Dimension Estimation Research Articles

State of Charge Accurate Estimation of Lithium-ion Batteries Based on Augmenting Observation Dimension Estimator Over Wide Temperature Range

State of Charge Accurate Estimation of Lithium-ion Batteries Based on Augmenting Observation Dimension Estimator Over Wide Temperature Range

Interstellar scintillation of sources B0821+394 and B1812+412 according to observations on the LPA LPI radio telescope

The search for long-term variability of compact components of radio sources B0821+394 and B1812+412 over an interval of 10 years was carried out. The LPA LPI radio telescope with an operating frequency of 111 MHz was used for observations. According to our estimates, the characteristic time of variability for both sources is 1.5–2.5 years. It is shown that the observed variability is not related to intrinsic variations in the radiation flux, but is due to refractive scintillation on inhomogeneities of the interstellar medium. From the obtained upper estimates of the apparent angular dimensions of the sources, it follows that the main contribution to the scattering of radio emission is made by turbulent plasma concentrated in sufficiently thin screens, the distance to which does not exceed 300–400 pc.

Conceptual Design of a Novel Autonomous Water Sampling Wing-in-Ground-Effect (WIGE) UAV and Trajectory Tracking Performance Optimization for Obstacle Avoidance

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, which indicates a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively.

Computed tomography versus near-infrared spectroscopy for the assessment of coronary atherosclerosis.

Coronary computed tomography angiography (CCTA) has been proposed as an alternative to intravascular imaging for assessing plaque pathology. We aimed to assess the efficacy of CCTA against near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) in evaluating atheroma burden and composition and for guiding coronary interventions. Seventy patients with a chronic coronary syndrome were recruited and underwent CCTA and NIRS-IVUS. The imaging data were matched, and the estimations of lumen, vessel wall and plaque dimensions and composition of the two modalities were compared. The primary endpoint of the study was the efficacy of CCTA in detecting lipid-rich plaques identified by NIRS-IVUS. Secondary endpoints included the performance of CCTA in evaluating coronary artery pathology in the studied segments and its value in stent sizing, using NIRS-IVUS as the reference standard. In total, 186 vessels were analysed. The attenuated plaque volume on CCTA had weak accuracy in detecting lipid-rich plaques (58%; p=0.029). Compared to NIRS-IVUS, CCTA underestimated the lumen volume (309.2 mm3 vs 420.4 mm3; p=0.001) and plaque dimensions (total atheroma volume 116.1 mm3 vs 292.8 mm3; p<0.001 and percentage atheroma volume 27.67% vs 41.06%; p<0.001) and overestimated the lipid component (lipid core burden index 48.6 vs 33.8; p=0.007). In the 86 lesions considered for revascularisation, CCTA underestimated the reference vessel area (8.16 mm2 vs 12.30 mm2; p<0.001) and overestimated the lesion length (23.5 mm vs 19.0 mm; p=0.029) compared to NIRS-IVUS. CCTA has limited efficacy in assessing plaque composition and quantifying lumen and plaque dimensions and tissue types, which may potentially impact revascularisation planning.

The Fertility Assumptions for the Population Projection of the Czech Republic of the Czech Statistical Office from 2023

This paper introduces the methodology used to estimate the fertility parameters for the population projection of the Czech Republic for 2023–2050. Elements of methodology discussed here include the assumptions, input and output data, and the details of the computations and estimations. The analysis focus on the birth order dimension, cohort perspective, and high and low estimation variants. The paper further compares the estimated summary fertility indicators with past projections by the Czech Statistical Office, as well as with recent projections by other agencies.

Enhancing River Channel Dimension Estimation: A Machine Learning Approach Leveraging the National Water Model, Hydrographic Networks, and Landscape Characteristics

AbstractKnowledge of bankfull hydraulic geometry represents an essential requirement for various applications, including accurate flood prediction, hydrological routing, river behavior analysis, river management and engineering practices, water resource management, and beyond. Our work builds upon an extensive body of literature about estimating bankfull top‐width and depth at ungauged locations to enhance the understanding of observable factors that affect these parameters. Using more than 200,000 USGS Acoustic Doppler Current Profiler (ADCP) records, we developed a method employing machine learning (ML) using discharge estimates and landscape characteristics from sources, including the National Water Model (NWM), the National Hydrologic Geospatial Fabric network (NHGF), the EPA stream characteristic data set (StreamCat), and an array of satellite and reanalysis data products. Our method achieved log‐transformed R2 = 0.8 predicting bankfull depth (R2 = 0.77 for in‐channel conditions) and R2 = 0.76 predicting bankfull top‐width (R2 = 0.66 for in‐channel conditions) in the testing data set. The depth and width predictions showed lowest skill in mountainous and plateau regions. Our analysis demonstrates the benefit of data‐driven modeling in contrast to other global scaling‐based or regional statistical methods. In summary, our study illustrates how top‐width and depth can be better predicted using ML, reanalysis streamflow simulations, hydrographic networks, and summarized geospatial data.

Correlative SECCM and AFM Microscopy Study of Local Electrochemical Activity in HOPG

Highly-oriented pyrolytic graphite (HOPG) is a favored substrate in scanning probe microscopy (SPM) research due to its flatness, uniformity,and structured layers. Extensively employed as a model electrode substrate, HOPG facilitates the development and optimization of electrocatalytic materials and reactionsthat are crucial in driving energy conversion processes.1-4 HOPG's exceptional structural organization and electrical conductivity position it as an ideal substrate for investigating electrocatalysis at the atomic and molecular scale. At the microscopic level, the basal plane and edges of the HOPG surface reveal heterogeneous electro- and electrocatalytic traits owing to variances in their electronic and structural attributes.5 Scanning electrochemical cell microscopy (SECCM) is a valuable technique for investigating behavior at atomic-scale structural features (such as defects and step edges) and their electrochemical activities within two-dimensional (2D) materials.6,7,8,9 SECCM allows for localized cyclic voltammetry (CV) measurements on with higher spatial resolution. This is achieved by using a small meniscus formed at the pipette tip as both a probe and an electrochemical cell. This confined volume enables precise delivery of reactants to a specific location on the surface. Previously, maps of electrochemical activity measured by SECCM were co-localized with AFM topography to identify the step edge from the basal plane with higher electrochemical activity on HOPG.8 SECCM leaves behind electrolyte residue “footprints” that indicate the size of the electrode formed by individual electrolyte contact events on the surface of 2D material. Atomic force microscopy (AFM) topography enables precise estimation of footprint dimensions. These footprints vary due to differences in electrolyte dispersity resulting from hydrophobic or hydrophilic interactions and surface geometry.10 This study demonstrates the qualitative investigation of the heterogeneity in the electrochemical activities of HOPG using correlative SECCM and AFM microscopy. Estimation of the electrode for each cyclic voltammetry from SECCM allows for accurate current density calculations, improving standardization in electrochemical analysis. Understanding the variation in footprints assists in the selective quantification and qualification of SECCM data from a metrological standpoint. This highlights the importance of not simply assuming that footprints are identical in size to pipette tips. More details will be discussed in the poster.References Banerjee, S.; Sardar, M.; Gayathri, N.; Tyagi, A.K.; and Raj, B., Rev. B, 2005, 72, 075418.Ma, H.; Lee, L.; Brooksby, P.A.; Brown, S.A.; Fraser, S.J.; Gordon, K.C.; Leroux, Y.R.; Hapiot, P.; Downard, A. J., Phys. Chem. C, 2014, 118(11), 5820-5826.Pham, K. D.; Hieu, N.N.; Phuc, H.V.; Fedorov, I.A.; Duque, C.A.; Amin, B.; Nguyen, C.V., Phys. Lett. 2018, 113, 171605.Tao, L.; Qiao,M.; Jin, R.; Li, Y.; Xiao, Z.; Wang, Y.; Zhang, N.; Xie, C.; He, Q.; Jiang, D.; Yu, G.; Li, Y.; Wang, Sh., Chem. Int. Ed. 2019, 58, 1-7.Jaouen, K.; Henrotte, O.; Campidelli, S.; Jousselme, B.; Derycke, V.; Cornut, R., Mater. Today, 2017, 8, 116-124.Yu, Yun, et al., Nature Chem. 2022, 14(3), 267-273.Snowden, M.E.; Güell, A.G.; Lai, S. C. S.; McKelvey, K.; Ebejer, N.; O’Connell, M.A.; Colburn, A.W.; Unwin, P.R., Chem., 2012, 84(5), 2483-2491.Patel, A.N.; Collignon, M.G.; O’Connell, M.A.; Hung, W.O.Y.; McKelvey, K.; Macpherson, J.V.; Unwin, P.R., Am. Chem. Soc., 2012, 134(49), 20117-20130.Lai, S.C.S., Patel, A.N., McKelvey, K. and Unwin, P.R., Chem. Int. Ed., 2012, 51, 5405-5408.Cabré, Marc Brunet, et al., Phys. Chem. C, 2022, 126(28), 11636-11641.Soojin Jeong, Myung-Hoon Choi, Gargi S. Jagdale, Yaxu Zhong, Natasha P. Siepser, Yi Wang, Xun Zhan, Lane A. Baker, and Xingchen Ye, A. C. S., 2022, 144 (28), 12673-12680.

Robust Bayesian causal estimation for causal inference in medical diagnosis

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a regressional framework, we assign a treatment and outcome model to estimate the average causal effect. Additionally, for high dimensional regression problems, variable selection methods are also used to find a subset of predictor variables that maximises the predictive performance of the underlying model for better estimation of the causal effect. In this paper, we propose a different approach. We focus on the variable selection aspects of high dimensional causal estimation problem. We suggest a cautious Bayesian group LASSO (least absolute shrinkage and selection operator) framework for variable selection using prior sensitivity analysis. We argue that in some cases, abstaining from selecting (or, rejecting) a predictor is beneficial and we should gather more information to obtain a more decisive result. We also show that for problems with very limited information, expert elicited variable selection can give us a more stable causal effect estimation as it avoids overfitting. Lastly, we carry a comparative study with synthetic dataset and show the applicability of our method in real-life situations.

Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks

Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance in many fields. However, many data such as natural images usually do not populate the ambient Euclidean space but instead reside in a lower-dimensional manifold. Thus an inappropriate choice of the latent dimension fails to uncover the structure of the data, possibly resulting in mismatch of latent representations and poor generative qualities. Toward addressing these problems, we propose a novel framework called the latent Wasserstein GAN (LWGAN) that fuses the Wasserstein auto-encoder and the Wasserstein GAN so that the intrinsic dimension of the data manifold can be adaptively learned by a modified informative latent distribution. We prove that there exist an encoder network and a generator network in such a way that the intrinsic dimension of the learned encoding distribution is equal to the dimension of the data manifold. We theoretically establish that our estimated intrinsic dimension is a consistent estimate of the true dimension of the data manifold. Meanwhile, we provide an upper bound on the generalization error of LWGAN, implying that we force the synthetic data distribution to be similar to the real data distribution from a population perspective. Comprehensive empirical experiments verify our framework and show that LWGAN is able to identify the correct intrinsic dimension under several scenarios, and simultaneously generate high-quality synthetic data by sampling from the learned latent distribution. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Gorenstein projective, injective, flat modules and dimensions over polynomial rings

We investigate Gorenstein projective, injective, flat modules and dimensions over a polynomial ring using the equivalent category of the module category over the polynomial ring. Several estimates of Gorenstein homological dimensions of modules over the polynomial ring are obtained.

Dimension estimates for badly approximable affine forms

Abstract For given $\epsilon&gt;0$ and $b\in \mathbb {R}^m$ , we say that a real $m\times n$ matrix A is $\epsilon $ -badly approximable for the target b if $$ \begin{align*}\liminf_{q\in\mathbb{Z}^n, \|q\|\to\infty} \|q\|^n \langle Aq-b\rangle^m \geq \epsilon,\end{align*} $$ where $\langle \cdot \rangle $ denotes the distance from the nearest integral vector. In this article, we obtain upper bounds for the Hausdorff dimensions of the set of $\epsilon $ -badly approximable matrices for fixed target b and the set of $\epsilon $ -badly approximable targets for fixed matrix A. Moreover, we give a Diophantine condition of A equivalent to the full Hausdorff dimension of the set of $\epsilon $ -badly approximable targets for fixed A. The upper bounds are established by effectivizing entropy rigidity in homogeneous dynamics, which is of independent interest. For the A-fixed case, our method also works for the weighted setting where the supremum norms are replaced by certain weighted quasinorms.

Artificial Intelligence-Based Segmentation and Classification of Plant Images with Missing Parts and Fractal Dimension Estimation

Existing research on image-based plant classification has demonstrated high performance using artificial intelligence algorithms. However, limited camera viewing angles can cause parts of the plant to be invisible in the acquired images, leading to an inaccurate classification. However, this issue has not been addressed by previous research. Hence, our study aims to introduce a method to improve classification performance by taking these limitations into account; specifically, we incorporated both segmentation and classification networks structured as shallow networks to expedite the processing times. The proposed shallow plant segmentation network (Shal-PSN) performs adversarial learning based on a discriminator network; and a shallow plant classification network (Shal-PCN) with applied residual connections was also implemented. Moreover, the fractal dimension estimation is used in this study for analyzing the segmentation results. Additionally, this study evaluated the performance of the proposed Shal-PSN that achieved the dice scores (DSs) of 87.43% and 85.71% with PlantVillage and open leaf image (OLID-I) open datasets, respectively, in instances where 40–60% of plant parts were missing. Moreover, the results demonstrate that the proposed method increased the classification accuracy from 41.16% to 90.51% in the same instances. Overall, our approach achieved superior performance compared to the existing state-of-the-art classification methods.

Fractal dimension analysis of lightning discharges of various types based on a comprehensive literature review

In this study, photographic records of natural lightning flashes obtained from high-speed video camera observations are analyzed based on an extensive literature review. The purpose of this work is to estimate the fractal dimension of lightning discharges and to analyze and evaluate the effect of lightning type (downward and upward flashes during their propagation as well as return strokes) and lightning polarity (negative and positive). Three methods of fractal dimension estimation are employed namely the: (i) box-counting, (ii) sandbox, and (iii) correlation method, utilizing algorithms developed in MATLAB software. Appropriate image processing techniques are employed to guarantee precision in fractal dimension estimation. A thorough discussion is conducted by comparing the estimated fractal dimension values with those previously documented in the relevant literature based on field observations. The mean fractal dimension of downward negative leaders for the three methods (1.18, 1.31, 1.38) aligns well with previous studies, while positive downward leaders exhibit lower values (1.08, 1.15, 1.26), denoting a reduced branching behavior; upward leaders demonstrate slightly higher fractal dimensions than downward ones. Additionally, an attempt to correlate the lightning return stroke peak current with the fractal dimension is made. The outcomes of this research may assist in facilitating precise modeling of the lightning attachment phenomenon, thereby contributing to the development of safer lightning protection systems.

Correlation dimension for temperature and other micrometeorological variables in different ground cover from six biomes of the Amazon basin

Correlation dimension () for embedding dimensions, varying between 3 to 11, were estimated for a 1- or 2-month time series of air temperature and other micrometeorological variables, such as energy fluxes, solar radiation and concentration, in six ecosystems in Amazon and the central region of Brazil. Dc remained within the 1.5 - 3.0 range, for variables locally coupled to ecosystems, and outside the range for variables not locally coupled, such as precipitation and wind velocity. Results indicate that the feature of the variables does not have an important stochastic component. There is a small amount of data available concerning estimates of correlation dimension () of micrometeorological variables. The measurement of for several variables from six Brazilian biomes indicates that this parameter is sensitive to environmental conditions as the rainfall. The obtained results also indicates that the natural processes in the ecosystems remain in a non-chaotic regime. In certain circumstances, as in conditions of high humidity, natural ecosystems may stay inside a limit which is close but lower than chaos. Moreover, an important stochastic component was not observed.

Human Pose Classification Based on Pose Long Short-Term Memory

Objective: At present, pose classification research is a hot topic in the field of computer vision, playing a crucial role in surveillance security, and motion data mining. The research serves as a crucial follow-up to three dimensional (3D) pose estimation and makes a significant contribution to the advancement of artificial intelligence. Pose classification is a complex problem involving a large amount of complicated data, making its digital modeling and further processing challenging. Methods: This article proposes the Pose long short - term memory (LSTM) method to classify 3D poses reconstructed from 2D image sequences. Result: Compared with traditional methods, the experimental tests show that the performance of the Pose LSTM is more superior in all aspects. Conclusion: Pose LSTM is particularly effective for recognizing continuous action poses because they can capture temporal dependencies in sequential data. In the context of pose recognition, it can analyze the sequence of poses over time and understand how these poses transition from one to another, making it possible to accurately classify or predict actions that unfold over a series of frames.

Single radius spherical cap discrepancy on compact two-point homogeneous spaces

In this note we study estimates from below of the single radius spherical discrepancy in the setting of compact two-point homogeneous spaces. Namely, given a d-dimensional manifold M\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {M}$$\\end{document} endowed with a distance ρ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho $$\\end{document} so that (M,ρ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\mathcal {M}, \\rho )$$\\end{document} is a two-point homogeneous space and with the Riemannian measure μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu $$\\end{document}, we provide conditions on r such that if Dr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$D_r$$\\end{document} denotes the discrepancy of the ball of radius r, then, for an absolute constant C>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C>0$$\\end{document} and for every set of points {xj}j=1N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\{x_j\\}_{j=1}^N$$\\end{document}, one has ∫M|Dr(x)|2dμ(x)⩾CN-1-1d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\int _{\\mathcal {M}} |D_{r}(x)|^2\\, d\\mu (x)\\geqslant C N^{-1-\\frac{1}{d}}$$\\end{document}. The conditions on r that we have depend on the dimension d of the manifold and cannot be achieved when d≡1(mod4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d \\equiv 1 \\ ( {\ ext {mod}}4)$$\\end{document}. Nonetheless, we prove a weaker estimate for such dimensions as well.

Modelling multivariate spatio-temporal data with identifiable variational autoencoders

Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they can be modelled univariately. Blind source separation aims to recover the latent components by estimating the unknown linear or nonlinear unmixing transformation based on the observed data only. In this paper, we extend recently introduced identifiable variational autoencoder to the nonlinear nonstationary spatio-temporal blind source separation setting and demonstrate its performance using comprehensive simulation studies. Additionally, we introduce two alternative methods for the latent dimension estimation, which is a crucial task in order to obtain the correct latent representation. Finally, we illustrate the proposed methods using a meteorological application, where we estimate the latent dimension and the latent components, interpret the components, and show how nonstationarity can be accounted and prediction accuracy can be improved by using the proposed nonlinear blind source separation method as a preprocessing method.

Conversion of a piston–cylinder dimensional dataset to the effective area of a mechanical pressure generator

Recent developments in diameter metrology at NIST have improved the dimensional characterization of piston-cylinder assemblies (PCAs) to unprecedented precision. For the newest generation of PCAs, the standard uncertainty in the measurement of the outer diameter is 12 nm, while uncertainty in the measurement of the inner diameter is 14 nm. With a high-accuracy dimensional dataset in hand, the task of determining the pressure generated by a specific PCA is reduced to converting the diameter (and straightness and roundness) to an effective area (and distortion coefficient). The details on how this was performed for the artifact PCA2062 are described. PCA2062 was dimensioned in 2017 and 2020; the area repeated within . The calculation produced estimates of fall rate and rotation decay that agreed with experimental observations within 12 %. The fall rate is proportional to the square of the gap width; therefore, the agreement between calculation and measurement validates the dimensional estimate of the gap width within (36 ± 42) nm, where the 42 nm standard uncertainty is governed by the present state of flow theory. The piston gage model is buttressed by three comparison tests against a laser barometer, which support a view that PCA2062 is linear and reproducible within 0.2 μPa Pa-1. Finally, an estimate of uncertainty in the effective area of a dimensioned artifact is provided: as expected, the diameter measurement is the main culprit, but there are open questions regarding the flow model that preclude an accurate evaluation of the distortion coefficient. For the 530 kPa operating range of PCA2062, distortion is not a significant problem, but the effect would be dominant in assemblies operating at 1 MPa and above.

Development of automatic audio panning system for immersive sound through stage size-aware model and object-tracking

We investigated a novel AI-assisted automated 'immersive' audio panning system designed to track audio-related objects within a video clip. This system comprises four sequential steps: Object-Tracking, Stage dimension Estimation, XY-Coordinate Calculation, and Object Audio Rendering. The system is designed to overcome existing challenges arising from the rapid and frequent movement of target objects by employing a pre-trained object-tracking model and integrating depth information to ensure stability in subsequent tasks. Additionally, we introduce a stage size-aware model to extrapolate stage dimension using our manually collected dataset, formatted as (Image, Width, Depth), which facilitates model training. Consequently, the system calculates XY-Coordinate pairs, serving as panning values for conventional audio mixers or decoders to enable immersive audio reproduction. We anticipate that this video- and space-aware automatic panning system will be valuable for the rapid production of new media.

Shear Mechanism and Optimal Estimation of the Fractal Dimension of Glass Bead-Simulated Sand

Spherical glass beads weaken the influences of particle morphology, surface properties, and microscopic fabric on shear strength, which is significant for revealing the relationship between macroscopic particle friction mechanisms and the particle size distribution of sand. This paper explores the shear mechanical properties of glass beads with different particle size ratios under different confining pressures. It obtains the particle size ratio and fractal dimension D through an optimal mechanical response. Simultaneously, we explore the range of the fractal dimension D under well-graded conditions. The test results show that the strain-softening degree of Rs is more obvious under a highly effective confining pressure, and the strain-softening degree of Rs can reach 0.669 when the average particle size d¯ is 0.5 mm. The changes in the normalized modulus ratio Eu/Eu50 indicate that the particle ratio and arrangement are the fundamental reasons for the different macroscopic shear behaviors of particles. The range of the peak effective internal friction angle φ is 23 °~35 °, and it first increases and then decreases with the increase in the effective confining pressure. As the average particle size increases, the peak stress ratio MFL and the peak effective internal friction angle φ first increase and then decrease, and both can be expressed using the Gaussian function. The range of the fractal dimension D for well-graded particles is 1.873 to 2.612, and the corresponding average particle size d¯ ranges from 0.433 to 0.598. Under the optimal mechanical properties of glass beads, the particle size ratio of 0.25 mm to 0.75 mm is 23:27, and the fractal dimension D is 2.368. The study results provide a reference for exploring friction mechanics mechanisms and the optimal particle size distributions of isotropic sand.