology that classifies calibration errors predicated on the pixel offsets between corresponding points in the left and right images. Conversely, NIR camera calibration techniques are modified to address the distinctive properties of near-infrared light. We deliberate on the difficulties encountered in devising NIR–visible calibration patterns and the imperative to consider the spectral response and temperature sensitivity within the calibration procedure. The paper also puts forth an innovative calibration assessment application that is relevant to both systems. Stereo cameras evaluate the corner detection accuracy in real time across multiple image pairs, whereas NIR cameras concentrate on assessing the distortion correction and intrinsic parameter accuracy under varying lighting conditions. Our experiments validate the necessity of routine calibration assessment, as environmental factors may compromise the calibration quality over time. We conclude by underscoring the disparities in the calibration requirements between stereo and NIR systems, thereby emphasising the need for specialised approaches tailored to each domain to guarantee an optimal performance in their respective applications. Full article

Viewing high-redshift sources at near-opposite directions on the sky can ensure, using light-travel-time arguments, acausality between their emitted photons. One utility would be true random-number generation through sensing these via two independent telescopes that each flip a switch based on the latest-arrived colours; for example, to autonomously control a quantum-mechanical (QM) experiment. Although demonstrated with distant quasars, those were not fully acausal pairs, which are restricted when simultaneously viewed from the ground at any single observatory. In optical light, such faint sources also require a large telescope aperture to avoid sampling assumptions when imaged at fast camera framerates: unsensed intrinsic correlations between them or equivalently correlated noise may ruin the expectation of pure randomness. One such case that could spoil a QM test is considered. Based on that, the allowed geometries and instrumental limits are modelled for any two ground-based sites, and their data are simulated. For comparison, an analysis of photometry from the Gemini twin 8 m telescopes is presented using the archival data of well-separated bright stars obtained with the instruments ‘Alopeke (on Gemini North in Hawai’i) and Zorro (on Gemini-South in Chile) simultaneously in two bands (centred at $562\mathrm{nm}$ and $832\mathrm{nm}$) with 17 Hz framerate. No flux correlation is found; these results were used to calibrate an analytic model predicting where a search with a signal-to-noise over 50 at 50 Hz can be made using the same instrumentation. Finally, the software PDQ (Predict Different Quasars) is presented, which searches a large catalogue of known quasars, reporting those with a brightness and visibility suitable to verify acausal, uncorrelated photons at these limits. Full article

The cutting of leaf stems is a critical step in the mechanized harvesting of tuber mustard (Brassica juncea L.). This study focuses on the calibration of parameters for the discrete element model of mustard leaf stems to visualize the cutting process and facilitate numerical simulations. Intrinsic material properties were measured based on mechanical testing, and EDEM2022 simulation software was utilized to calibrate the model parameters. The Hertz–Mindlin (no-slip) model was employed to simulate the stacking angle of mustard leaf stems, and the contact parameters for the discrete element model were determined using a combination of two-level factorial design, steepest ascent, and CCD (central composite design) tests. The results showed that the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction for the leaf stems were 0.45, 0.457, and 0.167, respectively, while for interactions between the leaf stems and the working parts, these values were 0.45, 0.55, and 0.175, respectively. Based on the Hertz–Mindlin with bonding model, the primary bonding parameters were calculated, and a BBD (Box–Behnken design) test was applied for optimization. The comparison between the simulation and experimental results showed that the relative error in the maximum shear force was within 5%, indicating that the calibrated model can serve as a reliable theoretical reference for the design and optimization of tuber mustard harvesting and cutting equipment. Full article