Grid-based Method Research Articles

Changes in diagnostics of depression among Family physicians in Latvia following education course of National Research Programme BIOMEDICINE 2014-2017

The quantification and identification of ground cover plays a key role in erosion modelling, weed measurement, plant disease identification and other environmental applications. Currently, a variety of methods are used to mechanically classify digital images for ground cover. Only a few of these methods can distinguish green vegetation, straw/dormant vegetation, and exposed soil using only the Red-Green-Blue (RGB) spectrum. This research presents an approach to classifying ground cover using standard JPEG images and readily available Matlab (2018b) functions. The approach uses block segmentation, as opposed to pixel-wise or object-based segmentation, and compares multiple machine learning approaches with varying pixel block size and classification acceptance thresholds. The most successful classification approach found through this study was the decision tree algorithm with a 70-pixel block size and 60% classification acceptance threshold. Images were reduced to three feature descriptors: colour, texture, and oriented gradients to represent the respective RGB spectrum for an image. Both the training set and test set images used in this research came from field and greenhouse studies done between 2016 and 2019. The produced classifications were compared to manual coverage classifications using Samplepoint, a grid-based method, with R-squared values of 0.86 for green vegetation, 0.87 for straw/dormant vegetation, and 0.96 for exposed soil, respectively. This method showed strong performance for images containing exposed soil and either green vegetation or straw/dormant vegetation. The method was less effective for images with large quantities of both green vegetation and straw/dormant vegetation likely due to their similar shape.

Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

Predicting city-scale taxi origin-destination (OD) flows takes an important role in understanding passengers' travel demands as well as managing taxi operation and scheduling. But the complex spatial dependencies and temporal dynamics make this problem challenging. In this paper, a hybrid deep neural network prediction model based on convolutional LSTM (ConvLSTM) is proposed. For improving the prediction accuracy, the implicit correlation between travel time and OD flow is explored and they are combined as inputs of the prediction model. Moreover, in order to realize OD flows prediction at the road network level, and solve the problem that grid-based representation method cannot distinguish traffic flow at different heights, such as in multi-layer overpass areas, this paper presents a grid and road nested method to represent ODs. With the time of day partitioned into time slots, OD flows are extracted and predicted in both spatial and temporal domain. In the experiment, real taxi data are used to verify the proposed model and prediction method fully. And the experimental results show that the proposed model can effectively predict city-wide taxi OD flow, and outperforms the typical time sequence models and existing deep neural network models.

Efficient approximation approaches to minimal exposure path problem in probabilistic coverage model for wireless sensor networks

A well-known method for evaluating the coverage quality of Wireless Sensor Networks (WSNs) is using exposure as a measure, especially in barrier coverage problems. Among all studies related to exposure, discussions regarding the Minimal Exposure Path (MEP) problem have dominated research in recent years. The problem aims to find a path on which an intruder can penetrate through the sensing field with the lowest probability of being detected. This path along with its exposure value enables network infrastructure designers to identify the worst-case coverage of the WSN and make necessary improvements. Most prior research worked on the MEP problem under the assumption that there are no environmental factors such as vibration, temperature, etc., which causes errors in practical WSN systems. To overcome this drawback, we first formulate the MEP problem based on Probabilistic Coverage Model with noise (hereinafter PM-based-MEP) and introduce a new definition of the exposure metric for this model. The PM-based-MEP is then converted into a numerically functional extreme with high dimension, non-differentially and non-linearity. Adapting to these characteristics, we propose two approximation methods, GB-MEP and GA-MEP, for solving the converted problem. GB-MEP is based on the traditional grid-based method which is fine-tuned by several tweaks, and GA-MEP is formed by the genetic algorithm with a featured individual representation and an effective combination of genetic operators. Experimental results on numerous instances indicate that the proposed algorithms are suitable for the converted PM-based-MEP problem and perform well regarding both solution accuracy and computation time compared with existing approaches.

Rapid handling of outliers in dense sampling descriptor correspondence fields

The sparse-to-dense approach is considered to be the standard method of capturing the long-range motion of small objects during large displacement optical flow. Despite progress in the matching and interpolation of this approach, little work has focused on improving the handling of outliers after dense sampling descriptor matching. We propose an improved grid-based statistical matching method that can quickly remove outliers without calculating backward flow. First, a multigrid statistical matching method is developed to remove the most outliers of the dense sampling descriptor correspondence field. Second, to improve the accuracy of outliers handling, the misjudgment match in the edge grid is corrected based on the statistical matching constraint. The results of extensive experiments on public optical flow datasets demonstrate the effectiveness of the proposed method.

Optimal Irregular Wind Farm Design for Continuous Placement of Wind Turbines with a Two-Dimensional Jensen-Gaussian Wake Model

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.

On wind speed pattern and energy potential in China

To achieve sustainable socioeconomic development as part of the global community, China has set ambitious goals for developing wind power within its national energy security framework. Existing wind power density maps in China present some knowledge gaps (e.g., lack of spatiotemporal resolution/characteristics and estimates at the height of a typical wind turbine). To address them, we combine the advantages of large network of wind speed observations (2430 meteorological stations; 2006–2015) and the grid-based method in order to create the spatiotemporal distributions of wind resource at the hub height. We found that the spatiotemporal variability of wind resources in China is apparent. Nationally, the annual mean wind speed and wind power density are 4.09 m s−1 and 164.1 W m−2, respectively. Spatially, Northeast China has the highest wind potential with annual mean wind speed and power density 4.64 m s−1 and 204 W m−2, while South China has the lowest wind potential annual mean wind speed and power density 3.55 m s−1 and 97.4 W m−2. Temporally, the wind resources over the entire China are higher in the cold season (spring and winter with peaks in April) than those in the warm season (summer and autumn with bottoms in August). Specifically, the wind power output in “Three North” region (including Northeast China, North China and Northwest China) in the cold season is 1.4 times higher than that in the warm season. Some coastal regions in East China have relatively high wind power density (>500 W m−2) and small seasonal variations. These findings should help decision-makers to identify the areas suitable for utilizing wind energy, and optimizing power generation and transmission across the grid for better system performance.

Quantification of the strain field of sands based on X-ray micro-tomography: A comparison between a grid-based method and a mesh-based method

An in-situ triaxial shearing of a quartz sand sample with X-ray micro-tomography scanning is carried out. A grid-based method is presented to experimentally quantify the strain field of the sand based on its particle kinematics throughout the shear. The method incorporates the effects of particle rotations into the strain calculation. It is an extension of a grid-based method from the calculation of 2D strain fields of idealized granular materials in a DEM study to the experimental quantification of 3D strain fields of natural sands.Meanwhile, a widely used mesh-based method is also applied to the sand to acquire the strain results based on its particle displacements. The accuracies of both methods are evaluated by comparing the calculated local volumetric strain results with those from a voxel-based calculation. Both strain calculation methods are found to provide results that capture the overall trend of strain localization throughout the shearing, which follows the same trend as the evolution of particle kinematics. However, the results from the two methods exhibit different degrees of strain localization. Specifically, the strain results from the mesh-based method have a lower degree of strain localization than the voxel-based method, which is mainly due to the lack of accounting for particle rotations in the calculation. While the grid-based method provides basically consistent strain results with the voxel-based calculation when sufficient grids are used in the calculation. The study highlights the important role of particle rotation in the strain localization of sands.

A Grid-Based Method for Distribution Network Planning in Urban Areas

This paper proposes a grid-based method for distribution network planning. Firstly, based on the analysis of the economic and social development targets and distribution network status in the region, the central area is meshed and classified according to the relevant principles and objectives of distribution network planning as well as the nature and development depth of the land. Load forecasting based on grid layout is carried out to determine the saturated load corresponding to the current planning of the area. Finally, according to the current grid, load forecasting results and planning targets, the target grid and transition grid of the planning grid are determined. The above method is applied to the grid planning of a distribution network in Hangzhou China. The planning index is greatly improved, the target network frame of the distribution network is strong, the power supply range is clear, and the power supply reliability is high, which verifies the effectiveness of the method.

Comparative Study of Time-Domain Fatigue Assessments for an Offshore Wind Turbine Jacket Substructure by Using Conventional Grid-Based and Monte Carlo Sampling Methods

Currently, in the design standards for environmental sampling to assess long-term fatigue damage, the grid-based sampling method is used to scan a rectangular grid of meteorological inputs. However, the required simulation cost increases exponentially with the number of environmental parameters, and considerable time and effort are required to characterise the statistical uncertainty of offshore wind turbine (OWT) systems. In this study, a K-type jacket substructure of an OWT was modelled numerically. Time rather than frequency-domain analyses were conducted because of the high nonlinearity of the OWT system. The Monte Carlo (MC) sampling method is well known for its theoretical convergence, which is independent of dimensionality. Conventional grid-based and MC sampling methods were applied for sampling simulation conditions from the probability distributions of four environmental variables. Approximately 10,000 simulations were conducted to compare the computational efficiencies of the two sampling methods, and the statistical uncertainty of the distribution of fatigue damage was assessed. The uncertainty due to the stochastic processes of the wave and wind loads presented considerable influence on the hot-spot stress of welded tubular joints of the jacket-type substructure. This implies that more simulations for each representative short-term environmental condition are required to derive the characteristic fatigue damage. The characteristic fatigue damage results revealed that the MC sampling method yielded the same error level for Grids 1 and 2 (2443 iterations required for both) after 1437 and 516 iterations for K- and KK-joint cases, respectively. This result indicated that the MC method has the potential for a high convergence rate.

A unified pipe-network-based numerical manifold method for simulating immiscible two-phase flow in geological media

A unified pipe-network-based numerical manifold method (NMM) is developed to simulate immiscible two-phase flow in a geological medium. Both heterogeneous and non-heterogeneous geological media can be discretized as numerical pipe networks, which have high efficiency and accuracy in simulating fluid and mass transfer in fractured rock masses. A manifold element method is developed to solve the governing equations of immiscible two-phase flow in pipes. The developed NMM can simulate moving and deforming of two-phase flow interface. A grid-based front-tracking method updates the marker points constructing the fluid interface explicitly in each time step. The effectiveness of the NMM is verified through analytical and finite element analysis. Parametric studies are conducted by simulating immiscible two-phase flows with various fluid properties in homogeneous and inhomogeneous geological conditions. The results show that the developed method can efficiently simulate the moving interface of two-phase flow in geological media, including effects such as “viscous fingering”, a noteworthy phenomenon in enhanced oil recovery. When the mobility of the driving fluid is larger than that of the driven fluid, the inhomogeneity of the medium can cause the fluid interface to roughen, which increases over time during the process of two-phase flow. For the inverse situation, although the fluid interface remains rough, the roughness variation throughout the process is not prominent.

City block-based assessment of land cover components’ impacts on the urban thermal environment

Rapid urbanization has posed many problems for the urban thermal environment, which has spurred many scientists to focus their studies on the effects of landscape compositions and configurations on land surface temperature (LST) to offer some support to policy-making decisions to mitigate the phenomenon of the urban heat island (UHI) effect. Many prior studies have developed the relationship between land cover types and LST using pixel-by-pixel or grid-by-pixel regression analysis; from the viewpoint of practical usage in urban planning, this study estimates the impact of land cover compositions on LST by taking a city block-based regression analysis approach. Based on Landsat 8 TIRS images over a continuous span of 4 years, the proposed approach is compared to the conventional grid-based method and discussed in terms of its feasibility. We evaluate the relative contributions of different land cover components to the estimated relationship. First, the phenomenon of UHI is found to be an event affecting area rather than a pixel or point location. Second, as more images are employed, a more meaningful relationship between the land cover components and LST can be achieved based on statistical analysis. Third, the estimated effect of land cover composition on LST with different relative weights can be more helpful to allow local governments or other metropolitan areas with similar urban size to make policy decisions in urban management and mitigate the influence of the UHI effect on the human population.

Visual Evaluation of Soil Structural and Sugarcane Root Under Deep Strip-till and Conventional Tillage

The Visual Evaluation of Soil Structure (VESS) is a relatively simple methodology used for comparing management systems and for maintaining or recovering the quality of agricultural soils. The objective of this study was to evaluate the structural soil quality in the production of sugarcane using VESS. Three treatments were established: Deep Strip-till (DST), Conventional Tillage (CT) and Uncultivated area (UC). For DST and CT soil samples were taken from two locations: in-row and inter-row. Soil blocks were extracted from mini-trenches and carefully fragmented into aggregates, whose appearance, resistance, and characteristics of the structural units define quality scores. The density of visible roots was quantified by a grid-based counting method. DST at in-row location had improved the structural quality of the soil, providing greater root growth. Scores of visual soil quality in CT showed no difference between in-row and inter-row locations. Preserved from machinery traffic the in-row trail in CT did not result in benefit to soil quality. Variability in the scores among the replicate blocks for DST in-row suggests that the equipment had produced irregular soil tillage. VESS proved to be a good indicator from which it is feasible to evaluate impacts of agricultural machines and tillage implements on soil quality.

A semicoherent glitch-robust continuous-gravitational-wave search method

Isolated nonaxisymmetric rotating neutron stars producing continuous-gravitational-wave signals may undergo occasional spin-up events known as glitches. If unmodeled by a search, these glitches can result in continuous wave signals being missed or misidentified as detector artifacts. We outline a semicoherent glitch-robust search method that allows identification of continuous wave signal candidates that contain glitches and inferences about the model parameters. We demonstrate how this can be applied to the follow-up of candidates found by wide-parameter space searches. We find that a Markov chain Monte Carlo method outperforms a grid-based method in speed and accuracy.

Numerical investigation of high-peclet-number mixing in periodically curved microchannel with strong curvature

Liquid mixing in periodically curved microchannels with strong curvature is studied mainly using a random-walk particle-tracking method. The method uses a large number of particles to simulate fluid mixing. These particles are advected according to the velocity obtained by a grid-based method and undergo a random-walk to simulate the diffusion. For comparison purpose, the convective–diffusive equation for concentration is also solved by a grid-based method and a particle-tracking simulation with an approximate diffusion model (ADM). The present results show that the concentration distributions obtained by the random-walk particle-tracking method and the particle-tracking simulation with an ADM are in good agreement with each other. The details of concentration distribution at the exit of micromixers with strong curvature at higher Peclet numbers can be obtained by the present method. Multi-directional vortices due to large flow rate and strong curvature stretch and distort the interface between different species, and so improve fluid mixing effectively. The results reveal that the influence of channel curvature on fluid mixing increases with the increase of Reynolds number and higher mixing performance appears in the case with stronger curvature at higher Reynolds numbers.

Measuring Taxi Accessibility Using Grid-Based Method with Trajectory Data

Accessibility has drawn extensive attention from city planners and transportation researchers for decades. With the benefits of large-scale and varying time, this study aims to combine the taxi global positioning system (GPS) data with a cumulative opportunity measure to calculate taxi accessibility in Beijing, China. As traffic conditions vary significantly over time and space, we select four typical time periods and introduce a grid-based method to divide the study area into grid cells. Both the GPS signals and opportunities that include the constant points of interest, total drop-offs, and dynamic drop-offs, are aggregated in these grid cells. The cumulative opportunity measure counts all reachable grid cells within the given travel time threshold, along with the corresponding opportunities. The results demonstrate that the accessibility varies in the four time periods, with better performance seen in the late-night hours. Although the spatial distributions of the three kinds of opportunities are different, these accessibilities show great similarity. In addition, the relative accessibilities of different measures are highly correlated. In general, grid cells with higher accessibilities in one time period are likely to also have higher accessibilities in other time periods. Moreover, the results suggest that taxi accessibility can be measured from its trajectory data only.

De novo design of a fluorescence-activating β-barrel.

The regular arrangements of β-strands around a central axis in β-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with β-barrels. Here we show that accurate de novo design of β-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of β-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI,and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures.

A New Square Grid-Based Method for Identifying Solar Activities

The FITS is widely used as a common file format in astronomy. However, how to detect an effective celestial event from large-scale astronomical images with complex background is a thorny issue. This paper proposes a square grid structure for target detection (GSTD) of solar activities, inncluding the separation of the target area and the deletion of the background area. This kind of processing can greatly reduce the storage cost of the data set, while the solar activity area can be well preserved. The solar image is operated through an equally sized square grid to separate the solar activity target area and the background area to speed up the processing of images, improve processing accuracy, and effectively prevent image noise interference. The experiment results have shown that this method delivers satisfactory performance in accuracy and time-cost. Through the anti-jamming processing of image noise, the accurate positioning and effective recognition of solar activities are realized. This method has been proved to achieve good image segmentation recognition of solar phenomenon in the research of various types of solar activities. Moreover, it can provide a feasible way to reduce the storage occupancy of Content-Based Image Retrieval (CBIR) systems.

GRAIL: GRids of phArmacophore Interaction fieLds.

In the absence of experimentally derived, three-dimensional structures of receptors in complex with active ligands, it is of high value to be able to gain knowledge about energetically favorable interaction sites solely from the structure of the receptor binding site. For de novo ligand design as well as for lead optimization, this information retrieved from the protein is inevitable. The herein presented method called GRAIL combines the advantages of traditional grid-based approaches for the identification of interaction sites and the power of the pharmacophore concept. A reduced pharmacophoric abstraction of the target system enables the computation of all relevant interaction grid maps in short amounts of time. This allows one to extend the utility of a grid-based method for the analysis of large amounts of coordinate sets obtained by long-time MD simulations. In this way it is possible to assess conformation dependent characteristics of key interactions over time. Furthermore, conformational changes of the protein can be taken into account easily and information thus obtained well-guides a rational ligand design process. A study employing MD trajectories of the oncology target heat shock protein 90 showcases how well our novel approach GRAIL performs for a set of different inhibitors bound to their target protein and how molecular features of the inhibitors are subject to optimization.

A grid-based variational method to the solution of the Schrödinger equation: the q-exponential and the near Hartree-Fock results for the ground state atomic energies.

A grid-based variational method was proposed and applied to the ground state energies of atoms from the first to the third period of the periodic table. The nonuniform grid in the radial coordinate was defined by a q-exponential sequence. Some unusual properties between the optimum q-parameters and the electronic energies or atomic numbers are described. The behavior of the electronic energy, with respect to the q-parameter, yields near Hartree-Fock accuracy with a relatively small number of integration points. A simple relationship between the optimum q-parameters and the atomic numbers was found, which allowed the determination of the optimum q-parameters for atoms of the same period from two results. The remarkable results provide a simple alternative route to reach accurate results. The consistent results also suggest that this is not a random or accidental effect, but some optimum condition achieved by using a q-exponential mesh grid. Graphical abstract The q-exponential and the near Hartree-Fock results for the ground state atomic energies.

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.