Estimation Of Density Distribution Research Articles

Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach

Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an improved yolov8n strawberry object detection model was employed to obtain the coordinates of the fruit centers in the images. The results indicated that the improved model achieved an accuracy of 94.7% with an mAP@0.5~0.95 of 87.3%. The relative error between the predicted and annotated coordinates ranged from 0.002 to 0.02, demonstrating high consistency between the model predictions and the annotated results. Subsequently, based on the strawberry center coordinates, the kernel density estimation algorithm was used to estimate the distribution density in the strawberry images. The results showed that with a bandwidth of 200, the kernel density estimation accurately reflected the actual strawberry density distribution, ensuring that all center points in high-density regions were consistently identified and delineated. Finally, to refine the strawberry distribution information, a comprehensive method based on nearest neighbor analysis was adopted, achieving target area segmentation and regional density estimation in the strawberry images. Experimental results demonstrated that when the distance threshold ϵ was set to 600 pixels, the correct grouping rate exceeded 94%, and the regional density estimation results indicated a significant positive correlation between the number of fruits and regional density. This study provides scientific evidence for optimizing strawberry planting density and formulating harvesting sequences, contributing to improved yield, harvesting efficiency, and reduced fruit damage. In future research, this study will further explore dynamic models that link fruit distribution density, planting density, and fruit growth status.

Distance profile layer for binary classification and density estimation

The research on methods trying to explain solutions based on Neural Networks (NN) is a vivid target of Machine Learning works in recent years. Such a need arose due to the black-box nature of neural models and their tendency to provide a high certainty for incorrect decisions regarding events outside the area covered by the training set. In this work, we present a novel layer building a Distance Profile of recognized samples, aiming to substitute one-hot encoding of labels. The presented approach shows promising results in classification and unsupervised distribution density estimation and maintains a consistent representation for both recognition tasks. This approach manages certainty quantification for objects outside of the scope represented by available data and could potentially contribute to the Open Set Recognition and Out of Distribution Detection research fields.

Constructions of statistical estimates of digital measurement error in the case of small samples

The paper presents a computational-experimental method for the construction of statistical estimates of the error of digital measurements carried out on complex technical objects with metrological support. The algorithm of construction of the estimation of the distribution density function of random error of measurements on the basis of complex application of methods of the theory of characteristic functions, the theory of Lie operator series and methods of statistical modeling in the processing of a limited amount of statistical information in the conditions of small samples is described. The solution of the practical problem on the construction of the estimation of the measurement error distribution density and the problem on the zero mark of the measuring instrument in the probabilistic formulation is given. The presented computational and experimental method can be used to construct statistical estimates for the probability distribution density of the general type, including those given by implicit functions, characteristic functions and operator series.

Risk Estimation of Typhoon Disaster Based on Three-Dimensional Information Diffusion Method

In the context of the increasing frequency and intensity of natural disasters, assessing the risk of typhoon disasters can provide significant assistance for risk control and emergency management of typhoon disasters. In this paper, based on the three-dimensional information diffusion method, the formal expected loss model is transformed into a computable typhoon risk assessment model. The fuzzy information in the small sample data is deeply mined, and the typhoon disaster risk assessment with the expected loss as the connotation is carried out, and the probability density distribution estimation of disaster-causing factors at different levels and the functional relationship identification between disaster-causing factors at different levels and direct economic loss rate are realized by using the information matrix. At the same time, combined with the frequency of typhoon occurrence, the annual risk of disasters is predicted to make up for the problem of insufficient marine environmental data and improve the calculation accuracy of risk assessment models. Taking Guangdong Province as an example, a typhoon risk assessment was conducted, estimating the probability distribution, direct economic loss rate distribution, and annual loss expectation of typhoon disasters under different wind speed scales and extreme wave heights. The results indicate that the risk estimation value of the three-dimensional information diffusion model is higher than that of the traditional model, which weakens the limitations of the low-dimensional information diffusion model and makes the evaluation results more reasonable and reliable.

Camera‐trapping density estimates suggest critically low population sizes for the Wet Tropics subspecies of the spotted‐tailed quoll (Dasyurus maculatus gracilis)

AbstractAccurate estimates of distribution and population density are critical for the management of threatened species. This is particularly pertinent for mammalian predators, whose generally low population density, elusive nature, and large home range requirements make it difficult to detect declines. We aimed to refine population estimates of the northern spotted‐tailed quoll (Dasyurus maculatus gracilis) in the Wet Tropics bioregion, to estimate the total number of adults, the likely size of subpopulations across the known distribution of the subspecies, and its associated conservation status. We performed targeted upland camera‐trapping surveys from June 2017 to May 2019. To calculate population densities, we used a combination of the number of individuals identified from each survey and the mean maximum distance moved from three life history stages. We then extrapolated these estimates to modelled suitable habitat areas, refined by the camera‐trapping surveys. Population sizes for the six defined subpopulations were estimated, and ranged from approximately 5 to 105 individuals. The total population was estimated to be 221 individuals. This total population estimate, and the estimates for each of the subpopulations, are lower than previous published estimates and are cause for concern. Given the low population estimates presented here and unresolved threats driving declines in some subpopulations, we suggest elevation of this subspecies to Critically Endangered under the EPBC Act.

An outlier detection algorithm based on segmentation and pruning of competitive network for glioma identification using Raman spectroscopy.

Raman spectroscopy is a promising diagnostic tool for brain gliomas, owing to its non-invasive and high information density properties. However, identifying patterns in glioma cancer tissue and healthy tissue in the brain is challenging, and outlier spectra resulting from operator error or changes in external conditions can compromise the model's robustness and generalizability to new data. Given the heterogeneity of glioma tissue, the within-group variance of data obtained by a portable Raman spectrometer is relatively high, and inconsistencies in instrument repeatability and experimental conditions can lead to an incompact distribution of non-outlier points, complicating outlier detection. Strict outlier criteria may result in the deletion of non-outlier points, leading to reduced sample utilization. To address these issues, we propose the SPCN outlier detection algorithm, which segments and prunes a competitive network to extract global outlier features, identifies topological errors, and divides initial outlier domains using the α-β region segmentation method. The algorithm also proposes a two-stage pruning method based on the characteristics of the manifold map and visualizes the outlier measure using a normalized histogram. Compared to traditional methods, SPCN is label-free and does not require an estimation of outlier distance threshold or data distribution density. We compared the accuracy of six outlier detection algorithms using Raman spectra collected from brain glioma tissues of 113 patients and examined changes in pattern recognition accuracy after removing the outliers, confirming the precision and robustness of SPCN. This method has the potential to enhance the accuracy and reliability of glioma diagnosis via Raman spectroscopy and can also be applied to outlier detection in other spectra such as near infrared and middle infrared.

Unsupervised Phonocardiogram Analysis With Distribution Density Based Variational Auto-Encoders.

This paper proposes an unsupervised way for Phonocardiogram (PCG) analysis, which uses a revised auto encoder based on distribution density estimation in the latent space. Auto encoders especially Variational Auto-Encoders (VAEs) and its variant β−VAE are considered as one of the state-of-the-art methodologies for PCG analysis. VAE based models for PCG analysis assume that normal PCG signals can be represented by latent vectors that obey a normal Gaussian Model, which may not be necessary true in PCG analysis. This paper proposes two methods DBVAE and DBAE that are based on estimating the density of latent vectors in latent space to improve the performance of VAE based PCG analysis systems. Examining the system performance with PCG data from the a single domain and multiple domains, the proposed systems outperform the VAE based methods. The representation of normal PCG signals in the latent space is also investigated by calculating the kurtosis and skewness where DBAE introduces normal PCG representation following Gaussian-like models but DBVAE does not introduce normal PCG representation following Gaussian-like models.

A distributed density estimation algorithm and its application to naive Bayes classification

We consider the problem of learning a density function from observations of an unknown underlying model in a distributed setting, where the observations are partitioned into different sites. Applying commonly used density estimation methods such as Gaussian Mixture Model (GMM) or Kernel Density Estimation (KDE) to distributed data leads to an extensive amount of communication. A familiar approach to address this issue is to sample a small subset of data and collect them into a central node to run the density estimation algorithms on them. In this paper, we follow an alternative to the sub-sampling approach by proposing the nested Log-Poly model. This model provides an accurate density estimation from a small sized statistic of the entire data. In distributed settings, it transfers the small sized statistics from the client nodes to a central node. The estimation process is then run in the central node. The proposed model can be used in different learning tasks such as classification in supervised learning and clustering in unsupervised learning. However, the properties of nested Log-Poly make it a suitable model for one-dimensional density estimations in the distributed settings. This makes Log-Poly a good choice for naive Bayes classifier, where one-dimensional density estimation is required for every feature conditioned on the class label. We provide a theoretical analysis of the efficiency of our model in estimating a wide range of probability density functions. Our experiments show that nested Log-Poly outperforms the state of the art density estimators on several synthetic datasets. We compare the accuracy and the communication load of naive Bayes classifier using nested Log-Poly and other related density estimators on several real datasets. The experimental outcomes depict that nested Log-Poly has less communication load, while maintaining a competitive classification accuracy compared to similar methods that use the entire data. Moreover, we present a comprehensive comparison between nested Log-Poly and validated KDE with sub-sampling, in terms of the number of communicated variables and the number of bytes transferred between the clients and the central node. Nested Log-Poly provides comparable accuracy with the validated KDE with sub-sampling, while communicating fewer variables. However, our method needs to compute and transmit the variables with a high precision in order to accurately capture the details of the underlying distributions.

Final Pleistocene and early Holocene population dynamics and the emergence of pottery on the Korean Peninsula

The final Pleistocene and early Holocene witnessed the emergence of the earliest pottery in East Asia. Despite extensive archaeological research conducted in Korea over the last three decades, the lack of comparable pottery evidence from the early postglacial, let alone the terminal Pleistocene, on the Korean Peninsula remains an archaeological enigma. Rather than anticipating future discoveries, we feel that it is time to explain why early pottery does not appear on the Korean Peninsula and attempt to provide a workable hypothesis focusing on the spatio-temporal dynamics of mobile hunter-gatherer-fisher populations during periods of substantial environmental change. Summed probability distribution and kernel density estimation of radiocarbon dates clearly indicate significant population fluctuations: occupation density increased under cold and dry Last Glacial Maximum (LGM) conditions and declined during warmer post-LGM and postglacial periods. We argue that a significant decline in population is the main reason why Pleistocene and postglacial pottery does not appear in Korea. On the other hand, the establishment of a Neolithic pottery tradition along Korea's coasts were likely associated with the 8.2 ka cooling event.

Individual specialization in the use of space by frugivorous bats.

Natural populations are not homogenous systems but sets of individuals that occupy subsets of the species' niche. This phenomenon is known as individual specialization. Recently, several studies found evidence of individual specialization in animal diets. Diet is a critical dimension of a species' niche that affects several other dimensions, including space use, which has been poorly studied under the light of individual specialization. In this study, which harnesses the framework of the movement ecology paradigm and uses yellow-shouldered bats Sturnira lilium as a model, we ask how food preferences lead individual bats of the same population to forage mainly in different locations and habitats. Ten individual bats were radiotracked in a heterogeneous Brazilian savanna. First, we modelled intraspecific variation in space use as a network of individual bats and the landscape elements visited by them. Second, we developed two novel metrics, the spatial individual specialization index (SpatIS) and the spatial individual complementary specialization index (SpatICS). Additionally, we tested food-plant availability as a driver of interindividual differences in space use. There was large interindividual variation in space use not explained by sex or weight. Our results point to individual specialization in space use in the studied population of S. lilium, most probably linked to food-plant distribution. Individual specialization affects not only which plant species frugivores consume, but also the way they move in space, ultimately with consequences for seed dispersal and landscape connectivity.

Temperature Distribution and Heat Flow Density Estimation in Geothermal Areas of Absheron Peninsula

Geothermal field of the Pliocene complex in the Absheron peninsula, Azerbaijan have been examined on the basis of temperature distributions in over 50 deep wells. Data analysis include variations in geothermal gradient and distribution of heat flow within complexes of Absheron formation of upper Pliocene in age. Geothermal gradients are in the range of 17 to 25oC/km. The heat flow values are found to fall in the range of 50 to 80mW/m2. Estimates have been made of geothermal energy resources up to depths of 6000 meters. The main productive strata are of middle Pliocene in age. The results have allowed identification of geothermal resources with temperature above the 20°C and at depths less than 110-180 meters. Assessments of in-situ and recoverable resources have been made for 21 sites. Model simulations point to perspectives for widespread utilization of geothermal energy in the Absheron peninsula.

The distribution of bed density in an air dense medium fluidized bed with single and binary mixtures of Geldart B and/or D particles

The distribution of bed density in an Air Dense Medium Fluidized Bed (ADMFB) with single and binary mixtures of solid particles was investigated for dry coal beneficiation. The effects of particle properties, superficial gas velocity, and mixture composition on the axial distribution of fluidized bed density were examined. A lower density region at the bottom of the fluidized bed with single particles has been observed, whereas the bed density at the upper part remains almost consistent. The increasing excess gas velocity does not change this trend although decreasing the overall bed density, however the binary mixtures of solid particles can be used to balance this non-uniform density distribution. A new equation has been derived to predict the bed density distribution, considering particle properties and fluidization characteristics. This correlation successfully accounts for the estimation of density distribution in ADMFB involving both single and binary mixtures of Geldart B and/or D particles.

Pokrycie terenu województwa łódzkiego w latach 2000–2018 w świetle danych Corine Land Cover

The aim of the author was to analyse land cover and its changes in the Łódź Voivodeship in the years 2000–2018. Based on CORINE Land Cover data and using GIS tools, analysis of land cover and its changes were performed for three intervals – the years 2000–2006, 2006–2012 and 2012–2018 – in spatial and quantitative terms. This approach allowed the land cover of the studied area to be characterised and the directions, intensity and spatial distribution of changes to be captured. In the study the density of changes was measured using the method of density distribution estimation with the use of non-para-metric nuclear estimators – the kernel function.

Direct Estimation of Density Functionals Using a Polynomial Basis

A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are useful in a number of applications. Typically, estimating these quantities requires complete knowledge of the underlying distribution followed by multi-dimensional integration. Existing methods make parametric assumptions about the data distribution or use non-parametric density estimation followed by high-dimensional integration. In this paper, we propose a new alternative. We introduce the concept of "data-driven basis functions" - functions of distributions whose value we can estimate given only samples from the underlying distributions without requiring distribution fitting or direct integration. We derive a new data-driven complete basis that is similar to the deterministic Bernstein polynomial basis and develop two methods for performing basis expansions of functionals of two distributions. We also show that the new basis set allows us to approximate functions of distributions as closely as desired. Finally, we evaluate the methodology by developing data driven estimators for the Kullback-Leibler divergences and the Hellinger distance and by constructing empirical estimates of tight bounds on the Bayes error rate.

Ant-inspired density estimation via random walks

Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the ants bump into each other. We study distributed density estimation from a theoretical perspective. We prove that a group of anonymous agents randomly walking on a grid are able to estimate their density within a small multiplicative error in few steps by measuring their rates of encounter with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), our bound nearly matches what would be required to estimate density by independently sampling grid locations. From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using local mixing properties of the underlying graph. Our results extend beyond the grid to more general graphs, and we discuss applications to size estimation for social networks, density estimation for robot swarms, and random walk-based sampling for sensor networks.

A local density-based approach for outlier detection

This paper presents a simple and effective density-based outlier detection approach with local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of using only k nearest neighbors, we further consider reverse nearest neighbors and shared nearest neighbors of an object for density distribution estimation. Some theoretical properties of the proposed RDOS including its expected value and false alarm probability are derived. A comprehensive experimental study on both synthetic and real-life data sets demonstrates that our approach is more effective than state-of-the-art outlier detection methods.

Study of gas–liquid two-phase flow patterns of self-excited dust scrubbers

Self-excited dust scrubbers are widely used in the purification of a high dust concentration. However, the existing research has been primarily focused on the macroscopic performance of the dust scrubbers and has neglected the gas–liquid two-phase flow patterns that are closely related to its dust collection principle. This paper employed high-speed photography technology to acquire video images of liquid-phase patterns and employed high-frequency acquisition technology to acquire the corresponding dynamic pressure signals of the gas-phase of the self-excited dust scrubber. Using the probability density distribution (PDD), wavelet analysis, and power spectral density (PSD) estimation, in combination with the dynamic image analysis of the gas–liquid two-phase flow, a quantitative study of the gas–liquid two-phase flow patterns of self-excited dust scrubbers was conducted. This study provides strong supporting data to overcome the bottleneck that limits theoretical studies on self-excited dust scrubbers.

A novel distributed variational approximation method for density estimation in sensor networks

In this paper, a consensus filter based distributed variational Bayesian (CFBDVB) algorithm is developed for distributed density estimation. Sensor measurements are assumed to be statistically modeled by a finite mixture model for which the CFBDVB algorithm is used to estimate the parameters, including means, covariances and weights of components. This algorithm is based on three steps: (1) calculating local sufficient statistics at every node, (2) estimating a global sufficient statistics vector using a consensus filter, (3) updating parameters of the finite mixture model based on the global sufficient statistics vector. Scalability and robustness are two advantages of the proposed algorithm. Convergence of the CFBDVB algorithm is also proved using Robbins–Monro stochastic approximation method. Finally, to verify performance of CFBDVB algorithm, we perform several simulations of sensor networks. Simulation results are very promising.

Narrow density fraction prediction of coarse coal by image analysis and MIV-SVM

Fast estimation of density distribution is important for the gravity coal preparation in coal processing field. A narrow density-fraction prediction method for coarse coal by image analysis and MIV-SVM was proposed for the future estimation of density distribution. A semi-automatic local-segmentation algorithm was proposed to identify the coal particle regions and 38 colour and texture features extracted. Mean impact value (MIV) is considered as one of the best indexes to evaluate features correlation in neural network or classification methods. Support vector machine (SVM) is state-of-the-art large margin classifiers which have recently gained popularity within visual pattern recognition. Results indicated MIV-SVM can effectively select surface features of coal particles and establish the prediction model of density fractions. Classification accuracy (CA) rises gradually with the increase of feature number and the trend is smaller and smaller. The group of 32 features with the CA 84.29% is more suitable to predict the density fraction of coarse coal. [Received: April 26, 2014; Accepted: December 28, 2014]

Distributed Density Estimation Based on a Mixture of Factor Analyzers in a Sensor Network.

Distributed density estimation in sensor networks has received much attention due to its broad applicability. When encountering high-dimensional observations, a mixture of factor analyzers (MFA) is taken to replace mixture of Gaussians for describing the distributions of observations. In this paper, we study distributed density estimation based on a mixture of factor analyzers. Existing estimation algorithms of the MFA are for the centralized case, which are not suitable for distributed processing in sensor networks. We present distributed density estimation algorithms for the MFA and its extension, the mixture of Student’s t-factor analyzers (MtFA). We first define an objective function as the linear combination of local log-likelihoods. Then, we give the derivation process of the distributed estimation algorithms for the MFA and MtFA in details, respectively. In these algorithms, the local sufficient statistics (LSS) are calculated at first and diffused. Then, each node performs a linear combination of the received LSS from nodes in its neighborhood to obtain the combined sufficient statistics (CSS). Parameters of the MFA and the MtFA can be obtained by using the CSS. Finally, we evaluate the performance of these algorithms by numerical simulations and application example. Experimental results validate the promising performance of the proposed algorithms.