Large Number Of Points Research Articles

Recurrence analysis of 3D point clouds for the detection of surface anomalies in advanced manufacturing

Due to tool wear, machine malfunction, and other internal and external factors, surface anomalies are widely observed in diverse manufacturing processes, such as machining, stamping, and additive manufacturing. The detection of surface anomalies remains an important challenge for assuring the quality and reliability of manufacturing products. Conventional image-based approaches lack the ability to fully capture the 3-dimensional (3D) morphologies of surface defects, which hinder their applications in defect identification, characterization, and diagnosis. The emerging 3D sensing technologies, such as laser or structured-light scanning, enable the collection of point clouds, which embodies a comprehensive representation of defect-altered surface morphologies in 3D space. However, scanned point cloud data are unstructured and oftentimes contain large numbers of points, which pose significant challenges for the recognition of defective surface patterns. To address the challenges, this study develops a recurrence-based approach for the representation learning of 3D point clouds to detect surface defects in manufactured products. Specifically, with a scanned point cloud, key points are first extracted using a voxel-based sampling strategy, and each key point and its neighboring points form a localized sub-region. Then, a recurrence network is generated through a tailored approach, which considers both the morphology similarity and spatial closeness of sub-regions. For anomaly detection, a tensor-based one-class classification model is further deployed to distinguish abnormal surface patterns from normal ones. The developed methodology is experimentally evaluated through simulation and real-world case studies. Experimental results have demonstrated that the developed methodology outperforms benchmarks, highlighting its efficacy in the detection of surface anomalies.

Filtering of 3D point clouds using maximum likelihood algorithm

Recently, the 3D point cloud (PC) has become more popular as an innovative object representation. However, there is usually noise and outliers in the raw point cloud. It is essential to eliminate the noise from the point cloud and outlier data while maintaining the features and finer details intact. This paper presents a comprehensive method for filtering and classification point clouds using a maximum likelihood algorithm (ML). TOPCON GLS-2000 3D terrestrial laser scanners (TLS) have been used to collect the 3D PC data set; the scan range is up to 350 m. About 30 m apart from the study area. ScanMaster software has been used to import, view, and filter point cloud information. The position information of the points is linked with the training point cloud and the filtered point cloud to derive the nonlinear model using MATLAB software. To evaluate the quality of the denoising results, two error metrics have been used: the average angle (δ) and distance (Dmean) between the ground truth point and the resulting point. The experimental findings demonstrate that the suggested approach can effectively filter out background noise while improving feature preservation. The filtering and classifying technique is more effective and efficient compared to the selected filtering methods when applied to 3D point clouds containing a large number of points and a variety of natural characteristics.

Sufficient Conditions for Central Limit Theorems and Confidence Intervals for Randomized Quasi-Monte Carlo Methods

Randomized quasi-Monte Carlo methods have been introduced with the main purpose of yielding a computable measure of error for quasi-Monte Carlo approximations through the implicit application of a central limit theorem over independent randomizations. But to increase precision for a given computational budget, the number of independent randomizations is usually set to a small value so that a large number of points are used from each randomized low-discrepancy sequence to benefit from the fast convergence rate of quasi-Monte Carlo. While a central limit theorem has been previously established for a specific but computationally expensive type of randomization, it is also known in general that fixing the number of randomizations and increasing the length of the sequence used for quasi-Monte Carlo can lead to a non-Gaussian limiting distribution. This paper presents sufficient conditions on the relative growth rates of the number of randomizations and the quasi-Monte Carlo sequence length to ensure a central limit theorem and also an asymptotically valid confidence interval. We obtain several results based on the Lindeberg condition for triangular arrays and expressed in terms of the regularity of the integrand and the convergence speed of the quasi-Monte Carlo method. We also analyze the resulting estimator’s convergence rate.

Gravitational wave imprints of the doublet left-right symmetric model

We study the stochastic gravitational wave (GW) background resulting from the strong first-order phase transition (SFOPT) associated with SU(2)R×U(1)B−L-breaking in the doublet left-right symmetric model (DLRSM). For different values of the symmetry-breaking scale vR=20, 30, and 50 TeV, we construct the one-loop finite temperature effective potential to explore the parameter space for regions showing SFOPT. We identify the region where the associated GW background is strong enough to be detected at planned GW observatories. A strong GW signature favors a relatively light CP-even neutral scalar H3, arising from the SU(2)R doublet. The SU(2)L subgroup of DLRSM is broken by three vacuum expectation values: κ1, κ2, and vL. We observe a preference for O(1) values of the ratio w=vL/κ1, but no clear preference for the ratio r=κ2/κ1. A large number of points with strong GW signal can be ruled out from precise measurement of the trilinear Higgs coupling and searches for H3 at the future colliders. Published by the American Physical Society 2024

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains.

Non-reflective hard source method for multiple physically extended sources and scattering bodies

In this paper, we focus on methodologies to inject a noise source in a numerical model of noise propagation in confined domains. This is a problem of primary importance when dealing with propagation of fluid-dynamic induced noise in confined basins, like ships at sea or wind farms. We first assess the performance of the literature hard source (HS) and transparent source methods; successively, we propose a novel method named the non-reflective HS (NRHS) method. It takes advantage of the linearity of the equation governing the propagation of acoustic waves in fluids and is based on the decomposition of the total signal in the sum of direct and reflected signals. It presents the advantages of the hard source method removing the main drawback consisting of the well-known problem of spurious reflections. To check the reliability of the HS vs the NRHS, a non-dimensional parameter (the encumbrance) has been defined, which gives a measure of the extension of the generation domain with respect to the propagation domain in relation to the principal wavelength of the acoustic waves and the presence of reflecting surfaces. The method herein developed gives accurate results in the case of a single-point source, where the literature methods behave well; more importantly, the NRHS method maintains its own accuracy when a noise source needs to be represented by a large number of points in space, situations of very practical importance where the standard methods may exhibit inaccuracy. This is a point of importance since the use of large generation domains is in favor of the accuracy of the source characterization, which can exhibit a complex directivity. The new method has been tested in a number of archetypal situations characterized by the presence of a reflecting plane, a scattering body close to the source location, and two sources placed side by side. In all cases, the method has shown its own superiority with respect to the standard HS method, still preserving the flexibility and simplicity of the latter.

Fast Radius Outlier Filter Variant for Large Point Clouds

Currently, several devices (such as laser scanners, Kinect, time of flight cameras, medical imaging equipment (CT, MRI, intraoral scanners)), and technologies (e.g., photogrammetry) are capable of generating 3D point clouds. Each point cloud type has its unique structure or characteristics, but they have a common point: they may be loaded with errors. Before further data processing, these unwanted portions of the data must be removed with filtering and outlier detection. There are several algorithms for detecting outliers, but their performances decrease when the size of the point cloud increases. The industry has a high demand for efficient algorithms to deal with large point clouds. The most commonly used algorithm is the radius outlier filter (ROL or ROR), which has several improvements (e.g., statistical outlier removal, SOR). Unfortunately, this algorithm is also limited since it is slow on a large number of points. This paper introduces a novel algorithm, based on the idea of the ROL filter, that finds outliers in huge point clouds while its time complexity is not exponential. As a result of the linear complexity, the algorithm can handle extra large point clouds, and the effectiveness of this is demonstrated in several tests.

Derivation of septic B-spline function in n-dimensional to solve n-dimensional partial differential equations

Abstract In this study, a new structure for the septic B-spline collocation algorithm in n-dimensional is presented as a continuation of generating B-spline functions in n-dimensional to solve mathematical models in n-dimensional. The septic B-spline collocation algorithm is displayed in three forms: one dimensional, two dimensional, and three dimensional. In various domains, these constructs are essential for solving mathematical models. The effectiveness and correctness of the suggested method are demonstrated using a few two- and three-dimensional test problems. The proposed new structure provides better results than other methods because it deals with a larger number of points than the field. To create comparisons, we use different numerical approaches accessible in the literature.

COMPARISON OF OPTIMIZATION STRATEGIES AND ESTIMATION TECHNIQUES FOR RADIO NETWORK PLANNING AND OPTIMIZATION PROBLEMS

Background. Radio network planning is one of the main phases of the cellular network lifecycle, as it determines capital and operating costs and allows system performance evaluation at any given time. An accurate and comprehensive analysis of existing network statistics is necessary for proper cell planning during network expansion. These statistics are collected throughout the life cycle of the cellular network and usually have certain imperfections (heterogeneity of statistics, which have different densities in different parts of the search space, up to the presence of significant voids, etc.) The system describing the functioning of the radio network can be represented as a black box because its internal processes are too complex to be defined by mathematical functions. This determines the need to use appropriate tools.
 Objective. The purpose of the paper is to create a toolkit that allows finding the proper relationships between network parameters to define target values that will help to build an effective network plan in terms of performance and costs for its creation and operation. The tools should be able to work efficiently using the minimum set of available statistical data, as well as taking into account their imperfections.
 Methods. Mathematical estimation and optimization methods are used, namely Ordinary Least Squares, Ridge Regression, Lasso, Elastic-net, LARS lasso, Bayesian Ridge Regression, Automatic Relevance Determination, Stochastic gradient descent, Theil-Sen estimator, Huber Regression, Quantile regression, Polynomial regression. We consider 12 estimation methods in combination with two optimization strategies. Additionally, the method of partial analysis of the search space with different number of configurations is considered.
 Results. A software package using the Python programming language has been created, which contains a practical implementation of all the considered estimation and optimization methods, as well as tools for evaluating arbitrary configurations of the software package (benchmark) and visualizing the results. The best estimation method is Ordinary Least Squares for finding the optimal configuration of the statistical parameters of the 4G radio network to maximize the download speed. To obtain satisfactory results, it is enough to consider 25 initial and 250 estimated points - a larger number of points will not significantly increase prediction accuracy.
 Conclusions. The results indicate the possibility of using the created software package for radio network planning tasks. Further research is aimed at expanding the created software package's functionality and considering additional estimation methods and optimization strategies.
 Keywords: radio network planning; radio network optimization; cellular networks; estimation techniques; optimization strategies; big data; 2G; 3G; 4G; CQI; capacity.

Application of normal cones to the computation of solutions of the nonlinear Kolmogorov backward equation

A numerical approach to computing solutions of a generalized Kolmogorov backward equation is proposed, in which the stochastic matrix is replaced by a nonlinear operator obtained as the lower bound of a set of stochastic matrices. The equation is central to the theory of imprecise Markov chains in continuous time, which has made rapid progress in recent years. One of the obstacles to its implementation remains the high computational complexity, with the prevailing existing approaches relying on a discretization of the time interval. In order to achieve sufficient accuracy of the approximations, the grid must typically contain a large number of points on which optimization steps are performed, usually using linear programming. The main goal of this work is to develop a new, more efficient approach by significantly reducing the number of optimization steps required to achieve the prescribed accuracy of the solutions. Our approach is based on the Lipschitz continuity of the solutions of the equation with respect to time, which results in the optimization problems occurring at nearby points of the time interval having similar optimal solutions. This property is exploited using the theory of normal cones of convex polytopes. If the solution vectors remain within the same normal cone of a polytope corresponding to the nonlinear operator in a given interval, the optimization problem to be solved becomes linear, which allows much faster computations. This paper is primarily concerned with providing the theoretical basis for the new technique. However, initial tests show that it significantly outperforms existing methods in most cases.

Replacing Histogram with Smooth Empirical Probability Density Function Estimated by K-Moments

Whilst several methods exist to provide sample estimates of the probability distribution function at several points, for the probability density of continuous stochastic variables, only a gross representation through the histogram is typically used. It is shown that the newly introduced concept of knowable moments (K-moments) can provide smooth empirical representations of the distribution function, which in turn can yield point and interval estimates of the density function at a large number of points or even at any arbitrary point within the range of the available observations. The proposed framework is simple to apply and is illustrated with several applications for a variety of distribution functions.

Ocelot, Leopardus pardalis (Mammalia, Carnivora, Felidae), home range in the Lowland Atlantic Forest of Southeastern Brazil

The ocelot is an important Neotropical mesopredator and information on its spatial ecology remains scarce. Here we estimated the ocelot home range in a remnant of Lowland Atlantic Forest in Southeastern Brazil. The data were collected by camera traps installed at eight known ocelot latrines. We estimated the home range both based on the Minimum Convex Polygon (MCP) and the 95% adaptive Kernel density estimator (95%K) to compare with other published studies. We identified 22 ocelots (adult males = 8; adult females = 12; cubs = 2). Six males were recorded at more than one latrine, while all females were recorded at only one sampling point. In addition to male ocelots being recorded at a large number of points, they showed greater intrasexual spatial overlap as they used the same latrines, suggesting larger home ranges than females. The mean home range size for males was 12.1 ± SE 4.4 km2(range = 6.2 to 20.8 km2) using MCP, and 19.9 ± SE 9.5 km2(range = 10.1 to 38.9 km2) applying 95%K. Despite our estimates representing an approximation of the total area used by males, both values are consistent with those reported from other locations. Our data complemented the gradient of vegetation type sampled for ocelots in Atlantic Forest and support the suggestion that this environmental variable and, consequently, its effect on prey availability, influence the home range size of ocelot. Information on population ecology and other spatial ecology data are also presented.

Development of look-ahead NURBS interpolation based on the exponential function

Aiming at the problem of large number of points and complex calculation of NURBS pre-interpolation, this paper puts forward a look-ahead interpolation with offline feedrate optimization. The NURBS interpolator is divided into two stages: pre-interpolation and real-time interpolation. The pre-interpolation preprocesses the curve segment by segment according to the curvature characteristics, at the same time the exponential function method is adopted during the pre-interpolation in the look-ahead module. This method makes the step increment of interpolation parameters change exponentially in the area with gentle curvature, and greatly reduces the number of pre-interpolation points, which reduces the amount of calculation and improves the real-time performance. The real-time interpolation stage adopts the bidirectional adaptive acceleration and deceleration control method to realize speed smoothing without needing to calculate the deceleration point. The simulation results show that the real-time performance of the algorithm is greatly improved.

Modify Self-Attention via Skeleton Decomposition for Effective Point Cloud Transformer

Although considerable progress has been achieved regarding the transformers in recent years, the large number of parameters, quadratic computational complexity, and memory cost conditioned on long sequences make the transformers hard to train and implement, especially in edge computing configurations. In this case, a dizzying number of works have sought to make improvements around computational and memory efficiency upon the original transformer architecture. Nevertheless, many of them restrict the context in the attention to seek a trade-off between cost and performance with prior knowledge of orderly stored data. It is imperative to dig deep into an efficient feature extractor for point clouds due to their irregularity and a large number of points. In this paper, we propose a novel skeleton decomposition-based self-attention (SD-SA) which has no sequence length limit and exhibits favorable scalability in long-sequence models. Due to the numerical low-rank nature of self-attention, we approximate it by the skeleton decomposition method while maintaining its effectiveness. At this point, we have shown that the proposed method works for the proposed approach on point cloud classification, segmentation, and detection tasks on the ModelNet40, ShapeNet, and KITTI datasets, respectively. Our approach significantly improves the efficiency of the point cloud transformer and exceeds other efficient transformers on point cloud tasks in terms of the speed at comparable performance.

Enhancement Efficiency of Calculation of the Phonon Images for GaP Piezoelectric Crystal

In this paper an improved phonon imaging for GaP has been calculated, this crystal is one of III-V group. In the presence of piezoelectric properties, the computation treatment is based on increasing the number of points in the reduced Brillouin zone and increasing the size of the phonon image matrix. The phonon images can be calculated by large number of points in the reduced Brillouin zone which represent the wave vectors and these vectors can be transformed to group velocity vectors using Christoffel equation, these set of group velocity vectors can be projected in a certain direction to form the phonon image. A program was prepared in MATLAB language to calculate these images by determining the number of points in the reduced zone and determining the size of the matrix that represents the phonon images in addition to the direction of the projections. To calculate the phonon images in the presence of the effect of the piezoelectric properties, it is through the presence of the k44 factor, which indicated of the influence of the piezoelectric properties on the phonon images. The results showed that the size of the image matrix is more effective factor than the number of points in the reduced Brillouin zone especially when the number of points is greater than 8×〖10〗^5 points, and when the number of points less than 〖10〗^4 points all the obtained phonon images is not clear.

Effects of icon arrays to communicate risk in a repeated risky decision-making task

Abstract In two experiments, participants decided on each of several trials whether or not to take a risk. If they chose to take the risk, they had a relatively high probability (from 75% to 95%) of winning a small number of points and a relatively low probability (5% to 25%) of losing a large number of points. The loss amounts varied so that the expected value of taking the risk was positive on some trials, zero on others, and negative on the rest. The main independent variable was whether the probability of losing was communicated using numerical percentages or icon arrays. Both experiments included random icon arrays, in which the icons representing losses were randomly distributed throughout the array. Experiment 2 also included grouped icon arrays, in which the icons representing losses were grouped at the bottom of the array. Neither type of icon array led to better performance in the task. However, the random icon arrays led to less risk taking than the numerical percentages or the grouped icon arrays, especially at the higher loss probabilities. In a third experiment, participants made direct judgments of the percentages and probabilities represented by the icon arrays from Experiment 2. The results supported the idea that random arrays lead to less risk taking because they are perceived to represent greater loss probabilities. These results have several implications for the study of icon arrays and their use in risk communication.

The course of postpsychotic depressions in adolescence

To identify psychopathological features of postpsychotic depressions formed within the achievement of remission after the first psychotic episode in adolescence. Fifty-six male patients of adolescent age (average age 19.7±2.5 years) with postpsychotic depression (F20, F25) were enrolled. The study used clinical-psychopathological, psychometric and statistical methods. HDRS-21 was used for psychometric assessment of depressive symptoms, and suicidal risk was assessed using SSI. There were two types of post-psychotic depression: type 1 with a positive affectivity (58.93%, n=33) and type 2 with negative affectivity (41.07%, n=23). Significant differences in the structure of the first psychosis were found (χ2=7.8; p=0.02). In the case of depression type 1, the HDRS-21 score was 17.49±7.49 points with the largest number of points on the sub-scales «low mood» (2.81±0.83) and «mental anxiety» (2.88±0.45). The SSI average score was 7.81±6.46. Anti-vital thinking was noted by 75.76% (n=25). In the case of depression type 2, the HDRS-21 average score was 23.68±9.24 points (the largest number of points was fixed in the sub-scales «low mood» - 2.44±0.73, «work and activity» - 3.19±0.89). The SSI average score was 12.30±8.47. The study showed the heterogeneity of the course of postpsychotic depressions that occur after a manifest psychotic episode of adolescence. Differences in the structure of depression were identified, which are important for therapeutic tactics and prognosis of the disease.

Pengembangan Prototipe Aplikasi Kurir Antarjemput Kotak Amal LazisMu Umbulharjo Menggunakan Metode User Centered Design

LazisMu Umbulharjo has quite a number of social activities, including raising funds through charity boxes to several points, such as places of worship, community services, and Muhammadiyah residents in the Umbulharjo area and its surroundings. LazisMu Umbulharjo has managed at least 900 charity boxes spread across 7 sub-districts of the Umbulharjo area and also outside the Umbulharjo area. Currently, the courier who shuttles the charity box is guided by the address that is packaged in a printed sheet that comes from Excel data. Some of the difficulties experienced by couriers when picking up charity boxes at these 900 points, among others, the large number of points that must be visited by one courier. Couriers have difficulty memorizing the positions of the hundreds of charity boxes. Courier difficulties and often missed on the way to the points of the charity box. Problems occur if the staff replaces them with new ones so that adjustments are made from scratch again in learning the pick-up location. This research was conducted based on the stages in the User Centered Design (UCD) process. Plan the human centered process, specify the context of use, specify user and organizational requirements, product design solutions, and evaluate design against user requirements. Research has produced a prototype of the LazisMu Umbulharjo application with the main features of a dashboard for managing data and general information and managing a charity box. User management, charity box location management, and finally the determination of the pick-up route. The application prototype has been tested for usability and calculated using the System Usability Scale (SUS) with an average score of 82. Based on this score, the prototype of the LazisMu Umbulharjo application can be accepted by users (ACCEPTABLE) with grade A and gets an EXCELLENT level

Digitalized continuous learning system in the company: organizational aspect

Information and technical support of the personnel management system is gaining more and more distribution, informatization is carried out in virtually all modern companies without exception. As a rule, in most companies, they primarily automate the functions of personnel records management and administration. These areas are associated with the storage and processing of information about employees: personal files, work books, employment contracts, data on the movement of personnel, payroll. Information technologies can be used not only for processing large amounts of data, but also for their analysis.Recently, the use of artificial intelligence in recruiting has become widespread; such technologies significantly reduce the time spent on the selection of candidates. Artificial intelligence is used at the initial stage of recruiting calling candidates, which allows the HR manager not to miss a promising specialist from the stream of vacancies. At the same time, there is a trend towards automating more complex HR functions such as employee assessment, development and training. In this study, the sought-after directions for improving the information and technical support of the personnel management service were highlighted: artificial intelligence, continuous learning, big data. The hierarchy analysis method based on expert judgment determined the criteria that guided the group of experts when choosing the most promising direction with growth potential. According to the results of the analysis, the largest number of points was received by the direction continuous learning in the format of electronic distance learning 56.1 %. Further, a technological scheme for information and technical support of the personnel management service was developed. The process of introducing distance learning in the company is described, the main items of expenses, the departments responsible for the implementation of this system, the preparation of the necessary regulatory documentation, the choice of software, the costs of administration of the system are given.

Multilevel Techniques for the Solution of HJB Minimum-Time Control Problems

The solution of minimum-time feedback optimal control problems is generally achieved using the dynamic programming approach, in which the value function must be computed on numerical grids with a very large number of points. Classical numerical strategies, such as value iteration (VI) or policy iteration (PI) methods, become very inefficient if the number of grid points is large. This is a strong limitation to their use in real-world applications. To address this problem, the authors present a novel multilevel framework, where classical VI and PI are embedded in a full-approximation storage (FAS) scheme. In fact, the authors will show that VI and PI have excellent smoothing properties, a fact that makes them very suitable for use in multilevel frameworks. Moreover, a new smoother is developed by accelerating VI using Anderson’s extrapolation technique. The effectiveness of our new scheme is demonstrated by several numerical experiments.