Grid-based Method Research Articles

Augmenting progress monitoring in soil-foundation construction utilizing SOLOv2-based instance segmentation and visual BIM representation

Efficient management of construction progress requires regular progress tracking, but monitoring progress on a daily basis can be both time-consuming and labor-intensive since the meticulous manual data processing is involved. Soil-foundation construction entails multiple uncertain underground safety risks, the elimination of which requires significant time and effort, thereby increasing the likelihood of schedule overruns. Automation and augmentation of progress monitoring are necessary for soil-foundation construction. There are two primary research challenges in the context of soil-foundation construction: the precise differentiation of objects with similar apparent characteristics, and the accurate detection of partially obscured objects. The existing approaches primarily address such issues by image post-processing rather than augmenting image feature inference. To enhance the efficiency of progress monitoring and facilitate unmanned inspection, an integrated computer vision-based framework for the tracking of soil-foundation construction progress was proposed. An improved SOLOv2 was utilized to qualitatively and quantitatively recognize the construction progress. Subsequently, the actual and differential progress could be integrated into streamlined BIM based on a self-adaptive grid-based mapping method. The approach was applied to a campus building project in China, resulting in a high segmentation accuracy (mAP = 90.9%). The improved SOLOv2 was found to surpass other state-of-the-art segmentation algorithms. Further, the impact of grid size on the mapping accuracy of construction progress was explored. The present study promotes automated schedule tracking and provides a feasible approach for developing a digital twin of soil-foundation construction.

Kernel-free boundary integral method for two-phase Stokes equations with discontinuous viscosity on staggered grids

A discontinuous viscosity coefficient makes the jump conditions of the velocity and normal stress coupled together, which brings great challenges to some commonly used numerical methods to obtain accurate solutions. To overcome the difficulties, a kernel free boundary integral (KFBI) method combined with a modified marker-and-cell (MAC) scheme is developed to solve the two-phase Stokes problems with discontinuous viscosity. The main idea is to reformulate the two-phase Stokes problem into a single-fluid Stokes problem by using boundary integral equations and then evaluate the boundary integrals indirectly through a Cartesian grid-based method. Since the jump conditions of the single-fluid Stokes problems can be easily decoupled, the modified MAC scheme is adopted here and the existing fast solver can be applicable for the resulting linear saddle system. The computed numerical solutions are second order accurate in discrete ℓ2-norm for velocity and pressure as well as the gradient of velocity, and also second order accurate in maximum norm for both velocity and its gradient, even in the case of high contrast viscosity coefficient, which is demonstrated in numerical tests. In addition, moving interface driven by the surface tension is also provided to validate the efficiency of the KFBI method.

Visual Secret Sharing of Gray and Color Images using Fuzzy Random Grids

Problem:Visual secret sharing is a method used to secure images by converting a secret image into multiple shares so that the original image cannot be recognized from individual shares, and no calculations are required during the image decryption, but the original image can be visually seen by stacking the shares. However, one of the significant challenges in this method is the increased size of the shares and the recovered image, which is called pixel expansion. Researchers have proposed several methods to solve this problem, but many of them are limited to binary images, and they cannot handle gray and color images without converting them to binary. Solution:This study proposes a novel fuzzy random grid-based method to directly encrypt gray and color images without any pixel expansions. In this method, fuzzy random grids with decimal values between 0.0 to 1.0 are generated during the encryption stage, and fuzzy operators are used for the decryption stage. Results:The evaluation results demonstrate the ability of the proposed solution in encrypting gray and color images without converting them to binary and without any pixel expansions. The individual shares of the proposed method do not reveal any information from the original image, making it a secure method. The quality of the decrypted images has been evaluated using both subjective and objective evaluation methods such as PSNR and SSIM metrics, which show state-of-the-art results.

A grid-based interpretable machine learning method to understand the spatial relationships between watershed properties and water quality

Understanding the spatial relationship between watershed properties and water quality is essential for watershed management. However, it remains challenging to identify such a relationship because of its nonlinearity. To understand the temporal and spatial effects of the driving of water quality, a grid-based interpretable machine learning approach was developed to explore the relationship between water quality (i.e. nitrogen, phosphorus, and chemical oxygen demand) and watershed properties in the Minjiang River Watershed (MRW). A grid-based model was developed with 1837 input features for streams and reservoirs, respectively, based on land use, population, fertilizer, and Noah land surface model in the MRW. Compared to the water quality in the stream, the water quality in the reservoirs may be more sensitive to the change environmental settings. The reservoirs may amplify the effects of climate variability on water quality. The soil moisture may modify the water quality, especially the dryness of the top soil may control the pollutant that entry into the watershed. The effects of the urbanization may modify the distributions of important feature that control the regional water quality. The point source pollution per inhabitant may be reduced with increasing urbanization. This study provides an in-depth understanding of the relationship between water quality and watershed properties, temporally and spatially.

Numerical investigation of structural models equipped with tuned liquid damper

Tuned liquid dampers (TLDs) are introduced in structural systems to suppress vibration of the structure due to dynamic actions. Using a grid-based Finite Volume Method-Finite Element Method (FVM/FEM), efficiency of TLDs of cylindrical and rectangular geometries was evaluated by performing a two-way Fluid-Structure Interaction (FSI) analysis. The nonlinear sloshing response of fluid was simulated by employing a three-dimensional Navier–Stokes equation as the governing equation and occurrence of wave breaking was accounted by using Volume of Fluid (VOF) method for free surface tracking. Utilizing the developed numerical model, a series of three-dimensional transient analysis were conducted on a structure with and without TLDs aiming to investigate effect of base excitation amplitude. The result show that maximum performance of TLDs at resonance is obtained when the forcing ratio is limited to 0.1, and for this case, both cylindrical and rectangular TLDs displayed comparable efficiency. Effect of excitation frequency fe was also investigated under a wide range of the forcing ratio showing that as fe increases, the natural frequency of coupled TLD-structural system approaches the natural frequency of un-damped structural system.

Tumor Heterogeneity Confounds Lymphocyte Metrics in Diagnostic Lung Cancer Biopsies.

The immune microenvironment is involved in fundamental aspects of tumorigenesis, and immune scores are now being developed for clinical diagnostics. To evaluate how well small diagnostic biopsies and tissue microarrays (TMAs) reflect immune cell infiltration compared to the whole tumor slide, in tissue from patients with non-small cell lung cancer. A TMA was constructed comprising tissue from surgical resection specimens of 58 patients with non-small cell lung cancer, with available preoperative biopsy material. Whole sections, biopsies, and TMA were stained for the pan-T lymphocyte marker CD3 to determine densities of tumor-infiltrating lymphocytes. Immune cell infiltration was assessed semiquantitatively as well as objectively with a microscopic grid count. For 19 of the cases, RNA sequencing data were available. The semiquantitative comparison of immune cell infiltration between the whole section and the biopsy displayed fair agreement (intraclass correlation coefficient [ICC], 0.29; P = .01; CI, 0.03-0.51). In contrast, the TMA showed substantial agreement compared with the whole slide (ICC, 0.64; P < .001; CI, 0.39-0.79). The grid-based method did not enhance the agreement between the different tissue materials. The comparison of CD3 RNA sequencing data with CD3 cell annotations confirmed the poor representativity of biopsies as well as the stronger correlation for the TMA cores. Although overall lymphocyte infiltration is relatively well represented on TMAs, the representativity in diagnostic lung cancer biopsies is poor. This finding challenges the concept of using biopsies to establish immune scores as prognostic or predictive biomarkers for diagnostic applications.

Kernel free boundary integral method for 3D incompressible flow and linear elasticity equations on irregular domains

A second-order accurate kernel-free boundary integral method is presented for Stokes flow and linear elasticity boundary value problems on three-dimensional irregular domains. It solves equations in the framework of boundary integral equations, whose corresponding discrete forms are well-conditioned and solved by the GMRES method. A notable feature of this approach is that the boundary or volume integrals encountered in BIEs are indirectly evaluated by a Cartesian grid-based method, which includes discretizing corresponding simple interface problems with a MAC scheme, correcting discrete linear systems to reduce large local truncation errors near the interface, solving the modified system by a CG method together with an FFT-based Poisson solver. No extra work or special quadratures are required to deal with singular or hyper-singular boundary integrals and the dependence on the analytical expressions of Green’s functions for the integral kernels is completely eliminated. Numerical results are given to demonstrate the efficiency and accuracy of the Cartesian grid-based method.

Optimal sampling-based path planning for mobile cable-driven parallel robots in highly constrained environment

Mobile cable-driven parallel robots (MCDPRs) is a novel concept of cable-driven parallel robots (CDPRs) developed by mounting several mobile bases to discrete the conventional fixed frame. However, the additional mobile bases introduce more degree-of-freedom (DoF), thereby causing the kinematic redundancy. Moreover, mobile bases are susceptible to disturbances causing inconsistent performance. Hence, path planning of MCDPRs becomes a challenging issue due to various internal and external constraints involved. In this article, an optimization-based path planning method is proposed for MCDPRs in highly constrained environments with considering kinematic stability. The proposed approach quickly generates feasible paths for coupled mobile bases by using the developed multi-agent rapidly exploring random tree (MA-RRT). In this process, the tree can share information through the heuristics method to optimize the path, and the adaptive sampling strategy is thus proposed to increase the tree growth efficiency by self-adjusting sampling space. Moreover, the developed dynamic control checking method (DCC) is integrated with MA-RRT to smooth the path and the kinodynamic constraints of mobile bases can be satisfied. To generate the path of the end-effector, two performance metrics are designed considering the kinematic and stability of the MCDPR. Based on the performance metrics, the grid-based search method is developed to generate the path for the end-effector. Finally, the convincing performance of the proposed method is revealed through the dynamic simulation software (CoppeliaSim) and real-world experiments based on a self-built MCDPR prototype.

Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties

Axial compressors are inevitably affected by various uncertain factors in the process of manufacture and operation. These uncertainties obviously lead to reduced efficiency and large performance dispersion. However, researches on uncertainty quantification and robust design of compressors still faces severe difficulties due to the complexity of compressor structure and internal flow. This paper aims to present an automated and effective framework for uncertainty quantification and aerodynamic robustness optimization of axial compressor. The manufacturing error distribution is derived from the measurement data of machined fan blades, and the sparse grid-based polynomial chaos expansion method is used to propagate the uncertain factors and predict the probability density distribution of the fan performance. A novel surrogate model that combines a self-organizing mapping and a back-propagation neural network is constructed to explore and visualize the correlation between uncertainty parameters and performance responses. Robust aerodynamic design optimization is achieved based on the genetic algorithm. The results indicate that the coupled neural network model exhibits good accuracy for uncertain approximate modeling. Compared with the prototype fan, the optimized fan's mean isentropic efficiency and pressure ratio increase by 0.97% and 0.72%, respectively. The standard deviation of isentropic efficiency, pressure ratio, and mass flow rate decrease by 46.3%, 21.4%, and 15.2%, respectively. The present study provides a reference and exploration for uncertainty quantification and robust optimization of advanced refined multi-stage turbomachinery.

Collaborative Intelligence Enabled Routing in Green IoV: A Grid and Vehicle Density Prediction-Based Protocol

Green Internet of Vehicles (IoV) is a newly-emerged research area which focuses on reducing networking overhead and improving communication efficiency in future vehicular cyber-physical systems (VCPS). A critical requirement to realize such tasks in Green IoV is the achievement of effective routing protocols for data dissemination. However, maintaining end-to-end communication and reducing the communication overhead at the same time is quite challenging due to high vehicle dynamics and complex traffic environments. In this paper, we propose a vehicle density prediction-based routing protocol called VDPGrid. Firstly, we introduce a vehicle density prediction model according to the spatio-temporal features of vehicle trajectories. Then, to reduce the communication overhead and improve communication efficiency, we divide the map into grids and present a routing path evaluation scheme that jointly considers the vehicle density, link quality, and routing length. Moreover, a grid-based routing method is proposed to select the optimal relay node according to real-time traffic information. Finally, extensive experiments using real-world vehicle trajectories are conducted to validate the effectiveness of our method.

An Efficient Cartesian Grid-Based Method for Scattering Problems with Inhomogeneous Media

An Efficient Cartesian Grid-Based Method for Scattering Problems with Inhomogeneous Media

Regional ionospheric electron density modeling by assimilation of GPS-derived TEC into IRI-provided grids on May 8, 2016

Retrieved vertical total electron content (VTEC) via dual-frequency GPS measurements is a valuable observation for regional VTEC modeling of the ionosphere (3D), and also ionospheric electron density (IED) modeling (4D). In the case of 4D modeling, spatial and temporal changes in IED are imaged. 4D reconstruction of IED in different ionosphere layers is not possible only by retrieved VTECs. In all IED modeling methods, in addition to VTEC observations, a priori knowledge of how electron density is distributed in different layers is required, which is often taken from global empirical models. One of these methods is data assimilation, in which the information of a global model is considered as background and both observations and background are used optimally for IED modeling. Data assimilation has more prominent importance in regions where the observations are sparse. In this study, a grid-based data assimilation method is proposed for IED modeling. The optimum interpolation method (OI) is presented with a modification to find the optimal variance factor related to the background covariance matrix. The covariance matrix of the background during the data assimilation interval is estimated by an optimization process. The method is utilized for regional assimilative IED modeling over Iran with sparse VTEC data from a low number of permanent GPS stations on May 8, 2016 which is the most active ionospheric day in that year. International reference ionosphere (IRI) is considered as background, and although it has a significant bias in the study region, the proposed method resulted in an accurate regional IED model. The model is evaluated in two ways. First, by measuring RMS of the differences between parts of VTECs that are excluded from observations as test data and the obtained VTECs from the model. Second, by examining the accuracy of ionospheric delay estimation via single-frequency positioning at a control station in Tehran. Despite the use of sparse data on an active ionospheric day and poor background accuracy in the study region, the constructed regional assimilative model is accurate both in RMS with test data and ionospheric delay estimation.

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites.

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Vlasov–Maxwell equations with spin effects

We present a numerical method to solve the Vlasov–Maxwell equations for spin-1/2 particles, in a semiclassical approximation where the orbital motion is treated classically while the spin variable is fully quantum. Unlike the spinless case, the phase-space distribution function is a $2\times 2$ matrix, which can also be represented, in the Pauli basis, as one scalar function $f_0$ and one three-component vector function $\boldsymbol f$ . The relationship between this ‘vectorial’ representation and the fully scalar representation on an extended phase space first proposed by Brodin et al. (Phys. Rev. Lett., vol. 101, 2008, p. 245002) is analysed in detail. By means of suitable approximations and symmetries, the vectorial spin-Vlasov–Maxwell model can be reduced to two-dimensions in the phase space, which is amenable to numerical solutions using a high-order grid-based Eulerian method. The vectorial model enjoys a Poisson structure that paves the way to accurate Hamiltonian split-time integrators. As an example, we study the stimulated Raman scattering of an electromagnetic wave interacting with an underdense plasma, and compare the results with those obtained earlier with the scalar spin-Vlasov–Maxwell model and a particle-in-cell code.

An innovative integrated path planning and trajectory tracking framework for a quadrotor slung load system in an urban environment

In this paper, a hybrid path planning and trajectory tracking controller for a quadrotor slung load system is presented. This controller has benefited from the fuzzy inference system in its tracking and swing stabilization tasks. This system also plays an important role in creating a grid-based obstacle avoidance method that works based on the concept of generating Non-stationary Artificial Potential Field (NAPF) in the proximity of the objects tracking a virtual target on the path computed by A* algorithm. The weakness of the conventional artificial potential field method, that is to stuck in local minima, is now resolved by this so-called hybrid A*-NAPF method in which the potential field is non-stationary. The A* algorithm as a fast optimal path determination method computes the discrete initial path using a network of grids already generated through transferring the environment to a binary occupancy map. The performance of the whole integrated system is investigated through various simulation scenarios including those with moving obstacles. The results indicate a very good performance with high adaptability characteristic.

Promising insights in parallel grid-based algorithms for quantum chemical topology.

Some straightforward improvements designed to make grid-based quantum chemical topology more efficient and faster are presented. The strategy focuses on both the evaluation of the scalar function over three-dimensional discrete grids and the algorithms aimed to follow and integrate gradient trajectories over the basin volumes together. Beyond the density analysis, we show that the scheme is quite suitable for the electron localization function and its complex topology. With this speed-up of the parallelized process used to generate 3d-grids, this new scheme is several orders of magnitude faster than the original grid-based method implemented in our laboratory (TopMod09). The efficiency of our implementation (TopChem2) was also compared with well-known grid-based algorithms designed to assign the grid points to basins. The performances, speed versus accuracy have been discussed on the basis of results obtained from selected illustrative examples.

Grid-based methods for chemistry simulations on a quantum computer.

First-quantized, grid-based methods for chemistry modeling are a natural and elegant fit for quantum computers. However, it is infeasible to use today's quantum prototypes to explore the power of this approach because it requires a substantial number of near-perfect qubits. Here, we use exactly emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms that model 2D and 3D atoms with single and paired particles. A range of tasks is explored, from ground state preparation and energy estimation to the dynamics of scattering and ionization; we evaluate various methods within the split-operator QFT (SO-QFT) Hamiltonian simulation paradigm, including protocols previously described in theoretical papers and our own techniques. While we identify certain restrictions and caveats, generally, the grid-based method is found to perform very well; our results are consistent with the view that first-quantized paradigms will be dominant from the early fault-tolerant quantum computing era onward.

Multivariate Correlation Matrix-Based Deep Learning Model With Enhanced Heuristic Optimization for Short-Term Traffic Forecasting

Accurately capturing the spatial correlations of traffic network significantly benefits short-term traffic forecasting. Some existing works represent spatial correlations in a simple one-dimensional space, but they cannot represent the real spatial correlations among sensors comprehensively. The other existing works represent the spatial correlations through grid-based method, but the local correlation of constructed spatial map is too superficial to extract deep spatial features effectively. Therefore, a novel deep learning model is proposed, which aims to represent the spatial correlations more effectively through a new correlation matrix structure. In the proposed model, the correlations among sensors are calculated from multiple perspectives to construct the speed, volume, and occupancy correlation matrices respectively. Then, considering that highly correlated sensors are close in the spatial dimension, an enhanced heuristic optimization algorithm is proposed to evolve these three correlation matrices into optimal ones by reorganizing the highly correlated sensors into each others neighborhood. Finally, the three optimal correlation matrices are combined to form a three-dimensional multivariate correlation matrix characterized by locally high correlation, which is beneficial to exploit the deep spatial features of traffic network. The experiments show that the proposed model has better accuracy and stability than other commonly used baseline models.

Numerical simulation of aluminum particle agglomeration near the burning surface of solid propellants

Aluminum is an important additive in solid propellants used to improve energy efficiency, but agglomeration of aluminum particles can reduce the specific impulse of the engine, increase two-phase flow losses and cause ablation of the adiabatic layer. Therefore, it is important to understand the mechanism of formation, mode of movement and particle size characteristics of aluminum agglomerates in solid propellants to study the inhibition of agglomeration. In this paper, a three-dimensional numerical simulation of aluminum particle agglomeration near the burning surface of solid propellants was conducted using the discrete unit method based on direct tracking calculations of the detailed motion of individual particles. The model considered physical processes such as burning surface motion, aluminum particle precipitation, turbulent pulsation and the heating and melting of aluminum particle and used a grid-based contact detection method to accelerate the computational process. The agglomeration process of a solid propellant with specific formulation was experimentally investigated. Aluminum particle aggregates underwent splitting, and the final aluminum particle agglomerates were spherical droplets with typical structure. The agglomeration process of this propellant was investigated by means of a simulation. The accuracy of the agglomeration model was verified by comparing the results of the model for the agglomeration process and the shape and size distribution of agglomerates with available experimental data. Compared with the experimental results, the deviations of the equivalent particle sizes of agglomerates D10, D50, D90, D4,3, and D3,2 obtained by using the simulation results were 6.3%, 6.3%, 9.4%, 7.5% and 6.7%, respectively. The aluminum particle agglomeration model established in this paper can predict the particle size distribution of aluminum agglomerates near the burning surface of solid propellants. The model was also used to further investigate the effects of aluminum particle content, ammonium perchlorate (AP) particle size and environmental pressure on aluminum particle agglomeration. Increasing the aluminum content or AP particle size aggravated aluminum particle agglomeration, whereas increasing pressure weakened it.