Search Results (166)

This paper presents a computationally efficient two-stage analytical framework for predicting daylight performance in buildings. It is designed to support real-time applications in smart lighting and intelligent building management systems. This approach combines a facade lighting model—driven by solar geometry and atmospheric transmittance—with an interior light distribution module that represents the window as a discretized light source. This formulation provides a lightweight alternative to computationally intensive ray tracing methods. It allows rapid estimation of spatial lighting patterns with minimal input data. The framework is validated using a one-year measurement campaign with class A photometric sensors in three facade orientations. The facade module achieved an average relative error below 15%, while the interior lighting model yielded an RMSE of 83 lx (≈10% error). The integrated system demonstrated an overall average deviation of 18.6% under different sky and season conditions. Owing to its low computational complexity and physically transparent formulation, the proposed method is suitable for deployment in smart building platforms, including daylight-responsive lighting control, embedded energy management systems, and digital twins requiring fast and continuous simulation of daylight availability. Full article

Accurate sky–obstacle segmentation in hemispherical fisheye imagery is essential for solar irradiance forecasting, photovoltaic system design, and environmental monitoring. However, existing methods often rely on expensive all-sky imagers and region-specific training data, produce coarse sky–obstacle boundaries, and ignore the optical properties of fisheye lenses. We propose a low-cost segmentation framework designed for fisheye imagery that combines synthetic data generation, lens-aware augmentation, and a hybrid deep-learning pipeline. Synthetic fisheye training images are created from publicly available street-view panoramas to cover diverse environments without dedicated hardware, and lens-aware augmentations model fisheye projection and photometric effects to improve robustness across devices. On this dataset, we train a convolutional neural network (CNN) and refine its output with gradient-boosted decision trees (GBDT) to sharpen sky–obstacle boundaries. The method is evaluated on real fisheye images captured with smartphones and low-cost clip-on lenses across multiple sites, achieving an Intersection over Union (IoU) of 96.63% and an F1 score of 98.29%, along with high boundary accuracy. An additional evaluation on an external panoramic baseline dataset confirms strong cross-dataset generalization. Together, these results show that the proposed framework enables accurate, low-cost, and widely deployable hemispherical sky segmentation for practical solar and environmental imaging applications. Full article

This study presents multi-band photometric observations and detailed period analysis of a totally eclipsing binary system exhibiting low photometric amplitude. The system exhibits characteristic W Ursae Majoris (EW)-type light curves with complete eclipses. In our light curve modeling, we tested two setups: one excluding third light and the other including it as a free parameter (accounting for a potential tertiary component). Photometric analysis reveals that ASASSN-V J171815.10+450432.9 (hereafter J171815) represents a marginal contact binary system with an extreme mass ratio (the more massive component is designated as the primary star), approaching the theoretical lower limit for stable contact configurations. Furthermore, our investigation of orbital period variations uncovers a long-term period increase at a rate of ${\displaystyle \frac{dP}{dt}}=(1.08\pm 0.05)\times {10}^{-6}\mathrm{day}{\mathrm{yr}}^{-1}$, which is likely attributable to ongoing mass transfer between components. This interpretation aligns with the system’s geometric configuration and observed light curve asymmetries. The unique characteristics presented by this binary system serve as a rare opportunity for in-depth research on the mass ratio theory, and also provide an important opportunity for testing the Thermal Relaxation Oscillation (TRO) theory. Full article

An efficient method for evaluating the spectral irradiance properties of the white light of white LEDs is conducted. The method includes two main steps. The first step is to build up spectral irradiance modeling for the blue and yellow emission bands. The photometric parameter of the spectral irradiance of white light which is generated by yellow and blue light mixing is determined based on the photometry and colorimetry theories. The correlated color temperature value strongly depends on the power ratios of blue and yellow light. In addition, the result indicates that the emission bandwidth of yellow phosphor is also an important factor for increasing the color performance of output light. The selection of material with a broader bandwidth of yellow light can control a slower variation in color property compared to the case of using a material with a narrower bandwidth. In addition, the blue light hazard ratio of the spectral irradiance of white light can be extracted, which is helpful for designing the white light with moderate blue and yellow power ratios before fabricating the white LEDs product. Full article

The three-dimensional documentation of hypogean structures poses significant methodological challenges due to the absence of natural light, confined spaces, and the presence of fragile painted surfaces. This study presents an integrated workflow for the survey of the Tomba dell’Orco (Tarquinia), combining terrestrial laser scanning, photogrammetry, and the light painting technique. Borrowed from photographic practice, light painting was employed as a dynamic lighting strategy during photogrammetric acquisition to overcome issues of uneven illumination and harsh shadows typical of underground environments. By moving handheld LED sources throughout long-exposure shots, operators produced evenly illuminated images suitable for feature extraction and high-resolution texture generation. These image datasets were subsequently integrated with laser scanning point clouds through a structured pipeline encompassing registration, optimization, and texture reprojection, culminating in web dissemination via the ATON framework. The methodological focus demonstrates that light painting provides a scalable and replicable solution for documenting complex hypogean contexts, improving the photometric quality and surface readability of 3D models while reducing acquisition time compared to static lighting setups. The results highlight the potential of dynamic illumination as an operational enhancement for 3D recording workflows in low-light cultural heritage environments. Full article

Plant disease recognition is a critical technology for ensuring food security and advancing precision agriculture. However, challenges such as class imbalance, heterogeneous image quality, and limited model interpretability remain unresolved. In this study, we propose a Synergistic Dual-Augmentation and Class-Aware Hybrid (SDA-CAH) model designed to achieve robust and interpretable recognition of plant diseases. Our approach introduces two innovative augmentation strategies: (1) an optimized MixUp method that dynamically integrates class-specific features to enhance the representation of minority classes; and (2) a customized augmentation pipeline that combines geometric transformations with photometric perturbations to strengthen the model’s resilience against image variability. To address class imbalance, we further design a class-aware hybrid sampling mechanism that incorporates weighted random sampling, effectively improving the learning of underrepresented categories and optimizing feature distribution. Additionally, a Grad-CAM–based visualization module is integrated to explicitly localize lesion regions, thereby enhancing the transparency and trustworthiness of the predictions. We evaluate SDA-CAH on the PlantVillage dataset using a pretrained EfficientNet-B0 as the backbone network. Systematic experiments demonstrate that our model achieves 99.95% accuracy, 99.89% F1-score, and 99.89% recall, outperforming several strong baselines, including an optimized Xception (99.42% accuracy, 99.39% F1-score, 99.41% recall), standard EfficientNet-B0 (99.35%, 99.32%, 99.33%), and MobileNetV2 (95.77%, 94.52%, 94.77%). For practical deployment, we developed a web-based diagnostic system that integrates automated recognition with treatment recommendations, offering user-friendly access for farmers. Experimental evaluations indicate that SDA-CAH outperforms existing approaches in predictive accuracy and simultaneously defines a new paradigm for interpretable and scalable plant disease recognition, paving the way for next-generation precision agriculture. Full article

BL Lacertae is not only archetypical of an entire class of jet-dominated active galactic nuclei, blazars, but also one of the most active and rapidly changing objects in this class. In the fall of 2024 (September–November), BL Lacertae underwent another episode of strong optical activity, reaching an R-band magnitude of about 12 and showing extremely rapid and large-amplitude inter- and intra-night flux and polarization variations. During this period, the object was monitored over 40 nights using telescopes with an aperture of up to 2 m at three observatories: Rozhen and Belogradchik in Bulgaria and Skinakas in Greece. The results from this study include some of the most spectacular intra-night variability episodes detected in a blazar. These rapid variations, combined with high photometric accuracy and high time resolution, allowed for confirmation of consistency between different optical bands with zero time delays, down to a minute scale. Unlike previous activity reports, polarization was relatively stable on these short time-scales. Possible connections between polarization, flux, and intra-night variability were explored in order to better model or constrain the physical processes and emission mechanisms in the relativistic jets. Full article

Background: Rare diseases represent a significant research challenge due to the limited availability of data, small patient cohorts, and heterogeneous phenotypes. Data augmentation and synthetic data generation are increasingly adopted to mitigate these limitations. Methods: This scoping review maps the application of data augmentation and synthetic data generation methods as strategies to address these limitations. A total of 118 studies published between 2018 and 2025 were identified through PubMed, Scopus, and Electronics Engineers (IEEE) Xplore. Results: Imaging data headed the field, followed by clinical and omics datasets. Classical augmentation, mainly geometric and photometric transformations, emerged as the most frequent approach, while deep generative models have rapidly expanded since 2021. Rule- and model-based methods were less common but demonstrated high interpretability in small datasets. Conclusions: Overall, these techniques enabled dataset expansion and improved model robustness. However, both approaches require rigorous validation to confirm biological plausibility. Together, these methods can transform data scarcity from a barrier into a driver of methodological innovation, enabling more inclusive rare disease research. Full article

Accurate modeling of non-cooperative space targets remains a significant challenge, particularly under complex illumination conditions. A hybrid virtual–real framework is proposed that integrates photometric compensation, 3D reconstruction, and visibility determination to enhance the robustness and accuracy of monocular-based modeling systems. To overcome the breakdown of the classical photometric constancy assumption under varying illumination, a compensation-based photometric model is formulated and implemented. A point cloud–driven virtual space is constructed and refined through Poisson surface reconstruction, enabling per-pixel depth, normal, and visibility information to be efficiently extracted via GPU-accelerated rendering. An illumination-aware visibility model further distinguishes self-occluded and shadowed regions, allowing for selective pixel usage during photometric optimization, while motion parameter estimation is stabilized by analyzing angular velocity precession. Experiments conducted on both Unity3D-based simulations and a semi-physical platform with robotic hardware and a sunlight simulator demonstrate that the proposed method consistently outperforms conventional feature-based and direct SLAM approaches in trajectory accuracy and 3D reconstruction quality. These results highlight the effectiveness and practical significance of incorporating virtual space feedback for non-cooperative space target modeling. Full article

Digital twins are essential for the real-time health management and monitoring of aeroengines, and the in situ three-dimensional (3D) reconstruction technology of key components of aeroengines is an important support for the construction of a digital twin model. In this paper, an in situ high-fidelity 3D reconstruction method, named PoseNeRF, for aeroengine blades based on the joint optimization of pose and neural radiance field (NeRF), is proposed. An aeroengine blades background filtering network based on complex network theory (ComBFNet) is designed to filter out the useless background information contained in the two-dimensional (2D) images and improve the fidelity of the 3D reconstruction of blades, and the mean intersection over union (mIoU) of the network reaches 95.5%. The joint optimization loss function, including photometric loss, depth loss, and point cloud loss is proposed. The method solves the problems of excessive blurring and aliasing artifacts, caused by factors such as smooth blade surface and weak texture information in 3D reconstruction, as well as the cumulative error problem caused by camera pose pre-estimation. The PSNR, SSIM, and LPIPS of the 3D reconstruction model proposed in this paper reach 25.59, 0.719, and 0.239, respectively, which are superior to other general models. Full article

The integrity of urban water supply pipelines, an essential element of municipal infrastructure, is frequently undermined by internal defects such as corrosion, tuberculation, and foreign matter. Traditional inspection methods relying on CCTV are time-consuming, labor-intensive, and prone to subjective interpretation, which hinders the timely and accurate assessment of pipeline conditions. This study proposes YOLOv8-VSW, a systematically optimized and lightweight model based on YOLOv8 for automated defect detection in in-service pipelines. The framework is twofold: First, to overcome data limitations, a specialized defect dataset was constructed and augmented using photometric transformation, affine transformation, and noise injection. Second, the model architecture was improved on three levels: a VanillaNet backbone was adopted for lightweighting, a C2f-Star module was introduced to enhance multi-scale feature fusion, and the WIoUv3 dynamic loss function was employed to improve robustness under complex imaging conditions. Experimental results demonstrate the superior performance of the proposed YOLOv8-VSW model. This study validates the framework on a curated, real-world image dataset, where YOLOv8-VSW achieved mAP@50 of 83.5%, a 4.0% improvement over the baseline. Concurrently, GFLOPs were reduced by approximately 38.9%, while the inference speed was increased to 603.8 FPS. The findings validate the effectiveness of the proposed method, delivering a solution that effectively balances detection accuracy, computational efficiency, and model size. The results establish a strong technical basis for the intelligent and automated control of safety in urban water supply systems. Full article

Accurate 3D semantic occupancy perception is critical for autonomous driving, enabling robust navigation in unstructured environments. While vision-based methods suffer from depth inaccuracies and lighting sensitivity, LiDAR-based approaches face challenges due to sparse data and dependence on expensive manual annotations. This work proposes LiGaussOcc, a novel self-supervised framework for dense LiDAR-based 3D semantic occupancy prediction. Our method first encodes LiDAR point clouds into voxel features and addresses sparsity via an Empty Voxel Inpainting (EVI) module, refined by an Adaptive Feature Fusion (AFF) module. During training, a Gaussian Primitive from Voxels (GPV) module generates parameters for 3D Gaussian Splatting, enabling efficient rendering of 2D depth and semantic maps. Supervision is achieved through photometric consistency across adjacent camera views and pseudo-labels from vision–language models, eliminating manual 3D annotations. Evaluated on the nuScenes-OpenOccupancy benchmark, LiGaussOcc achieved performance competitive with 30.4% Intersection over Union (IoU) and 14.1% mean Intersection over Union (mIoU). It reached 91.6% of the performance of the fully supervised LiDAR-based L-CONet, while completely eliminating the need for costly and labor-intensive manual 3D annotations. It excelled particularly in static environmental classes, such as drivable surfaces and man-made structures. This work presents a scalable, annotation-free solution for LiDAR-based 3D semantic occupancy perception. Full article

Intelligent autonomous systems in open-pit mining operations face critical challenges in perception and decision-making due to sensor-based visual degradations, particularly lens soiling and sun glare, which significantly compromise the performance and safety of integrated mining automation systems. We propose a comprehensive intelligent framework leveraging single-domain generalization with traditional data augmentation techniques, specifically Photometric Distortion (PD) and Contrast Limited Adaptive Histogram Equalization (CLAHE), integrated within the BiSeNetV1 architecture. Our systematic approach evaluated four state-of-the-art backbones: ResNet-50, MobileNetV2 (Convolutional Neural Networks (CNN)-based), SegFormer-B0, and Twins-PCPVT-S (ViT-based) within an end-to-end autonomous system architecture. The model was trained on clean images from the AutoMine dataset and tested on degraded visual conditions without requiring architectural modifications or additional training data from target domains. ResNet-50 demonstrated superior system robustness with mean Intersection over Union (IoU) of 84.58% for lens soiling and 80.11% for sun glare scenarios, while MobileNetV2 achieved optimal computational efficiency for real-time autonomous systems with 55.0 Frames Per Second (FPS) inference speed while maintaining competitive accuracy (81.54% and 71.65% mIoU respectively). Vision Transformers showed superior stability in system performance but lower overall performance under severe degradations. The proposed intelligent augmentation-based approach maintains high accuracy while preserving real-time computational efficiency, making it suitable for deployment in autonomous mining vehicle systems. Traditional augmentation approaches achieved approximately 30% superior performance compared to advanced GAN-based domain generalization methods, providing a practical solution for robust perception systems without requiring expensive multi-domain training datasets. Full article

Multiwavelength modelling of the light variations in the chemically peculiar star CU Vir is presented. The modelling is based on the recent Doppler Imaging of CU Vir, which provides maps of the surface distribution of Si, Fe, He, and Cr. Intensity maps in both individual photometric filters and in the wide wavelength range from UV to NIR were calculated, taking into account the individual chemical abundances on the stellar surface. Comparison with observations revealed good agreement of both the light curves and their amplitude along the spectrum. Additionally, we analysed changes in the photometric period of the CU Vir from 1955 to 2022, including TESS measurements. The data of the last decades clearly indicate a gradual decrease in this period. Measurements of the CU Vir period over the next two decades will be crucial for verifying or refuting the periodic nature of its variations. Full article

In this study, we present analysis of TESS photometry, spectral energy distribution (SED), high-resolution spectroscopy, and spot modeling of the ${\alpha }^2$ CVn-type star AL Col (HD 46462). The primary objective is to determine its fundamental physical parameters and investigate its surface activity characteristics. Using TESS short-cadence (120 s) SAP flux, we identified a rotational frequency of 0.09655 ${\mathrm{d}}^{-1}$ ($P_{\mathrm{rot}}=10.35733$ d). Wavelet analysis reveals that while the amplitudes of the harmonic components vary over time, the strength of the primary rotational frequency remains stable. A SED analysis of multi-band photometric data yields an effective temperature ($T_{\mathrm{eff}}$) of 11,750 K. High-resolution spectroscopic observations covering wavelengthrange 4500–7000 Å provide refined estimates of $T_{\mathrm{eff}}$ = 13,814 ± 400 K, $logg$ = 4.09 ± 0.08 dex, and $\upsilon sini$ = 16 ± 1 km s⁻¹. Abundance analysis shows solar-like composition of O ii, Mg ii, S ii, and Ca ii, while helium is under-abundant by 0.62 dex. Rare earth elements (REEs) exhibit over-abundances of up to 5.2 dex, classifying the star as an Ap/Bp-type star. AL Col has a radius of $R=3.74\pm 0.48{\mathrm{R}}_{\odot }$, with its H–R diagram position estimating a mass of $M=4.2\pm 0.2{\mathrm{M}}_{\odot }$ and an age of $0.12\pm 0.01$ Gyr, indicating that the star has slightly evolved from the main sequence. The TESS light curves were modeled using a three-evolving-spot configuration, suggesting the presence of differential rotation. This star is a promising candidate for future investigations of magnetic field diagnostics and the vertical stratification of chemical elements in its atmosphere. Full article