This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual...
Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant c...
Ensuring that ground point density after raw point cloud processing meets the accuracy requirements for subsequent DEM construction represents a challenge for field operators during airborne LiDAR data acquisition. In this study, we propose a method...
In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density...
The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajector...
The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms alwa...
DBSCAN and DPC are typical density-based clustering algorithms. These two algorithms have their drawbacks, such as difficulty in clustering when there are significant differences in density between clusters. This study proposes a clustering algorithm...
Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its n...
The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to...
In traditional density peak clustering, when the density distribution of samples in a dataset is uneven, the density peak points are often concentrated in the region with dense sample distribution, which is easy to affect clustering accuracy. Under t...
As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points...
Rock failure phenomena are accompanied by abundant energy variation, and the energy dissipation can explain the dynamic mechanical characteristics of the rock. In this study, a series of granite specimens (a total of 60) with different aspect ratios...
In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as...
Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing...
Density peak clustering (DPC) is popular in unsupervised clustering due to its intuitive theory and the absence of pre-defined cluster numbers. However, traditional DPC relies on a static single computation strategy. It uses all raw samples to calcul...
Density peaks clustering (DPC) algorithm can process data of any shape and is simple and intuitive. However, the distance between any two high-dimensional points tends to be consistent, which makes it difficult to distinguish the density peaks and ea...
Density Peaks Clustering (DPC) has recently received much attention in many fields by reason of its simplicity and efficiency. Nevertheless, empirical studies have shown that DPC has some shortfalls: (i) similarity measurement based on Euclidean dist...
Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly import...
Plant diseases and pests may seriously affect the yield of crops and even threaten the survival of human beings. The characteristics of plant diseases and insect pests are mainly reflected in the occurrence of lesions on crop leaves. Machine vision d...
The puncture force during sewing is a critical factor affecting sewing quality. In this study, the puncture process is divided into five stages, a mechanical model of the puncture process is established, and a quantitative expression is achieved. Usi...
This paper proposes ParDP, an algorithm and concrete tool for unsupervised clustering, which belongs to the class of density peaks-based clustering methods. Such methods rely on the observation that cluster representative points (centroids) are point...
ScMN2-type (M = V, Nb, Ta) phases are layered materials that have been experimentally reported for M = Ta and Nb. They are narrow-bandgap semiconductors with potentially interesting thermoelectric properties. Point defects such as dopants and vacanci...
Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the co...
In order to cope with data with an irregular shape and uneven density, this paper proposes a two-phase clustering algorithm based on detecting the peaks of dimensional density and the degree of interaction between clusters (CPDD-ID). In the partition...
The crucial feature of seersucker woven fabrics is their surface geometry. In such kinds of woven fabrics, the puckered stripes appear in the warp direction, separated by stripes of smooth fabric. There are practically no methods to assess the seersu...
The horizontal storm structure surrounding 92,512 lightning-ignited wildfires is examined in the mid to eastern sections of the United States from 2003 to 2015 using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV g...
Leaders of subsequent strokes in negative cloud-to-ground lightning are known to produce X-ray/gamma-ray emissions detectable at distances of a few kilometers or less from the lightning channel. These leaders usually develop in decayed but still warm...
In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environm...
Metal sheets have good performance and have been widely used. Different kinds of defects can be generated during the preparation and service of metal plates, which will cause the structural performance of the metal plates to decline, thus requiring s...
Kernel density estimation is a non-parametric method to estimate the probability density function of a random quantity from a finite data sample. The estimator consists of a kernel function and a smoothing parameter called the bandwidth. Despite its...
Clustering methods in data mining are widely used to detect hotspots in many domains. They play an increasingly important role in the era of big data. As an advanced algorithm, the density peak clustering (DPC) algorithm is able to deal with arbitrar...
In order to obtain a high microscopic and spectral resolution, both for the microscopic study and for the spectrometric analysis carried out simultaneously at the same area of the sample, an adaptive optoelectronic system for Raman spectromicroscopes...
Cluster analyses are often conducted with the goal to characterize an underlying probability density, for which the data-point density serves as an estimate for this probability density. We here test and benchmark the common nearest neighbor (CNN) cl...
Point clouds acquired by LiDAR typically contain a large number of outliers, which can substantially degrade downstream processing. Existing outlier removal methods suffer from significant performance degradation on point clouds with high outlier rat...
To overcome the challenges of processing soft-brittle potassium dihydrogen phosphate (KDP) crystals, this study proposes a back-propagation (BP) neural network model for the rapid prediction of the ion beam removal function using Faraday cup scanning...
Background: Different methods are established for the changes in aortic valve stenosis with cardiac computed tomography angiography (CCTA), but the effect of the grade of stenosis on contrast densities around the valve has not been investigated. Aims...
Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies...
This study proposes an enhanced physics-informed neural network (PINN) framework for predicting seepage fields around deeply buried tunnels with limited field measurements. Hard-constrained boundary enforcement via distance-function trial functions i...
The use of energy storage with high power density and fast response time at container terminals (CTs) with a power demand of tens of megawatts is one of the most critical factors for peak reduction and economic benefits. Peak shaving can balance the...
This article provides insights into the optical signatures of plastic litter based on a published laboratory-scale reflectance data set (350–2500
Airborne bathymetric LiDAR (ABL) acquires waveform data with better accuracy and resolution and greater user control over data processing than discrete returns. The ABL waveform is a mixture of reflections from the water surface and bottom, water col...
In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrat...
Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the n...
This study utilizes taxi trajectory data to uncover urban residents’ travel patterns, offering critical insights into the spatial and temporal dynamics of urban mobility. A fusion clustering algorithm is introduced, enhancing the clustering acc...
In this work, the advantages of applying the temperature and pressure replica-exchange method to investigate the phase transitions and the hysteresis for liquid-crystal fluids were demonstrated. In applying this method to the commonly used Hess–Su li...
The 3D modeling of urban buildings has become a common research area in various disciplines such as photogrammetry and computer vision, with different applications such as intelligent city management, navigation of self-driving cars and architecture,...
Integrating a solar water heater (SWH) with a phase change material (PCM)-based latent heat storage is an attractive method for transferring load from peak to off-peak hours. This transferring load varies as the physical parameters of the PCM change....
To elucidate the microscopic evolution characteristics of atmospheric-pressure air dielectric barrier discharge (DBD) under different voltage amplitudes, this study systematically investigates the discharge behavior of an atmospheric-pressure air DBD...
Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial sc...
Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many citi...
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