Spatial Points Research Articles

Novel Time-Saving Technique to Measure the 2D Complex Profile of Spectral Transmittance of Linearly Variable Filters and Assessing Homogeneity of Optically Transparent Materials

Emerging technologies for coating new types of filters, such as linearly variable bandpass filters, urges new kind of measurement systems requiring accurate and time-saving spatially resolved transmittance measurements. On the other hand, one of the main uses of coated filters is standardization use; e.g., as certified reference materials (CRMs) for transmittance and absorbance standards. In this case, the homogeneity assessment is of great interest. To do such measurements with low uncertainty, spatial scanning methods using reference double-beam spectrophotometers are routinely used. High time consumption and relatively high-cost are disadvantages of this traditional method. In this work, we propose a new system based on an imaging technique. The newly developed system comprises a quartz tungsten halogen (QTH)-based illuminator, with collimator and coupling optics, a double-grating monochromator, customized integrating sphere, and scientific highly digitized, high-resolution and dynamic range Si-based CCD (monochrome) camera. The system is time-saving: in one-shot we may measure the spatial transmittance distribution of the samples (e.g. filters) without separately measuring the reference (air), which has many instability issues. Validation measurements using standard filters are presented. Systematic errors may be studied and corrected for. Many types of filters and liquids, for instance chemical reference materials (RMs), may be measured and reported. Uncertainty of spatial, spectral and radiometric components are studied carefully and estimated. The combined uncertainty evaluation has shown that relatively low uncertainty levels may be achieved, specially in the visible range (U < 1 %, k=2 ). Spatially-resolved data with different curves at different positions (spatial points) are shown in the spectral range of 380 nm through 950 nm. The combined uncertainties at some spectral points are presented and proved to be sample- and wavelength-dependent at all the points in the field-of-view (FOV) of the CCD camera.

Three-dimensional spatial orbital angular momentum holography

The degree of freedom of orbital angular momentum of light has been used as a new information carrier in optical holographic information processing technology. However, current research on orbital angular momentum holography mainly focuses on two-dimensional orbital angular momentum holography, where the reconstructed two-dimensional holographic image is located in a certain plane in three-dimensional space. How to further implement three-dimensional spatial orbital angular momentum holographic technology and use it to increase the information capacity of holographic communication is still a blank. Here, we implement three-dimensional spatial orbital angular momentum holographic technology based on the degrees of freedom of orbital angular momentum and the positional degrees of freedom of reconstructed two-dimensional images in three-dimensional space. In other words, in the three-dimensional spatial orbital angular momentum holography, the acquisition of the target object image requires not only the correct orbital angular momentum state used for decoding, but also the correct spatial position where the object’s image is detected. In addition, we further investigate the three-dimensional spatial orbit angular momentum holographic multiplexing technology and point out that this multiplexing technology can be used for information encryption. Compared with traditional two-dimensional orbital angular momentum holography, three-dimensional spatial orbital angular momentum holography uses an additional degree of freedom. Therefore, the encryption scheme based on three-dimensional spatial orbital angular momentum holographic technology can further improve the security level of information. Our simulation results and experimental results have verified the feasibility of three-dimensional spatial orbit angular momentum holographic technology and three-dimensional spatial orbit angular momentum holographic encryption technology.

Advancing Aircraft Safety through CAx-Enhanced Digitalization and Thermal Stress Testing Methodologies: A Comprehensive Approach

Safety is of utmost importance in air transport, and the quality and durability of aircraft construction materials play a significant role in ensuring overall safety. The right technology and methodology for designing, simulating, and testing aircraft components can simplify the process, digitize components, and utilize non-destructive testing methods to increase safety. This article focuses on the digitization, creation of a 3D model, and testing of a small jet engine, MPM-20. Using a 3D scanner and position markers, the authors created a 3D digital model of the engine and adjusted it to the desired state using computer-aided technologies (CAx). Thermo-spectral analysis was then performed on the real object using a thermal camera and associated software. It was found that the engine’s structural integrity was not compromised by excessive thermal load in the specified spatial points. The methodology used can be applied to a wide range of aircraft components, improving their digitization, modification, and stress-testing.

Numerical Analysis of Low-Cost Recognition of Tunnel Cracks with Compressive Sensing along the Railway

Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage. As engineering safety has always been a very important issue, this study investigated the problem of multi-crack identification in rock masses within the environment of track tunnels using transient waves. A tunnel rock was modeled using MIDAS GTS NX software (2019.v1.2) and a crack transient wave model in the frequency domain was obtained through data analysis and simulation. Then, this was combined with compressive sensing techniques to locate and detect multiple cracks in tunnel rock. The performance of the proposed approach was validated through experimental simulations, which included experiments on differences in the number of cracks, as well as spatial samples. The experimental results indicate that the technique performs well for single-crack localization in tunnel rock mass, where the average localization error is 4 m. Meanwhile, the localization error is larger in multi-crack localization, and the number of spatial sample points set using compressive sensing also has a large impact on the experimental results.

Divertor Thomson Scattering on Globus-M2

We present the first Thomson scattering (TS) measurements of electron temperature and densityin the lower divertor of the Globus-M2 tokamak. The divertor TS diagnostics is designed for local measurementsof the Te(z, t) in the range of 1–100 eV and ne(z, t) in the range of m–3. Parameters of theprobing Nd:YAG laser are as follows 1064 nm/2 J/100 Hz/3 ns. The probing chord is launched vertically atR = 24 cm and covers areas of the inner leg, vicinity of separatrix and private flux region. Along probing chordof 110 mm, 9 spatial points were realized. Advanced filter polychromators were used to analyze Thomsonscattering spectra.

Geo-indistinguishable masking: enhancing privacy protection in spatial point mapping

ABSTRACT Spatial point mapping is a useful practice in exploratory point pattern analysis, but it poses significant privacy risks as the identity of individuals may be revealed from the maps. Geomasking methods have been developed to mitigate the risks by displacing spatial points before mapping. However, many of these methods rely on a weak privacy notion called spatial k-anonymity, which is insufficient to withstand the growing amount of spatial data (e.g. land use) that adversaries can use as side information to infer the actual locations of individuals. We proposes a method called geo-indistinguishable masking to address this issue by relying on a strong privacy notion called geo-indistinguishability. This notion ensures consistent levels of privacy protection regardless of any side information. The method consists of two steps. The first step involves creating a masking area for each spatial point to include a set of candidate locations to which the point can be relocated. In the second step, we formulate an optimization model to ensure the masked locations satisfy geo-indistinguishability while minimizing the distance displaced. Computational experiments on a synthetic dataset demonstrate that our proposed method is both efficient and effective in providing strong privacy protection while preserving the spatial point patterns.

The influence and function of the public art industry on aesthetic education under the background of new curriculum reform

This study takes the decoration of subway murals in the new era as the research entry point, and focuses on the impact of the new era curriculum reform on art education. Then, from the perspective of the artistic connotation and form evaluation of subway murals, it elaborates on the artistic design points of subway murals, and focuses on analyzing the application value of subway murals in enhancing an urban vitality, enhancing urban aesthetic value, inheriting regional culture, and displaying urban image, Furthermore, the overall cultural value of subway murals was analyzed. Finally, starting from the new forms of art education under the influence of public art, this paper explores the practical significance of unleashing students' independent potential and fully integrating with art practice for aesthetic education. After that, it introduces the application of modern technology in public art education, and comprehensively proposes the guiding role of public art in aesthetic education. Research has found that aesthetic education in the new era should follow the social laws of public art development, emphasizing the "combination" of spatial cultural expression and location orientation, the "connection" of spatial cultural layout points, lines, and surfaces, and the "integration" of traditional culture and modern civilization in the education process. By "combining", "connecting", and "integrating", a personalized cultural expression of the site is formed, thereby forming a cultural system for the integrated development of the line and network. In the new era of art education, attention should be paid to regional culture as the soul of urban personality quality. The "environmental characteristic information" contained in it is a design source that stitches the underground and above ground environmental zones, endowing similar spatial structures of subway stations with significant recognition and memory points.

Artificial Neural Networks for Predicting Pressure in High-Viscosity Two-Phase Flow: A Comparative Analysis

This article presents the procedure undertaken to discover the most suitable model for predicting the outlet pressure of a horizontally oriented pipe with high-viscosity two-phase flow. In order to achieve this, a collection of artificial neural networks (ANN) with diverse architectures was developed, utilizing experimental data for training and validation purposes. The data used in this study were obtained from experiments carried out in a 54-meter-long pipeline, where a mixture of air and glycerin was flowing. The various ANN were constructed based on three different combinations of input variables, namely, the air flow rate (Qa), glycerin flow rate (Qg), and pressure measurements at specific spatial points. Subsequently, a statistical analysis was performed, followed by a comparison of the models' performance across different scenarios.

AT-densenet with salp swarm optimization for outlier prediction

In the field of data mining, outlier prediction detects objects that behave differently from normal objects. The conventional density and distance based outlier detection cannot identify the outliers present generally in the stream data. Transfer learning applies the knowledge of Artificial Intelligence to the task of outlier prediction. It makes training faster than earlier stages of the deep learning model. However, the correlations between spatial points are not detected with the Transfer learning based approach. It can be resolved with attention based approach, which focuses on the most informative data points. Hence, this work introduces Transfer learning with an attention mechanism based deep learning model for outlier detection. The attention transfer-DenseNet model is utilized for the outlier prediction. The attention mechanism highlights outlier information in feature maps and enhances the prediction performance. Further, the attention transfer-DenseNet weights are optimized by the metaheuristic algorithm salp swarm optimization algorithm. The performance of the proposed outlier prediction is compared over the conventional models and attained better mean average precision of 0.98 (Glass dataset) and 0.986 (Abalone dataset), respectively.

High-Precision DOA Estimation Based on Synthetic Aperture and Sparse Reconstruction.

The direction-of-arrival (DOA) estimation is predominantly influenced by the antenna's aperture size. However, space constraints on flight platforms often necessitate the use of antennas with smaller apertures and fewer array elements. This inevitably imposes limitations on the DOA estimation's resolution and degrees of freedom. To address these precision constraints, we introduce an accurate DOA estimation method based on spatial synthetic aperture model. This method adopts a two-stage strategy to ensure both efficiency and precision in DOA estimation. Initially, the orthogonal matching pursuit (OMP) reconstruction algorithm processes the original aperture data, providing a rough estimate of target angles that guides the aircraft's flight direction. Subsequently, the early estimations merge with the aircraft's motion space samples, forming equivalent spatially synthesized array samples. The refined angle estimation then employs the OMP-RELAX algorithm. Moreover, with the off-grid issue in mind, we devise an estimation method integrating Bayesian parameter estimation with dictionary sequence refinement. The proposed technique harnesses the spatial synthetic aperture for pinpoint estimation, effectively addressing the challenges of atomic orthogonality and angular off-grid on estimation accuracy. Importantly, the efficiency of deploying sparse reconstruction for angle estimation is bolstered by our phased strategy, eliminating the necessity for fine grid analysis across the entire observation scene. Moreover, the poor estimation accuracy caused by coherent source targets and angular-flickering targets is improved by sparse reconstruction. Through simulation and experiment, we affirm the proposed method's efficacy in angle estimation. The results indicate that target angle estimation errors are limited to within 1°. Furthermore, we assess the impact of variables such as target state, heading angle, spatial sampling points, and target distance on the estimation accuracy of our method, showcasing its resilience and adaptability.

The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review

Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction ranged from 1 min to 1 week, with more articles at lower temporal resolutions. Most works employed neural networks, focusing recently on deep learning models. Among the reported performance metrics, the most prevalent were mean absolute error, mean squared error, and mean absolute percentage error. Considering these metrics, the mean performance of the examined works was 0.56 m/s, 1.10 m/s, and 6.72%, respectively. The results underscore the novel effectiveness of machine learning in predicting wind conditions using high-resolution time data and demonstrated that deep learning models surpassed traditional methods, improving the accuracy of wind speed and direction forecasts. Moreover, it was found that the inclusion of non-wind weather variables does not benefit the model’s overall performance. Further studies are recommended to predict both wind speed and direction using diverse spatial data points, and high-resolution data are recommended along with the usage of deep learning models.

Spatial sampling versus acquisition time of room impulse responses for low-frequency sound zones

Sound zone methods aim to create multiple listening areas within a room, allowing independent playback of different audio content. Achieving this requires a loudspeaker array, multiple microphones per sound zone, and a set of control filters, which design involves the room impulse responses (RIRs) between loudspeakers and microphones. Under certain acoustic conditions, acquiring these RIRs is possible using very short acquisition times. At lower frequencies, longer wavelengths enable spatial sampling of the sound zones with fewer points, reducing the number of microphone positions required and amount of data to process. This study exploits this characteristic to render and evaluate low-frequency sound zones in two real rooms, with few spatial sampling points. We assess the performance by objectively evaluating multiple spatial sampling configurations and RIR acquisition times. Furthermore, we evaluate the system's ability to control sound beyond the sampled regions by assessing the sound field over wider areas. For sound zones roughly the size of a human head, satisfactory performance is achieved in low reverberation conditions with at least two microphones per ear, even for RIR acquisition times as short as 150 μs. Conversely, the performance degradation in highly reflective environments cannot be compensated using additional microphones nor longer acquisition times.

Multizone reproduction by matching the velocity vectors

This paper proposes a velocity-based multizone reproduction method, which reproduces the velocity vectors throughout each listening zone. Velocity vectors are related to the localization of sound below 700 Hz. Previous work considered reproducing the velocity vectors at sweet spots or on the listening zone’s boundary, and usually involved measurements using multiple velocity sensors. In this paper, in each listening zone, the velocity vectors are calculated from the spherical harmonic coefficients of the pressure, which are obtained by a spherical microphone array. The calculation of the velocity vectors is based on the sound field translation formula. Moreover, by reproducing the desired velocity vectors throughout each listening zone, listener's movement within each zone is allowed. To enlarge the size of the listening zone, the sound field in each listening zone is expressed by a sparse distribution of plane waves that results in the same velocity vectors. The pressure transfer functions between each loudspeaker and each listening zone are measured by placing a spherical microphone array at multiple spatial sampling points within each listening zone. The loudspeaker driving functions are calculated by matching the velocity vectors due to the sparse distribution of plane waves in each listening zone.

On the stability of relativistic perfect fluids with linear equations of state p=Krho where 1/3

For 1/3<K<1, we consider the stability of two distinct families of spatially homogeneous solutions to the relativistic Euler equations with a linear equation of state p=Krho on exponentially expanding FLRW spacetimes. The two families are distinguished by one being spatially isotropic while the other is not. We establish the future stability of nonlinear perturbations of the non-isotropic family for the full range of parameter values 1/3<K<1, which improves a previous stability result established by the second author that required K to lie in the restricted range (1/3, 1/2). As a first step towards understanding the behaviour of nonlinear perturbations of the isotropic family, we construct numerical solutions to the relativistic Euler equations under a mathbb {T}{}^2-symmetry assumption. These solutions are generated from initial data at a fixed time that is chosen to be suitably close to the initial data of an isotropic solution. Our numerical results reveal that, for the full parameter range 1/3<K<1, the density gradient frac{partial _{x}rho }{rho } associated to a nonlinear perturbation of an isotropic solution develops steep gradients near a finite number of spatial points where it becomes unbounded at future timelike infinity. This behavior of the density gradient was anticipated by Rendall (Ann Henri Poincaré 5(6):1041–1064, 2004), and our numerical results confirm his expectations.

Research on the extraction method of traditional elements in environmental art space design based on intelligent algorithm

Abstract This paper builds a spatial design model based on the reconstruction of 3D visual spatial points in an indoor environment and combines the minimum Euclidean distance to calculate the vector relationship between spatial points and realize the corresponding spatial point matching. For the exploration of traditional elements in spatial design, an extraction method based on the improved cuckoo algorithm is proposed. By adding a dynamic inertia weight adjustment method, one inertia weight value is randomly selected from the Gaussian distribution each time to balance the algorithm’s local and global search. By extracting traditional elements, calligraphy is the most frequent in the home space, with 79.4% integration. The tea set is the most frequent traditional element in the dining space, with 84.53% integration. A couplet is the most frequent traditional element in the public open space, with 71.98% integration. The extraction based on intelligent algorithms can help designers better integrate traditional elements in the design of environmental spaces.

Drip Irrigation Soil-Adapted Sector Design and Optimal Location of Moisture Sensors: A Case Study in a Vineyard Plot

To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management.

Application of Data Visualization Interaction Technology in Aerospace Data Processing

A visualization and interactive network topology model are studied based on real-time features generated during spaceflight. Start by establishing a consistent set of data and logical interaction interfaces. This paper presents a method of scenario model construction and application programming based on virtual reality technology. The scene elements are extracted into two types of primitives, namely logical type and simulated object type. This provides a unified architecture for the editing and processing of graphic elements. This system can realize the automatic creation of the scene. Then the point cloud data obtained by sparse reconstruction of SFM is reconstructed to the Poisson surface. You get a dense, uniform grid. Experiments show that the proposed algorithm can realize the 3D reconstruction of non-cooperative objects. The spatial feature points obtained in the spatial positioning of non-cooperative objects can provide necessary technical support for its orbit positioning. The model can quickly generate new model scenario pages according to the characteristics of the task. This method changes the display mode, which can only be static or limited dynamic before. It has also improved the efficiency of space mission preparation.

LADI: Landslide displacement interpolation through a spatial-temporal Kalman filter

Monitoring landslide displacement is critical towards understanding kinematics and evaluating risk. Both remote sensing technology and in-situ sensors are currently used for monitoring purposes, each with their own spatio-temporal advantages. However, the majority of current workflows analyze these data independently, missing a synergistic opportunity to combine complementary datasets to enhance analyses both spatially and temporally. This work presents the first landslide specific displacement interpolation technique using a novel approach, LADI (Landslide Displacement Interpolation), which creates a high-spatial, high-temporal resolution interpolation of landslide surface displacement by combining remotely-sensed observation data with and in-situ sensor data. At a minimum, LADI requires only a single in-situ monitoring station (control point) and a single high-resolution displacement dataset (calibration data) to function. LADI implements a series of Kalman filters, where interpolation is first performed spatially, then optionally in the temporal domain. LADI is demonstrated on both simulated and real-world landslide data to showcase the behavior, effectiveness, and applicability of the method. Results are compared to common spatial interpolation methods, which demonstrate that LADI achieves superior performance (average EPE = 0.018 m and 0.031 m of LADI and the best performing common spatial interpolation method respectively when number of interpolation control points = 4) in the real-world test sparse displacement dataset when the number of spatial control points is less than sixteen. Additionally, the performance gain of utilizing LADI is further enhanced over common interpolation methods when the interpolation control points are not equally distributed geographically (average EPE = 0.016 m and 0.049 m respectively) as is usually the case with in-situ landslide displacement monitoring stations.

Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB0 shim array fields.

Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB0 shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels. Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations. Requirements for refocusing pulse designs are derived from the extended phase graph formalism obviating time-consuming sub-voxel isochromatic simulations to model the effects of crusher gradients. To refine resultant slice-profiles because of voxelwise optimization functions, we propose an algorithm that stochastically offsets spatial points at which loss is computed during optimization. We first applied our proposed design framework to standard slice-selective excitation and refocusing pulses in the absence of non-linear ΔB0 shim array fields and compared them against pulses designed with Shinnar-Le Roux algorithm. Next, we demonstrated our technique in a simulated setup of fetal brain imaging in pregnancy for restricted-slice excitation and refocusing of the fetal brain. Our proposed framework for optimizing RF pulse and time-varying spatially non-linear ΔB0 shim array fields achieve high fidelity restricted-slice excitation and refocusing for fetal MRI, which could enable zoomed fast-spin-echo-MRI and other applications.

Light-weight 3D mesh generation networks based on multi-stage and progressive knowledge distillation

Due to the high data dimensionality and the complexity of the problem, existing 3D mesh reconstruction models often require significant computational resources to achieve satisfactory results. While lightweight model based on knowledge distillation has been explored in many fields such as image classification, training a lightweight 3D mesh reconstruction model remains a challenging task. In this paper, we propose a method to learn a lightweight 3D mesh reconstruction network using knowledge distillation. Specifically, we introduce a novel approach called multi-stage and progressive knowledge distillation, which effectively enhances the guidance from the teacher network to the student network, thereby improving reconstruction performance. Additionally, we propose a projection-based spatial feature unpooling method to provide more accurate spatial features for the increased spatial points. Experimental results show that our lightweight 3D mesh reconstruction network has comparable performance to existing complex models while greatly reducing the number of parameters. Specifically, our method achieves its 98.97% accuracy while reducing the number of graph neural network parameters to 71.42% of the teacher network.