Set Of Points Research Articles

3DMNDT: 3D Multi-View Registration Method Based on the Normal Distributions Transform

The normal distributions transform (NDT) is an effective paradigm for point set registration. This method was initially designed for pair-wise registration and suffers from the accumulated error problem when directly applied to multi-view registration. Under the framework of point-to-cluster correspondence, this paper proposes a novel multi-view registration method named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the k-means clustering and Lie algebra optimizer to achieve multi-view registration. More specifically, the multi-view registration is cast into the maximum likelihood estimation problem. Firstly, k-means clustering is utilized to divide all data points into different clusters, where one normal distribution is computed to locally model the probability of measuring a data point in each cluster. Subsequently, the multi-view registration problem is formulated by the NDT-based likelihood function. To maximize this likelihood function, the Lie algebra optimizer is introduced and developed to optimize each rigid transformation sequentially. 3DMNDT implements data point clustering, NDT computing, and rigid transformation optimization alternately until the desired registration results are obtained. Experimental results tested on benchmark data sets illustrate that 3DMNDT can achieve state-of-the-art performance for multi-view registration. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by solving the problem of registering multiple point sets. The normal distributions transform (NDT) is a well-known pair-wise registration method widely applied in the robotic domain. This paper extends the original NDT and proposes a novel registration method to simultaneously align more than two point sets. The multi-view registration is cast into the maximum likelihood estimation problem. Subsequently, the k-means clustering and Lie algebra optimizer are integrated to estimate registration parameters. Experimental results demonstrate its superior performance on the accuracy, efficiency, and robustness for multi-view registration of point sets.

Inbound traffic capture link-design problem independent of assumptions on users’ route choices

In some traffic management situations, a cordon (a set of points at which traffic flows into a given area) is set in a road network to establish a reference for the location of equipment to implement traffic measurements and controls (e.g., traffic volume surveys and congestion charging). However, few studies have focused on the optimum location of a cordon. We devise a problem denoted the inbound traffic capture link-design problem to select the optimum combination of links for inclusion in a cordon. We regard this combination as the minimum number of links that can capture traffic on all routes, under the condition that there is a path between nodes inside the cordon that is not captured. We formulate this model by employing the graph theory concept of the minimum cut, and use the concept of a Steiner tree with auxiliary network flows to express the constraint of ensuring that there is an uncaptured path inside a cordon. After a basic formulation, to obtain an identical cordon, we devise two subsidiary schemes. In addition, we perform a linear relaxation of our method to reduce its computational cost. The results of computational experiments confirm that our model selects the optimal cordon location formed by a combination of capturing links and also outputs an identical cordon as a boundary line of an area. As the model is computationally feasible, even when applied on a large network, we believe it will have a wide range of practical applications.

A Dual Evidential Top-View Representation to Model the Semantic Environment of Automated Vehicles

We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDAR and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDAR and stereo camera data recorded in real traffic scenarios. Our method estimates cell occupancy robustly and with a high level of detail while maximizing efficiency and minimizing the dependency to external processing modules.

Extraction of Tooth Cusps based on DBSCAN Density Clustering and Neighborhood Search Algorithm.

A good tooth cusp extraction is helpful in evaluating the effect of cosmetic dental work in virtual tooth surgery. We propose a new tooth cusp extraction, which integrates the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm with the neighborhood search algorithm to extract tooth cusp from a three-dimensional cloud-point tooth model. This method used the point cloud height and curvature to screen out the dented point set. Then we employ the DBSCAN clustering algorithm to segment different feature regions of the tooth surface and generate the candidate point set. Finally, the candidate point set was accurately located at the tooth apex through the neighborhood search algorithm and the traversal search method of non-maximum suppression. The experimental results show that the proposed method is superior to the traditional watershed algorithm-based methods by calculating the recall rate and accuracy rate, and also has higher extraction speed and extraction precision than manual extraction methods.

Multi-Modal Fusion Based on Depth Adaptive Mechanism for 3D Object Detection

Lidars and cameras are critical sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, accurate and robust fusion methods are still under exploration due to non-homogenous representations. In this paper, we find that the complementary roles of point clouds and images vary with depth. An important reason is that the point cloud appearance changes significantly with increasing distance from the Lidar, while the image's edge, color, and texture information are not sensitive to depth. To address this, we propose a fusion module based on the Depth Attention Mechanism (DAM), which mainly consists of two operations: gated feature generation and point cloud division. The former adaptively learns the importance of bimodal features without additional annotations, while the latter divides point clouds to achieve differential fusion of multi-modal features at different depths. This fusion module can enhance the representation ability of original features for different point sets and provide more comprehensive features by using the dual splicing strategy of concatenation and index connection. Additionally, considering point density as a feature and its negative correlation with depth, we build an Adaptive Threshold Generation Network (ATGN) to generate the depth threshold by extracting density information, which can divide point clouds more reasonably. Experiments on the KITTI dataset demonstrate the effectiveness and competitiveness of our proposed models.

Designing and constructing of a laboratory scale concrete roof tile dryer

There are some problems with the drying process in the concrete tiles local industry, especially due to uncertain weather, where the drying process relies on sunlight. In addition, roof tile dryers have not yet hit the market. A laboratory-scale dryer is needed to develop a full-scale concrete roof tile dryer. This research aims to design and manufacture a laboratory-scale concrete tile dryer. The dryer will be used to investigate the characteristics of the concrete roof drying. The design was carried out using the Pahl and Beitz method, in three stages, namely planning, conceptual design, and detailed design. In the planning stage, a requirement list was stated, in the conceptual design stage, selected tools are determined for every sub-function, while in the detail design stage, shop drawings were produced. To produce a better distribution of air velocity and temperature, at this detailed design stage, a Solidworks CFD numerical analysis was used. The dimension of the drier is 1600 x 1100 x 1100 mm³, with a capacity of 60 - 180 kg, with air velocity of 3 m/s and variable temperature. The drying chamber consists of 3 layers. The heat source is LPG flame and electric heater. The fresh air is flown to the LPG burner, through a circulating fan into the drying chamber, where the concrete roof tiles are situated. The roof tile dryer was constructed and tested. The test results show that the air velocity at the top layer accorded with the CFD result, while in the second layer and third layer of the drying chamber, the air velocity was relatively small. The temperature in each layer of the drying chamber shows the same value, in this case, the temperature for each layer is 38°C, while the set point at the thermostat was 40°C. The relative humidity of the drying air was 21.7% while the ambient air relative humidity was 52%.

Comparisons of standards for liquid flow rates under static load changes

Main textThis report presents the results of an inter-laboratory comparison (ILC) organized in order to determine the degrees of equivalence of standards for liquid static flow rates in the range of 50 kg/h to 36 000 kg/h under ambient pressure and temperature conditions. Water was used as the calibration fluid.The objective of the comparison was to support and prove the Calibration and Measurement Capabilities (CMC) in liquid flow of the participating NMIs and DIs CETIAT (France), PTB (Germany), FORCE (Denmark), DTI (Denmark), CMI (Czech Republic), RISE (Sweden), VTT (Finland), BEV (Austria), LEI (Lithuania), SMU (Slovakia) and UME TUBITAK (Turkey). The comparison was organized and evaluated by CETIAT, CMI and PTB as pilot laboratories. The comparison was carried out as a three-loop robin, which started in September 2020 at CETIAT and finished in June 2021, also at CETIAT.Two Coriolis mass flow meters were used as transfer standards. In order to estimate the uncertainties u TS, both transfer meters were subjected to characterization measurements at pilot laboratory CETIAT. The following parameters were researched in detail: fluid temperature, line pressure, reproducibility and the effect of zero setting. Following the results of the Chi2-test, one set point of one participant was discarded from the E N calculation.The comparison shows very good agreement between the 11 participating laboratories. Only one laboratory failed the comparison at two set points due to an E N value > 1.2. Two laboratories showed comparison results at "warning level". In order to follow the latest developments in the field of flow comparison evaluation, the standard procedure of COX (2002) was extended by an application of two additional criteria, as for inconclusive or conclusive decisions for calibration. In sum, four laboratories were affected by inconclusive calibration results.The comparison has received financial support from the EMPIR project 17IND13 Metrowamet.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Design of Buck-Boost Converter as A Voltage Stabilizer on Solar Power Plant at PPNS Baruna 01 Crewboat

Electrical energy is one of the most important basic human needs and is used in everyday life in various activities. Over time, the need for electricity will increase due to the increase and development of both the population, the amount of in-vestment and technological developments. The use of coal as the main fuel for power plants is also running low and its existence is not renewable. One solution to deal with these problems is the utilization of renewable energy. One type of re-newable energy that is environmentally friendly in order to meet the needs of electrical energy is solar energy. The existence of solar energy which is very abundant is one solution to reducing fossil fuels, which are currently running low. In PPNS Baruna 01 Crewboat the DC power source used comes from Solar Panels with a total of two pieces and each power is 300WP. As we know solar energy is fluctuating (up and down), therefore a DC-DC converter is needed so that the resulting voltage is stable. The DC-DC converter used in this research is a buck-boost converter. This buck-boost converter is designed with a set point of 14.4V which is then used for battery charging. The components used are 22.5μH inductor, 2275μF capacitor, resistor, diode and mosfet. The simulation results of the buck-boost converter in PSIM software show that the converter is able to work in two modes with an output voltage in accordance with the set point. When buck mode the input voltage is 24V to 14.4V and when boost mode the input voltage is 12V to 14.4V. From the simulation results, the buck-boost converter can be realized on the PLTS in the PPNS Baruna 01 Crewboat.

Rational real algebraic models of topological surfaces

Comessatti proved that the set of all real points of a rational real algebraic surface is either a nonorientable surface, or diffeomorphic to the sphere or the torus. Conversely, it is well known that each of these surfaces admits at least one rational real algebraic model. We prove that they admit exactly one rational real algebraic model. This was known earlier only for the sphere, the torus, the real projective plane and the Klein bottle.

Grouping Subspace Segmentation

This work studies the subspace segmentation problem. Given a set of data points which are drawn from a union of multiple subspaces. The goal of subspace segmentation is to cluster the data into the underlying subspaces from which the data are drawn from. The most recent works use spectral clustering based on the affinity matrix obtained by Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Least Squares Regression (LSR). They write each data point as a linear combination of all the data points, and use the representation coefficients to form the affinity matrix. LSR shows that it is more effective due to the grouping effect for data with high correlation. In this work, we propose the Grouping Subspace Segmentation (GSS) method by enhancing the grouping effect of correlated data points. The affinity graph is constructed to encode the local structure of data. Experiments on the real data sets show that the proposed method GSS is more effective than the state-of-the-art methods.

An atomic surface site interaction point description of non-covalent interactions.

Molecular electrostatic potential surfaces (MEPS) calculated using density functional theory have been used to develop a simplified description of the non-covalent interaction properties of organic molecules. The Atomic Interaction Point (AIP) model introduced here represents an evolution of the Surface Site Interaction Point (SSIP) model described previously, in which a molecule is represented by a discrete set of interaction points that define sites of interaction with other molecules. The interaction sites are described by interaction parameters that are equivalent to the experimentally determined H-bond donor and acceptor parameters α and β. By using high electron density MEPS that lie inside the van der Waals surface, it is possible to obtain accurate interaction parameters and locations for polar sites (s-holes, H-bond donors and acceptors), which are identified as local maxima and minima on the MEPS. For non-polar sites that represent π-systems and halogens, an approach based on molecular orbitals was used to assign the locations of the AIPs, and the interaction parameters were obtained using a lower electron density MEPS that lies close to the van der Waals surface. The AIP descriptions can be implemented directly in the Surface Site Interaction Point Model for Liquids at Equilibrium (SSIMPLE) to calculate solvation free energies, and the free energy of transfer of 1504 compounds from n-hexadecane to water was predicted with a root mean square error of 5 kJ mol-1. AIPs also provide a useful tool for mapping non-covalent interactions in intermolecular complexes, and examples are provided showing how X-ray crystal structures can be converted into AIP interaction maps that allow quantification of the free energy contributions of both polar and non-polar interactions to the stabilities of complexes in solution.

THE IMPACT OF GLOBAL DYNAMICS ON THE FRACTALS OF A QUADROTOR UNMANNED AERIAL VEHICLE (QUAV) CHAOTIC SYSTEM

In this paper, we have extended the concept of advanced Julia function for the discovery of new type of trajectories existing inside outer and inner wings. A dynamical system based on four rotors, referred to as quadrotor unmanned aerial vehicle (QUAV), is considered for the first time to seek the generation of extra wings using fractal theory. Moreover, we have used Julia and advanced Julia function techniques for the creation of outer and inner wings, respectively. Furthermore, the trajectories achieved using both techniques are analyzed and compared with the aid of basin of attraction and bifurcation diagram. This work covers a gap in the literature, that what is the impact of global dynamics on the hidden fractals. Therefore to fill this gap, sets of initial points are plotted from each basin constructed for fractals-based QUAV systems. Finally, numerical simulations are carried out using MATLAB, whereas the data generated for the corresponding basins are attached for the convenience of readers to reproduce each figure.

Study of computation of number of lines from set of collinear and non-collinear points in a given space

Study of computation of number of lines from set of collinear and non-collinear points in a given space

Differentially private outcome‐weighted learning for optimal dynamic treatment regime estimation

Precision medicine is a framework for developing evidence‐based medical recommendations that seeks to determine the optimal sequence of treatments, tailored to all of the relevant, observable patient‐level characteristics. Because precision medicine relies on highly sensitive, patient‐level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting. Outcome‐weighted learning (OWL) is a family of techniques for estimating optimal DTRs based on observational data. OWL techniques leverage support vector machine (SVM) classifiers in order to perform estimation. SVMs perform classification based on a set of influential points in the data known as support vectors. The classification rule produced by SVMs often requires direct access to the support vectors. Thus, releasing a treatment policy estimated with OWL requires the release of patient data for a subset of patients in the sample. As a result, the classification rules from SVMs constitute a severe privacy violation for those individuals whose data comprise the support vectors. This privacy violation is a major concern, particularly in light of the potentially highly sensitive medical data that are used in DTR estimation. Differential privacy has emerged as a mathematical framework for ensuring the privacy of individual‐level data, with provable guarantees on the likelihood that individual characteristics can be determined by an adversary. We provide the first investigation of differential privacy in the context of DTRs and provide a differentially private OWL estimator, with theoretical results allowing us to quantify the cost of privacy in terms of the accuracy of the private estimators.

CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.

Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been proved that exploring and embedding context knowledge in deep network can significantly improve accuracy. To emphasize these tips, we present CDT-CAD, i.e., context-aware deformable transformers for end-to-end chest abnormality detection on X-Ray images. CDT-CAD firstly constructs an iterative context-aware feature extractor, which not only enlarges receptive fields to encode multi-scale context information via dilated context encoding blocks, but also captures unique and scalable feature variation patterns in wavelet frequency domain via frequency pooling blocks. Afterwards, a deformable transformer detector on the extracted context features is built to accurately classify disease categories and locate regions, where a small set of key points are sampled, thus leading the detector to focus on informative feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det 10 Datasets, CDT-CAD demonstrates its effectiveness in recognizing chest abnormities and outperforms 1.4% and 6.0% than the existing methods in AP50 and AR on VinBig dateset, and 0.9% and 2.1% on Chest Det-10 dataset, respectively.

A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au

Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a data set of 3-minute averaged sequential data points of the solar wind’s instantaneous bulk fluid plasma properties using about 20 years of measurements from Wind. We label samples by the presence and type of MFRs containing them using a catalog based on Grad–Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an area under the receiver-operator curve (AUC) of 94% and SMFRs with an AUC of 89%, and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the 〈B〉 > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind. We also suggest that they should be considered a salient feature of the solar wind’s magnetic structure rather than transient events.

Norest-Net: Normal Estimation Neural Network for 3-D Noisy Point Clouds.

The widely deployed ways to capture a set of unorganized points, e.g., merged laser scans, fusion of depth images, and structure-from- x , usually yield a 3-D noisy point cloud. Accurate normal estimation for the noisy point cloud makes a crucial contribution to the success of various applications. However, the existing normal estimation wisdoms strive to meet a conflicting goal of simultaneously performing normal filtering and preserving surface features, which inevitably leads to inaccurate estimation results. We propose a normal estimation neural network (Norest-Net), which regards normal filtering and feature preservation as two separate tasks, so that each one is specialized rather than traded off. For full noise removal, we present a normal filtering network (NF-Net) branch by learning from the noisy height map descriptor (HMD) of each point to the ground-truth (GT) point normal; for surface feature recovery, we construct a normal refinement network (NR-Net) branch by learning from the bilaterally defiltered point normal descriptor (B-DPND) to the GT point normal. Moreover, NR-Net is detachable to be incorporated into the existing normal estimation methods to boost their performances. Norest-Net shows clear improvements over the state of the arts in both feature preservation and noise robustness on synthetic and real-world captured point clouds.

A Central Local Metric Dimension of Generalized Fan Graph, Generalized Broken Fan Graph, and Cm ⊙ K¯m

The central local metric dimension is a new variation of local metric dimension that introduced in 2023. The central local metric dimension is a new concept that enriches research studies in graph theory, especially in the field of metric dimension. This concept combines the concept of local metric dimension by involving central points in the local metric set so that the existence of central points can strengthen the position of the local metric set in distinguishing every two neighboring points in a graph. The methodology of this research is study literature and observation. We find the central vertex of each graph and also find the local metric set of its graph, then we applied it to the related theorem to find the lower bound of central local metric dimension. Let 𝐺 be a connected graph with order 𝑛 and vertex set is 𝑉(𝐺). A subset 𝑊={𝑥1,𝑥2,…,𝑥𝑘}⊆𝑉(𝐺) is a local metric set of graph 𝐺 if the metric code of every two adjacent vertices 𝑢,𝑣 in 𝐺 are 𝑟(𝑢|𝑊)≠𝑟(𝑣|𝑊), where 𝑟(𝑢|𝑊)=(𝑑(𝑢,𝑥1),𝑑(𝑢,𝑥2),…,𝑑(𝑢,𝑥𝑘)) and 𝑟(𝑣|𝑊)=(𝑑(𝑣,𝑥1),𝑑(𝑣,𝑥2),…,𝑑(𝑣,𝑥𝑘)). A vertex 𝑥∈𝑉(𝐺) is a central vertex in 𝐺 if 𝑥 have the the shorthest distance to the another all vertices in 𝐺. If 𝑊 consist of all central vertices in 𝐺, then 𝑊 is called a central local metric set of 𝐺. The minimal cardinality of 𝑊 is called a central basis local set of 𝐺 and its cardinality is called central local metric dimension of 𝐺 or denoted by 𝑙𝑚𝑑𝑠(𝐺). In this paper we explored the central local metric dimension on generalized fan graph, generalized broken fan graph, and a graph resulting from corona operation Cm ⊙ K¯n. The result show that the central local metric dimension of generalized fan graph is equal with its order because the diameter and radius are equal. Different with it, the central local metric dimension of generalized broken fan is equal with its local metric dimension plus cardinality of central set, and the central local metric dimension of Cm ⊙ K¯n are equal with the central local metric dimension of 𝐶𝑚.

Memory loss can prevent chaos in games dynamics.

Recent studies have raised concerns on the inevitability of chaos in congestion games with large learning rates. We further investigate this phenomenon by exploring the learning dynamics in simple two-resource congestion games, where a continuum of agents learns according to a simplified experience-weighted attraction algorithm. The model is characterized by three key parameters: a population intensity of choice (learning rate), a discount factor (recency bias or exploration parameter), and the cost function asymmetry. The intensity of choice captures agents' economic rationality in their tendency to approximately best respond to the other agent's behavior. The discount factor captures a type of memory loss of agents, where past outcomes matter exponentially less than the recent ones. Our main findings reveal that while increasing the intensity of choice destabilizes the system for any discount factor, whether the resulting dynamics remains predictable or becomes unpredictable and chaotic depends on both the memory loss and the cost asymmetry. As memory loss increases, the chaotic regime gives place to a periodic orbit of period 2 that is globally attracting except for a countable set of points that lead to the equilibrium. Therefore, memory loss can suppress chaotic behaviors. The results highlight the crucial role of memory loss in mitigating chaos and promoting predictable outcomes in congestion games, providing insights into designing control strategies in resource allocation systems susceptible to chaotic behaviors.

Generalized 3-D Rigid Point Set Registration With Bidirectional Hybrid Mixture Models

Generalized 3-D Rigid Point Set Registration With Bidirectional Hybrid Mixture Models