Set Of Data Points Research Articles

Reconstruction of integrated hydraulic turbine characteristics curve based on classification weight neural network

To address the shortcomings in the drawing and processing of comprehensive characteristic curves of hydraulic turbines and the problems in the operation and management of small hydropower stations, this paper proposes a method for reconstructing the comprehensive characteristic curves based on classification weight neural network. Firstly, this study discretizes the comprehensive characteristic curve of the target turbine factory package to obtain multiple sets of discrete data points, and compares them with the actual operating data of small hydropower plants, and then pre-processes the operating data by the type and operating characteristics of the unit. Next, this paper uses XGBoost to classify the two types of data to obtain the leaf node optimal scores and uses the scores as the prediction weights of the deep neural network to grid the discrete points of the hydraulic turbine for prediction, and finally obtains the specific integrated characteristic graph of the unit. The method based on classification weight neural network can substantially improve the accuracy and reliability of composite characteristic curves while avoiding multivariate nonlinear mathematical problems, which provides a new method and idea for the reconstruction and expansion of the hydraulic turbine characteristic curve, and also provides a theoretical basis for the safe and economic operation of hydropower units.

FACAM: A Fast and Accurate Clustering Analysis Method for Protein Complex Quantification in Single Molecule Localization Microscopy

Single molecule localization microscopy (SMLM) enables the analysis and quantification of protein complexes at the nanoscale. Using clustering analysis methods, quantitative information about protein complexes (for example, the size, density, number, and the distribution of nearest neighbors) can be extracted from coordinate-based SMLM data. However, since a final super-resolution image in SMLM is usually reconstructed from point clouds that contain millions of localizations, current popular clustering methods are not fast enough to enable daily quantification on such a big dataset. Here, we provide a fast and accurate clustering analysis method called FACAM, which is modified from the Alpha Shape method (a point dataset analysis method used in many fields). By taking advantage of parallel computation, FACAM is able to process millions of localizations in less than an hour, which is at least 10 times faster than the popular DBSCAN method. Furthermore, FACAM adaptively determines the segmentation threshold, and thus overcomes the problem of user-defined parameters. Using simulation and experimental datasets, we verified the advantages of FACAM over other reported clustering methods (including Ripley’s H, DBSCAN, and ClusterViSu).

Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies.

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever-growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta-survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

Chinese Digital Arm (CDA): A High-Precision Digital Arm for Electrical Stimulation Simulation.

To effectively analyze the diffusion and accumulation of signals on the surface and inside the human body under electrical stimulation, we used the gray threshold of the Chinese Digital Human image dataset to segment an arm image and reconstruct the tissue to obtain its three-dimensional cloud point dataset. Finally, a semirefined digital arm entity model with the geometric characteristics of the actual human arm tissue was constructed using reverse engineering technology. Further input of the current signal stimulation under tDCS and tACS with additional analysis of the signal diffusion in the transient mode via model calculation revealed that DC electrical stimulation is likely to cause high-voltage burns. The effective depth achieved using the AC stimulation signal is considerable, and provides reference for the electrical stimulation selection. Simultaneously, in the digital arm model, the signal diffusion and tissue damage inside the arm can be analyzed by changing the field, which provides a theoretical basis for the experimental study of the human body.

The study of hypersurface-homogeneous space-time in Renyi holographic dark energy

The investigation of this work is carried on the hypersurface-homogeneous space-time in the presence of two fluids, one being the pressureless matter and the other being the Renyi holographic dark energy (RHDE). Eventually, this work solves the cosmological model with Renyi HDE by taking the Hubble horizon as an infrared (IR) cutoff ([Formula: see text]). The geometrical and matter parts of space-time are solved within the Saez–Ballester scalar-tensor theory of gravitation. Interestingly, this study obtains a time-varying deceleration parameter ([Formula: see text]) that exhibits a transition from deceleration to acceleration phase. For the configurations of parameter [Formula: see text] emerging in space-time, three physically plausible cosmological hypotheses of the cosmos are outlined in this work. The kinematical properties were obtained and discussed for the three values of [Formula: see text]. The study of cosmic expansion in the accelerated phase of this work is done through various cosmological parameters like EoS, deceleration parameter, statefinder parameter, etc. The Hubble parameter is derived in terms of cosmic time and redshift both. The present value of this parameter is estimated by taking [Formula: see text] point data sets of observational Hubble data (OHD). The stability of the model is verified through the analysis of squared speed of sound ([Formula: see text]) parameter. In this work, the obtained results match with recent observational data.

Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons

The current work aimed to predict three critical properties: critical temperature (Tc), critical volume (Vc), and critical pressure (Pc) of pure hydrocarbons. A multi-layer perceptron artificial neural network (MLP-ANN) has been adopted as a nonlinear modeling technique and computational approach based on a few relevant molecular descriptors. A set of diverse data points was used to build three QSPR-ANN models, including 223 points for Tc, Vc, and 221 for Pc. The entire database was randomly split into two subsets: 80% for the training set and 20% for the testing set. A large number of 1666 molecular descriptors were calculated and then reduced by a statistical methodology based on several phases to retain them into a reasonable number of relevant descriptors, wherein about 99% of initial descriptors were excluded. Thus, the Quasi-Newton backpropagation (BFGS) algorithm was applied to train the ANN structure. The results of three QSPR-ANN models showed good precision, confirmed by the high values of determination coefficient (R2) ranging from 0.9990 to 0.9945, and the low values of calculated errors, such as the Mean Absolute Percentage Error (MAPE) that ranged from 2.2497 to 0.7424% for the best three models of Tc, Vc, and Pc. The weight sensitivity analysis method was applied to know the contribution of each input descriptor individually or by class on each appropriate QSPR-ANN model. Moreover, the applicability domain (AD) method was also used with a strict limit of standardized residual values (di = ±2). However, the results were promising, with nearly 88% of the data points validated within the AD range. Finally, the results of the proposed QSPR-ANN models were compared with other well-known QSPR or ANN models for each property. Consequently, our three models provided satisfactory results, outperforming most of the models mentioned in this comparison. This computational approach can be applied in petroleum engineering and other related fields to accurately determine the critical properties of pure hydrocarbons: Tc, Vc, and Pc.

A point cloud segmentation method for power lines and towers based on a combination of multiscale density features and point-based deep learning

ABSTRACT The point segmentation of power lines and towers aims to use unmanned aerial vehicles (UAVs) for the inspection of power facilities, risk detection and modelling. Because of the unclear spatial relationship between the point clouds, the point segmentation of power lines and towers is challenging. In this paper, the power line and tower point datasets are constructed using Light Detection and Ranging (LiDAR) and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network. First, the data are blocked and the neighbourhood is constructed. Second, the point clouds are downsampled to produce sparse point clouds. The point clouds before and after sampling are rotated, and their density is calculated. Next, a direct mapping method is selected to fuse the density information; a lightweight network is built to learn the features. Finally, the point clouds are segmented by concatenating the local features provided by PointCNN. The algorithm performs effectively on different types of power lines and towers. The mean interaction over union is 82.73%, and the overall accuracy can reach 91.76%. This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.

Mapping suitable habitat for Nigeria–Cameroon chimpanzees in Kom-Wum Forest Reserve, North-Western Cameroon

Great apes lose suitable habitats required for their reproduction and survival due to human activities across their distribution range in Africa. Little is known about habitat suitability of the Nigeria–Cameroon chimpanzee [Pan troglodytes ellioti (Matschie, 1914)], particularly for populations inhabiting forest reserves in North-West Cameroon. To address this knowledge gap, we employed a common species distribution model (MaxEnt) to map and predict suitable habitats for the Nigeria–Cameroon chimpanzee in Kom-Wum Forest Reserve, North-West Cameroon, based on environmental factors that potentially affect habitat suitability. We related these environmental factors to a dataset of chimpanzee occurrence points recorded during line transect and reconnaissance (recce) surveys in the forest reserve and surrounding forests. Up to 91% of the study area is unsuitable for chimpanzees. Suitable habitats only represented 9% of the study area, with a high proportion of highly suitable habitats located outside the forest reserve. Elevation, secondary forests density, distance to villages and primary forests density were the most important predictors of habitat suitability for the Nigeria–Cameroon chimpanzee. The probability of chimpanzee occurrence increased with elevation, secondary forest density and distance from villages and roads. Our study provides evidence that suitable chimpanzee habitat in the reserve is degraded, suggesting that efforts to maintain protected areas are insufficient. The reserve management plan needs to be improved to conserve the remaining suitable habitat and to avoid local extinction of this endangered subspecies.

Application of multi-optimizer neural network in predicting the time-variant response of the dynamic systems

Due to high utility of the sandwich structures in the vast spectrum of applications, this research focuses on the practical solution to protect them from damaging consequences of thermal shocks through parametric assessment of its time-variant thermoelastic performance. To this end, current research proposes a novel approach by taking the advantage of coupling the exactitude of the analytical solutions with the agility of the machine-learning methods to predict the accurate results by lower computational efforts. Case study of this research is a sandwich sector plate with functionally-graded carbon-nanotubes reinforced (FG-CNTR) nanocomposite face-layers exposed to the thermal shock loading. As another novelty, this study considers a radial-variant elastic substrate as a mechanism of resistance against the destructive consequences of thermal shock loading. As all machine-learning based regressors require the set of datapoints, this study employs harmonic format of the differential quadrature approach (HDQA) to find the system’s response toward thermal shock in the background of general elasticity theory at definite design- points. Laplace transform integrated with the extended model of Dubner and Abate’s scheme to translate the time-variant response of the system from time to Laplace domain and vice versa. Validity of the solution used in this survey is confirmed through comparing its outcomes with those of the published articles. As a valuable finding, it is strongly suggested to enhance the system’s thermal shock resistance by using the substrates which their reaction’s dependency to the radial coordinate is expressed by higher-order polynomial estimator functions.

Multi-Morphological Pulse Signal Feature Point Recognition Based on One-Dimensional Deep Convolutional Neural Network

Radial pulse signals are produced by the periodic ejection of blood from the heart, and physiological and pathological information of the human body can be analyzed by extracting the time-domain characteristics of pulse waves. However, since pulse signals are weak physiological signals on the body surface and complex, the acquisition of pulse characteristics using the traditional curvature method will produce a large error, which cannot meet the needs of pulse wave analysis in current clinical practice. To solve this problem, a multi-morphological pulse signal feature recognition algorithm based on the one-dimensional deep convolutional neural network (1D-DCNN) model is proposed. We used the multi-channel pulse diagnosis instrument independently developed by the team to collect radial pulse signals under continuous pressure of the test subjects and collected 115 subjects and extracted a total of 1300 single-cycle pulse signals and then divided these pulse signals into 6 different forms. Five types of pulse signal time-domain feature points were labeled, and five independent feature point datasets were labeled and formed five customized neural network models that were generated to train and identify the pulse feature point datasets independently. The results show that the correction coefficient (Radjusted2) of the multi-class pulse signal processing algorithm proposed in this paper for each type of feature point recognition reaches more than 0.92. The performance is significantly better than that of the traditional curvature method, which shows the accuracy and superiority of the proposed method. Therefore, the multi-class pulse signal characteristic parameter recognition model based on the 1D-DCNN model proposed in this paper can efficiently and accurately identify pulse time-domain characteristic parameters, which can be applied to discriminate time-domain pulse information in clinical practice and assist doctors in diagnosis.

Point cloud registration: a mini-review of current state, challenging issues and future directions

<abstract> <p>A point cloud is a set of data points in space. Point cloud registration is the process of aligning two or more 3D point clouds collected from different locations of the same scene. Registration enables point cloud data to be transformed into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to those reliant on targets being placed in the scene before data capture, there are various registration methods available that are based on using only the point cloud data captured. Until recently, cloud-to-cloud registration methods have generally been centered upon the use of a coarse-to-fine optimization strategy. The challenges and limitations inherent in this process have shaped the development of point cloud registration and the associated software tools over the past three decades. Based on the success of deep learning methods applied to imagery data, attempts at applying these approaches to point cloud datasets have received much attention. This study reviews and comments on more recent developments in point cloud registration without using any targets and explores remaining issues, based on which recommendations on potential future studies in this topic are made.</p> </abstract>

A point-feature label placement algorithm based on spatial data mining.

The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index.

Method for improving the accuracy of 3D light field interaction based on a small dataset of hand key points using an MLP network

Method for improving the accuracy of 3D light field interaction based on a small dataset of hand key points using an MLP network

Tracer-field cross-correlations with k-nearest neighbour distributions

ABSTRACT In astronomy and cosmology significant effort is devoted to characterizing and understanding spatial cross-correlations between points – e.g galaxy positions, high energy neutrino arrival directions, X-ray and AGN sources, and continuous fields – e.g. weak lensing meiand Cosmic Microwave Background maps. Recently, we introduced the k-nearest neighbour (kNN) formalism to better characterize the clustering of discrete (point) data sets. Here, we extend it to the point – field cross-correlations analysis. It combines kNN measurements of the point data set with measurements of the field smoothed at many scales. The resulting statistics are sensitive to all orders in the joint clustering of the points and the field. We demonstrate that this approach, unlike the 2-pt cross-correlation, can measure the statistical dependence of two data sets even when there are no linear (Gaussian) correlations between them. We further demonstrate that this framework is far more effective than the two point function in detecting cross-correlations when the continuous field is contaminated by high levels of noise. For a particularly high level of noise, the cross-correlation between haloes and the underlying matter field in a cosmological simulation, between 10 h−1 Mpc and 30 h−1 Mpc, is detected at >5σ significance using the technique presented here, when the two-point cross-correlation significance is ∼1σ. Finally, we show that kNN cross-correlations of haloes and the matter field can be well modelled on quasi-linear scales using the Hybrid Effective Field Theory (HEFT) framework, with the same set of bias parameters as are used for 2-pt cross-correlations. The substantial improvement in the statistical power of detecting cross-correlations using this method makes it a promising tool for various cosmological applications.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization

Selenite (Se4+) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (REselenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (ICselenite) (10 mg/L – 60 mg/L) and hydraulic retention time (HRT) (24 h – 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The REselenite was maximized while minimizing the HRT for a preferably higher range of ICselenite. Thus, the most favourable optimum conditions were suggested as: ICselenite – 50.45 mg/L and HRT – 24 h, resulting in REselenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the REselenite in an ALR, and the process parameters can be effectively optimized using PSO.

Hybrid optimization with unconstrained variables on partial point cloud registration

3D point cloud registration is a fundamental problem in computer vision (CV) and computer graphics (CG). Recently, a series of learning-based algorithms have been proposed to show the advantages in registration accuracy and inference speed. However, those learning-based methods usually ignore transformations with constrained rotations and translations in registration. In this paper, we propose a novel hybrid optimization method to solve the constrained rotational and translational transformations. A mapping function is introduced to deal with the restrained variables in optimization. Our method achieves superior performance on the Multi-View Partial Point dataset, which won the first place on the registration challenge in ICCV 2021. The method is also validated on the synthetic datasets ModelNet, ICL-NUIM, and the realistic 3DMatch dataset. We demonstrate that the global optimization methods still have great potential research for point cloud registration. The code is available at https://github.com/Dizzy-cell/HOUV.

Fitting a triaxial ellipsoid to a set of quasi-selenoidal points

Abstract The aim of this work is the determination of the parameters of the triaxial ellipsoid of the Moon, as derived from a quasi-selenoid model. After a detailed description of various quasi-selenoid models of the lunar gravity field, which were proposed in the last twenty years, we prepare suitable data sets of three-dimensional Cartesian coordinates. The mathematical model adopted is the general (polynomial) equation of an ellipsoid functionally related to the nine unknowns: the coordinates of the ellipsoid center, the three rotation angles and the three ellipsoid semiaxes. Furthermore, we adopt mathematical models for one special and two degenerate cases of the triaxial ellipsoid. We implement the least-squares method of indirect observations and we derive results for eighteen data sets of quasi-selenoidal points. From the results, we report the values of the semiaxes of the triaxial ellipsoid of fitting with three unknowns, for the model GL0660B, to be 1,738,256.3 ± 0.2 m, 1,738,023.1 ± 0.2 m and 1,737,603.2 ± 0.2 m, while the other unknowns remain insignificant. This triaxial ellipsoid leads to the improvement in the RMS value of the height anomaly at about 12 per cent in comparison to the oblate spheroid.

Semantic Segmentation of Substation Site Cloud Based on Seg-PointNet

3D point cloud semantic segmentation has been widely used in industrial scenes and has attracted continuous attention as a critical technology for understanding the intelligent robot scene. However, extracting visual semantics in complex environments remains a challenge. We propose the Seg-PointNet model based on multi-layer residual structure and feature pyramid for the LiDAR point cloud data semantic segmentation task in the complex substations scene. The model is based on the PointNet network and introduces a multi-scale residual structure. The residual structure multilayer perception (RES-MLP) model is proposed to fully excavate features at different levels and improve the characterization capabilities of complex features. Moreover, the 3D point cloud feature pyramid module is proposed to characterize the substation scene’s semantic features. We tested and verified the Seg-PointNet model on a self-built substation cloud point (SCP) dataset. The results show that the proposed Seg-PointNet model effectively improves the point cloud data segmentation accuracy, with an accuracy of 89.23% and mean intersection over union (mIoU) of 63.57%. This shows that the model can be applied to substation scenarios and provide technical support to intelligent robots in complex substation environments.

Detecting common features from point patterns for similarity measurement using matrix decomposition

ABSTRACT Similarity of point patterns is critical to geographic information retrieval. Many methods depend on measuring the similarity between point patterns within the spatial database. However, previous researches mainly focus on point density which is only one aspect of point patterns. A point distribution can be complex by its numerous alignments of point groups, which usually imply different geographical meanings in reality. In this paper, we propose a new method that uses image analysis techniques to comprehensively consider the characteristics of a point pattern. Specifically, given a set of point datasets falling in the same region, our method first generates the point intensity surfaces to transform the original vector space to raster space; then, the method constructs a matrix to describe all the pattern-related information. Finally, the point pattern similarity is calculated by decomposing this matrix into the lower-order representation and the factorized basis features. Due to the use of matrix decomposition, the proposed method has the merits that it can eliminate noises from the original data and assess the similarity of two patterns with emphasis on their major features. As a case study, our method is effective in discovering regularity from the taxi pick-up/drop-off point datasets.

GPU accelerated scalable parallel coordinates plots

Parallel coordinates are a powerful technique to visually analyze multi-parameter data, i.e., sets of datapoints with potentially many associated parameter values per datapoint. When these sets are large, line rendering becomes a severe performance bottleneck, and since many lines fall into the same pixel the numerical precision of the color buffer is quickly reached. We propose a scalable GPU realization of parallel coordinates building upon 2D pairwise attribute bins, to significantly reduce the number of lines to be rendered. Our approach comprises a GPU compute pipeline that combines shader-based scattering with atomic increment operations to efficiently count how often a line is drawn. These counts are then used to draw all pairwise sub-plots in the parallel coordinates plot, by analytically calculating the opacity for each count and rendering a line with end points determined by the 2D coordinates of the bin. In this way, framebuffer precision issues that are paramount in classical approaches can be overcome. We demonstrate the efficiency of the proposed realization for visualizing a weather forecast ensemble comprising 2.7 billion datapoints, each carrying 7 prognostic floating-point variables like temperature, precipitation and pressure, plus spatial and simulation input variables. We compare our pipeline to a rasterization-based approach regarding performance, and demonstrate interactive brushing at 4 s per frame at full HD viewport resolution.