Search Results (80)

The generation of synthetic seismic accelerograms is a critical problem in earthquake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper presents a proof-of-concept wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that reproduce the physical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the proposed architecture decomposes each training signal into N wavelet sub-bands (experimentally $N=7$, six detail sub-bands D1–D6 and one approximation sub-band A6) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme ${\alpha }_i$ modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude $M_w$ serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python v3.9 using PyTorch v.2.10 and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Preliminary evaluation on 500 synthetic accelerograms across five magnitude classes provides evidence that the proposed wavelet-domain multi-discriminator scheme reproduces the essential spectral shape and non-stationary temporal structure of real ground-motion records within the considered magnitude range; full quantitative validation on a larger and more diverse corpus, rigorous comparison with competing methods, and extended multi-parameter conditioning are identified as the principal avenues for future work. Full article

Meaningful partitioning of 2D binary shapes remains a challenging problem in shape analysis because many existing methods rely mainly on local geometric rules or skeleton simplification, which often struggle to separate the main body of a shape from its protruding parts in a perceptually meaningful way. This limitation becomes more evident in shapes with thin limbs, branching structures, or irregular extensions, where preserving topology while achieving human-consistent decomposition is difficult. We present a fully automatic framework for the hierarchical partitioning of 2D binary shapes into semantically meaningful core bodies and protruding limbs (tails). The pipeline begins by generating candidate structural lines through multi-directional centroid tracking along horizontal, vertical, and diagonal (±45°) bands. Three direction-specific Sugeno fuzzy controllers first evaluate these lines based on normalized length, angular alignment, and minimum distance to the boundary. A second pair of fuzzy systems then classifies segments as either tails or core parts using thickness statistics derived from the distance transform. For ambiguous merged tail groups, iterative midpoint splitting is applied until stable labeling is achieved. High-curvature boundary corners are then detected via signed turning-angle analysis, and candidate cutting rays are assessed through exact region splitting, tail area measurement, and label purity analysis. An adaptive third-stage fuzzy controller ranks these candidates according to cut length, purity, and area. The highest-scoring non-overlapping cuts are executed iteratively, progressively peeling peripheral parts while preserving the overall topology and symmetry of the shape. The proposed framework is evaluated on a targeted subset of 32 categories from the 2D Shape Structure Dataset Results on this evaluated subset indicate that the method produces coherent and topologically consistent partitions, with competitive agreement with the available human-annotated references. This training-free framework provides an interpretable tool for 2D shape analysis, with potential applications in object recognition, computer animation, and symmetry studies. Full article

Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity prediction is formulated as a supervised regression problem, and six widely-used tree ensemble models (Random Forest, XGBoost, CatBoost, LightGBM, Extra Trees, and Decision Tree) are systematically evaluated using large-scale Spotify data. Among these models, Random Forest achieves the best predictive performance on this dataset (RMSE = 6.79, MAE = 5.10, and $R^2$ = 0.6658), followed by Extra Trees ($R^2$ = 0.6378) and Decision Tree ($R^2$ = 0.6328). Bayesian hyperparameter optimization based on a Tree-structured Parzen Estimator with an Expected Improvement acquisition function is conducted over 50 trials with 5-fold cross-validation to ensure robust model selection. Shapley value decomposition via SHAP analysis reveals that temporal recency dominates feature importance, far surpassing traditional musical attributes, while acoustic intensity (loudness) exhibits a U-shaped contribution pattern with optimal values at moderate intensity levels. Further SHAP dependence analysis uncovers non-linear relationships, indicating substantial popularity advantages for recent releases and optimal loudness levels around $-5$ to 0 dB. These findings suggest that streaming popularity is primarily governed by temporal exposure dynamics and production-related characteristics rather than intrinsic musical structure, offering both theoretical insights for music information retrieval research and suggestive empirical patterns that may inform future investigations into digital music ecosystems. Full article

Variance-driven decomposition based on the singular spectrum analysis of the European Project for Ice Coring in Antarctica (EPICA) δD, CO₂, and CH₄ records allowed a novel quantitative structural interpretation of all glacial/interglacial cycles and glacial terminations of the last 800 kyr. This bottom-up approach used the response components of EPICA stacked records to reconstruct the envelope of the thermal response through a physical interference model. The aim was to improve understanding of the intensity, amplitude, and asymmetry features of 73 marine isotope stages/substages (MISs) and seven glacial terminations. The Antarctic stack record can be described by a variance-weighted superposition of ten thermal waves of different origins (mid-term oscillation, orbitals, and suborbitals) that stochastically interfere at a given time according to their relative differences in frequency, amplitude, and polarity. Interglacial/glacial stages resulted from constructive interference and bipolar amplification of warming/cooling responses, respectively. The low-intensity MISs (including 90% of substages) and the unbiased-dated terminations fell in the low-interference regions, where dominant destructive patterns minimize the thermal envelope. The positive skewness of the EPICA stack resulted from constructive interference with a strong bias in the warming direction, especially after the Mid-Brunhes Event. Duration analysis of short eccentricity hemicycles exhibited an intrinsic unexpectedly prolonged mean cooling in the nominal solution (5.8 kyr) and its EPICA response as well (8.6 kyr), along with an interference-induced asymmetry (21.1 kyr). The overall effect has led to the saw-tooth shape of glacial cycles, which was strongly induced by interference. Full article

We investigated the properties of boxy/peanut-shaped (B/PS) bulges in a sample of 71 galaxies from the Edge-on Galaxies in the Pan-STARRS Survey (EGIPS) and 20 simulated galaxies from Illustris TNG50 using multicomponent photometric decomposition. For each real and simulated galaxy, we obtained a suitable photometric model in which the B/PS bulge was represented by a dedicated 2D photometric function. For real galaxies, we found that more flattened X-structures are generally residing in larger B/PS bulges. When tested against the galaxy masses, we verified that both larger bulges and more flattened X-structures are typically found in more massive galaxies. Since large bars are also known to reside in more massive galaxies, we conclude that the flatness of X-structures in larger B/PS bulges has a physical origin, rather than being solely a result of projection effects due to differences in observed bar viewing angles. When comparing the properties of B/PS bulges between EGIPS galaxies and TNG50 galaxies, with bars rotated for different viewing angles, we found that B/PS bulges in TNG50 are considerably smaller and less luminous in terms of total intensity. This is consistent with previous studies of bar properties in TNG50, indicating the B/PS bulges in TNG50 differ from those in real galaxies, as do their parent bars. Full article

This technical note presents a quantitative image-based framework for evaluating the packing and compressibility of granular soils, specifically applied to building foundation design in civil infrastructure projects. The Sedimaging system replicates hydraulic sedimentation in a controlled column, equipped with a high-resolution camera, to visualize particle orientation after deposition. Grayscale images of the settled bed are analyzed using Haar Wavelet Transform (HWT) decomposition to quantify directional intensity gradients. A new descriptor, termed the sediment index (B), is defined as the ratio of vertical to horizontal wavelet energy at the dominant scale, representing the preferential alignment and anisotropy of particles during sedimentation. Experimental investigations were conducted on fifteen granular materials that include natural sands, tailings, glass beads and rice grains with different shapes. The results demonstrate strong correlations between B and both microscopic shape ratios (d₁/d₂ and d₁/d₃) and macroscopic properties. Linear relationships predict the limiting void ratios (e_max, e_min) with mean absolute differences of 0.04 and 0.03, respectively. A power-law function relates B to the compression index (C_c) with an average deviation of 0.02. These findings confirm that the sediment index effectively captures the morphological influence of particle shape on soil packing and compressibility. Compared with conventional physical testing, the Sedimaging-based approach offers a rapid, non-destructive, and high-throughput solution for estimating soil packing and compressibility of cohesionless, sand-sized granular soils directly from post-settlement imagery, making it particularly valuable for preliminary site assessments, geotechnical screening, and intelligent monitoring of granular materials in building foundation design and other infrastructure applications, such as earth–rock dams. Full article

Aiming at the challenges of high dimensionality in both design variables and optimization objectives, along with high computational resource consumption in the multi-disciplinary optimization of aerodynamic and stealth performance for an unmanned aerial vehicle (UAV) S-shaped inlet, this paper proposes a multi-objective optimization method that integrates design variable dimensionality reduction and a Co-Kriging multi-fidelity surrogate model. First, the S-shape inlet was defined by utilizing parametric modeling with a total of 11 design variables. Simulations were performed to obtain a subset of samples, and Sobol’ sensitivity analysis was applied to eliminate parameters with minor influence on performance, thereby achieving design variable dimensionality reduction. Subsequently, a Co-Kriging surrogate model was constructed. Based on the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm, multi-objective optimization was carried out with the total pressure recovery coefficient, total pressure distortion coefficient, and the average forward radar cross-section (RCS) as the optimization objectives, yielding a Pareto front solution set. Finally, three optimized inlets were selected from the Pareto front and compared with the original inlet to evaluate their aerodynamic and stealth performance. The results demonstrate that the proposed optimization method balances efficiency and accuracy effectively, significantly increasing the total pressure recovery coefficient while markedly reducing the total pressure distortion coefficient and RCS of the optimized inlet. Full article

Partitioning rectangular and rectilinear shapes in n-dimensional binary images into the smallest set of axis-aligned n-cuboids is a fundamental problem in image analysis, pattern recognition, and computational geometry, with applications in object detection, shape simplification, and data compression. This paper introduces and evaluates four deterministic decomposition methods: pure greedy selection, greedy with backtracking, greedy with a priority queue, and an iterative integer linear programming (IILP) approach. These methods are benchmarked against three established baseline techniques across 13 diverse 1D–4D images (up to 8 × 8 × 8 × 8 elements), featuring holes, concavities, and varying orientations. Surprisingly, the simplest approach—a purely greedy heuristic selecting the largest unvisited region at each step—consistently achieved optimal or near-optimal decompositions, even for complex images, and maintained optimality under rotation without post-processing. By contrast, the more sophisticated methods (backtracking, prioritization, and IILP) exhibited trade-offs between speed and quality, with IILP adding overhead without superior results. Runtime testing showed IILP was on average ~37× slower than the fastest greedy method (ranging from ~3× to 100× slower). These findings highlight that a well-designed greedy strategy can outperform more complex algorithms for practical binary shape decomposition, offering a compelling balance between computational efficiency and solution quality in pattern recognition and image analysis. Full article

Lightning Very-High-Frequency (VHF) radiation source mapping technology represents a pivotal advancement in the study of lightning discharge processes and their underlying physical mechanisms. This paper introduces a novel methodology for reconstructing lightning discharge channels by employing the Multiple Signal Classification (MUSIC) algorithm to estimate the Direction of Arrival (DOA) of lightning VHF radiation sources, specifically tailored for both non-uniform and uniform L-shaped arrays (2D-MUSIC). The proposed approach integrates the Random Sample Consensus (RANSAC) algorithm with 2D-MUSIC, thereby enhancing the precision and robustness of the reconstruction process. Initially, the array data are subjected to denoising via the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Following this, the covariance matrix of the processed array data is decomposed to isolate the signal subspace, which corresponds to the signal components, and the noise subspace, which is orthogonal to the signal components. By exploiting the orthogonality between these subspaces, the method achieves an accurate estimation of the signal incidence direction, thereby facilitating the precise reconstruction of the lightning channel. To validate the feasibility of this method, comprehensive numerical simulations were conducted, revealing remarkable accuracy with elevation and azimuth angle errors both maintained below 1 degree. Furthermore, VHF non-uniform and uniform L-shaped lightning observation systems were established and deployed to analyze real lightning events occurring in 2021 and 2023. The empirical results demonstrate that the proposed method effectively reconstructs lightning channel structures across diverse L-shaped array configurations. This innovative approach significantly augments the capabilities of various broadband VHF arrays in radiation source imaging and makes a substantial contribution to the study of lightning development processes. The findings of this study underscore the potential of the proposed methodology to advance our understanding of lightning dynamics and enhance the accuracy of lightning channel reconstruction. Full article

Spiral arms are a common feature of local galaxies, but the exact form of the distribution of mass and light in them is not well known. In this work, we aim to measure this distribution as accurately as possible, focusing on individual spiral arms and using the so-called slicing method. The sample consists of 19 well-resolved, viewed face-on spiral galaxies from the S⁴G survey. We work primarily with infrared images at 3.6 μm from the same survey and, secondarily, with ultraviolet data from the GALEX telescope. We derive the properties of the spiral arms step by step, starting from their overall shape, then measuring their brightness profile and width variation along the arm and then examining the fine structure of the profile across the arm, namely, its skewness and Sérsic index. We construct a 2D photometric function of the spiral arm that can be used in further decomposition studies, validate it and identify the most and least important parameters. Finally, we show how our results can be used to unravel the nature of the spiral arms, supporting the evidence that NGC 4535 has a density wave in its disc. Full article

Digital Holographic Microscopy (DHM) is a method of converting hologram images into three-dimensional (3D) images by image processing, which enables us to obtain the detailed shapes of the objects to be observed. Three-dimensional imaging of the microscopic objects by DHM can contribute to the early diagnosis and the detection of the diseases in the medical field by observing the shape of the cells. DHM requires several experimental components. One of them is the laser, which is a problem because its high power may cause the deformation and the destruction of the cells and the death of the microorganisms. Since the greatest advantage of DHM is the detailed geometrical information of the object by 3D measurement, the loss of such information is a serious problem. To solve this problem, a Neutral Density (ND) filter has been used to reduce power after the laser irradiation. However, the image acquired by the image sensor becomes too dark to obtain sufficient information, and the effect of noise increased due to the decrease in the amount of light. Therefore, in this paper, we propose the Frame-Stacking Method (FSM) for dark DHM for reproducing 3D profiles that enable us to observe the shape of the objects from the images taken in low-power environments when the power is reduced. The proposed method realizes highly accurate 3D profiles by the frame decomposition of the low-power videos into images and superimposing and rescaling the obtained low-power images. On the other hand, the continuous irradiation of the laser beam for a long period may destroy the shape of the cells and the death of the microorganisms. Therefore, we conducted experiments to investigate the relationship between the number of superimposed images corresponding to the irradiation time and the 3D profile, as well as the characteristics of the power and the 3D profile. Full article

The imperative for sustainable development demands innovative solutions to address the environmental impact of transportation, particularly in the context of climate change. This study explores the intersection of renewable energy and sustainability within the automotive industry by examining how restrictions on the sale of gasoline-powered vehicles affect patent filings related to automotive engine technologies. Our approach employs a factorial analysis to capture structural shifts in patent filings, utilizing the logarithmic mean Divisia index for index decomposition. We scrutinize patent trends from 1985 to 2019, with a focus on both non-green (internal combustion engine vehicles) and green technologies (battery electric, hybrid electric, and full cell vehicles), to assess the transition towards sustainable transportation. This study also scrutinizes the patenting activities of four major patent offices—China, Japan, the United States, and Germany—to unveil global trends and disparities in sustainable technology innovation. Our findings underscore how a nation’s green investment strategy is contingent upon its developmental stage, with intellectual property rights and R&D incentives playing pivotal roles in shaping R&D landscapes, especially in emerging economies with nascent intellectual property markets. This study also reveals varying strategies for developing green automotive engines across nations, indicating that the growth of green patents in developed countries is probably spurred by financial incentives and enhanced intellectual property rights to promote specific tech advancements. This research contributes to the discourse on sustainability by highlighting the critical role of policy in fostering green technology development and the importance of aligning patent strategies with environmental goals. Full article

A block-based mode decomposition (BMD) algorithm is proposed in this paper, which reduces computational complexity and enhances noise resistance. The BMD uses randomly selected sample blocks of the beam images to restore mode coefficients instead of all pixels in the beam images. It allows for blocks of any shape, such as triangles. In noise-free simulations, compared to the spatially degenerated mode decomposition (SPMD) algorithm, the BMD algorithm requires only 1% of the multiplication operations, thereby significantly increasing the computational efficiency while maintaining a high mode decomposition accuracy. In simulations with noise, increasing the signal-to-noise ratio (SNR) reduces decomposition errors across all configurations. The amplitude error of BMD can outperform SPMD by 15 dB. The experimental results show that BMD has a better performance than SPMD. Full article

The quarrying and utilization of natural stones such as marble and granite are growing rapidly in developing countries. However, the processing, cutting, sizing, and shaping of these stones to render them functional generates huge quantities of waste and dust. These materials are often disposed of openly in the environment, and their potentially hazardous nature has negative repercussions on both the environment and human health. In this study, marble waste (MW) was used as a filler in the unsaturated polyester resin (UPR) matrix to enhance performance and characteristics while adding value to the waste and minimizing manufacturing costs. For this purpose, samples of UPR/MW composites were produced with 0, 5, 10, 15, and 20 wt.% of MW incorporated into the UPR. A full characterization that focused on the microstructure, thermal stability, and physical and mechanical properties was carried out. The results revealed that the use of 10 to 15% of MW improves mechanical performance, with increases from 17 to 26 kJ/m², 14 to 17 MPa, and 794 to 1522 GPa in impact strength, tensile strength, and elastic modulus, respectively. By introducing a 20% MW filler, the composite loses its performance, particularly Shore D hardness, and becomes very brittle. Thermogravimetric analysis (TGA) indicated significant thermal stabilization, with a delay in the start decomposition temperature of 28 °C for 20 UPR/MW compared to 0 UPR/MW. Additionally, morphological and microstructural tests, namely, FT-IR, XRD, and SEM analysis, show a microstructural change, including the formation of crystalline phases, enhancing matrix-filler interactions due to the creation of Mg-O and Ca-O chemical bonds and the forming of filler agglomeration at high introduction rates that lead to defects in the microstructure. These results confirmed the mechanical results of the UPR/MW composites. Full article

The empirical wavelet transform is a fully adaptive time-scale representation that has been widely used in the last decade. Inspired by the empirical mode decomposition, it consists of filter banks based on harmonic mode supports. Recently, it has been generalized to build the filter banks from any generating function using mappings. In practice, the harmonic mode supports can have a low-constrained shape in 2D, leading to numerical difficulties to estimate mappings adapted to the construction of empirical wavelet filters. This work aims to propose an efficient numerical scheme to compute empirical wavelet coefficients using the demons registration algorithm. Results show that the proposed approach is robust, accurate, and continuous wavelet filters permitting reconstruction with a low signal-to-noise ratio. An application for texture segmentation of scanning tunneling microscope images is also presented. Full article