Gaussian Kernel Density Research Articles

Application Of LDV Profile Sensor Technology For Velocity Determination In Small Axial Gaps

In development of Ventricular Assist Devices (VADs), Computational Fluid Dynamics (CFD) have arise as a powerful tool to gain insights on flow topologies within rotary blood pumps. Information regarding flow patterns and shear-stress distributions is of enormous importance, since flow induced blood damage as well as platelet activation is at-tributed to shear rates. Although methods for verification of numerical results are available, a lack of experimental validation is recognizable. To enable an experimental validation (i) hydraulic properties as well as flow patterns need to be preserved and (ii) a non-intrusive optical measurement technique needs to be capable of resolving flow topolo-gies appearing in rotary blood pumps. In this paper references to address (i) are provided and the focus is placed on (ii) by presenting a methodology to resolve flow topologies by usage of LDV Profile Sensor technology. This paper serves as a feasibility study and represents a preliminary stage towards actual optical measurements with rotary blood pumps. Therefore, a test bench with facilitated optical accessibility compared to Ventricular Assist Devices as well as operating conditions are introduced. Increased attention is dedicated to evaluation of measurement data by means of choosing a suitable procedure. Therefore, commonly used 2D-histograms for LDV raw data evaluation are compared to the method of Gaussian Kernel Density Estimation (Gaussian KDE). Subsequently, pre-processed raw data is further evaluated with a specially tailored Velocity Profile Validation Algorithm and determined circumferen-tial velocity profiles are discussed. In summary, herein presented feasibility study is deemed successful and LDV Pro-file Sensor technology is capable of resolving flow topologies within small gaps as they appear in Ventricular Assist Devices. Thus, LDV Profile Sensor technology may improve development of rotary blood pumps.

A novel federated learning based lightweight sustainable IoT approach to identify abnormal traffic

Purpose This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample. Design/methodology/approach To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed. Findings The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices. Originality/value With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

Multinomial Naïve Bayes Classifier: Bayesian versus Nonparametric Classifier Approach

This paper proposes a Naïve Bayes Classifier for Bayesian and nonparametric methods of analyzing multinomial regression. The Naïve Bayes classifier adopted Bayes’ rule for solving the posterior of the multinomial regression via its link function known as Logit link. The nonparametric adopted Gaussian, bi-weight kernels, Silverman’s rule of thumb bandwidth selector, and adjusted bandwidth as kernel density estimation. Three categorical responses of information on 78 people using one of three diets (Diet A, B, and C) that consist of scaled variables: age (in years), height (in cm), weight (in kg) before the diet (that is, pre-weight), weight (in kg) gained after 6 weeks of diet were subjected to the classifier multinomial regression of Naïve Bayes and nonparametric. The Gaussian and bi-weight kernel density estimation produced the minimum bandwidths across the three categorical responses for the four influencers. The Naïve Bayes classifier and nonparametric kernel density estimation for the multinomial regression produced the same prior probabilities of 0.3077, 0.3462, and 0.3462; and A prior probabilities of 0.3077, 0.3462, and 0.3462 for Diet A, Diet B, and Diet C at different smoothing bandwidths.

Estimating the Gait Speed of Older Adults in Smart Home Environments

Mobility is one of the key performance indicators of the health condition of older adults. One important parameter is the gait speed. The mobility is usually assessed under the supervision of a professional by standardised geriatric assessments. Using sensors in smart home environments for continuous monitoring of the gait speed enables physicians to detect early stages of functional decline and to initiate appropriate interventions. This in combination with a floor plan smart home sensors were used to calculate the distance that a person walked in the apartment and the inertial measurement unit data for estimating the actual walking time. A Gaussian kernel density estimator was applied to the computed values and the maximum of the kernel density estimator was considered as the gait speed. The proposed method was evaluated on a real-world dataset and the estimations of the gait speed had a deviation smaller than 0.10 , frac{mathrm{m}}{mathrm{s}}, which is smaller than the minimal clinically important difference, compared to a baseline from a standardised geriatrics assessment.

Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning

Accurate energy consumption prediction is the basis of predictive control for heating, ventilation and air conditioning (HVAC) systems. Data-driven models are widely used for energy consumption prediction. The prediction accuracy can be affected by data preprocessing or selection, which are not well studied yet. This paper attempts to study the impacts of different data processing methods and feature selection on the HVAC energy consumption prediction based on data-driven models. Long and short-term memory models are developed based on historical data to predict the day-ahead hourly energy consumption of HVAC systems. Two data smoothing methods, Gaussian kernel density estimation and Savitzky-Golay filter, are selected and compared. The impacts of feature selection, training set volumes and update frequency of models are analyzed and compared. To obtain general results of the above problems, three office buildings are selected. Results show that the smoothing methods can not ensure the improvement in accuracy by removing the exceptional data in the raw data which arise reasonably in practice. The inputs with raw 1-day historical energy consumption, the dry-bulb temperature and dew-point temperature in the next day, holiday type and day type is recommended to predict day-ahead HVAC energy consumption. It is recommended to use a larger training set if computing cost is acceptable. The model should be updated after being used for over 7 weeks. This study would guide the development of prediction models in practice.

Deep Learning based Currency Exchange Volatility Classifier for Best Trading Time Recommendation

This paper presents a deep artificial neural network approach based currency market volatility based recommendation engine. Since deep learning classification needs labeled data set that we don't have, an approach is designed specially for that point in order to generate labeled data set from non labeled one. This is a major innovative aspect in this contribution in addition to the recommendation service. It is based on Gaussian kernel density and Monte Carlo simulation. The main goal of the proposed approach is to predict - for each hour of the day - the volatility behaviour of the selected currency pair. The proposed model has a range of applications in financial market specially the algorithmic trading. Deep neural network was trained and evaluated and testing process gave good convergence rate.

A Novel Markov Model for Near-Term Railway Delay Prediction

Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop a chi-square test to show that the delay evolution over stations follows a first-order Markov chain. We then propose a delay prediction model based on non-homogeneous Markov chains. To deal with the sparsity of the transition matrices of the Markov chains, we propose a novel matrix recovery approach that relies on Gaussian kernel density estimation. Our numerical tests show that this recovery approach outperforms other heuristic approaches in prediction accuracy. The Markov chain model we propose also shows to be better than other widely-used time series models with respect to both interpretability and prediction accuracy. Moreover, our proposed model does not require a complicated training process, which is capable of handling large-scale forecasting problems.

Density peaks clustering algorithm based on kernel density estimation and minimum spanning tree

It is to be reported that a clustering algorithm that can achieve rapid searching of density peaks in 2014. Aiming at solving the shortcomings of the algorithm, this paper proposes a density peak clustering algorithm based on kernel density estimation and minimum spanning tree (MST). The proposed DPC algorithm adopts the Gaussian kernel density to estimate the local density of samples, coordinating the relationship between the part and the whole; proposing a new allocation strategy, which combines the idea of minimum spanning tree to generate a tree from the dataset according to the principle of high density and close distance. The degree of polymerisation is defined and calculated before and after disconnecting one edge of the tree, and the edge is disconnected with the larger degree of polymerisation, which until the expected number of clusters is met. The experimental results make known that the proposed algorithm has better clustering result.

Semiparametric Bayesian networks

We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones. We demonstrate that semiparametric Bayesian networks generalize two well-known types of Bayesian networks: Gaussian Bayesian networks and kernel density estimation Bayesian networks. For this purpose, we consider two different conditional probability distributions required in a semiparametric Bayesian network. In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data. To realize this, we employ a score function based on cross-validation. In addition, using a validation dataset, we apply an early-stopping criterion to avoid overfitting. To evaluate the applicability of the proposed algorithm, we conduct an exhaustive experiment on synthetic data sampled by mixing linear and nonlinear functions, multivariate normal data sampled from Gaussian Bayesian networks, real data from the UCI repository, and bearings degradation data. As a result of this experiment, we conclude that the proposed algorithm accurately learns the combination of parametric and nonparametric components, while achieving a performance comparable with those provided by state-of-the-art methods.

6G Multisource-Information-Fusion-Based Indoor Positioning via Gaussian Kernel Density Estimation

With the in-depth development of the Internet of Things (IoT) and the constant discussion of 6G visions, the future development of indoor location-based services (LBSs) in the 6G-enabled IoT is attracting people’s attention. In fact, due to the continuous updating of network techniques, the complexity of the indoor environment and the number of connected wireless access points (APs) will increase dramatically, which leads to the diversity of the received signal strength (RSS) on the performance of signal propagation distance estimation, resulting in low positioning accuracy and poor robustness. In response to this problem, this article proposes a multisource information fusion-based indoor positioning approach via Gaussian kernel density estimation. First, the heuristic distribution model is used to establish the mathematical relationship between the RSS from different Wi-Fi APs and the signal propagation distance, and then the Gaussian kernel density estimation approach is applied to estimate the signal propagation distance distribution. Second, the normalized signal propagation distance distribution is set as the basic probability assignment in the Dempster–Shafer (D–S) evidence theory, and then the multisource RSS information is fused according to D–S evidence synthesis rules. Meanwhile, the trust function synthesized by the D–S evidence theory is used to select matching reference points (RPs). Finally, the fuzzy decision algorithm is used to select the ideal matching RPs from the matching RPs for the positioning. Experimental results show that the proposed approach has higher positioning accuracy and stronger positioning robustness compared to existing ones.

Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules.

Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.

Estimating the dispersal of Lepeophtheirus salmonis sea lice within and among Atlantic salmon sites of the Bay of Fundy, New Brunswick.

The objective of this study was to estimate the impact of infestation pressures on the abundance of the parasitic sea louse, Lepeophtheirus salmonis, in the Bay of Fundy, New Brunswick (NB), Canada, using the Fish‐iTrends database for the years 2009–2018. Infestation pressures were calculated as time‐lagged weighted averages of the abundance of adult female (AF) sea lice within a site (internal infestation pressure: IIP) and among sites (external infestation pressure: EIP). The EIP weights were calculated from seaway distances among sites and a Gaussian kernel density for bandwidths of 5 to 60 km. The EIP with a bandwidth of 10 km had the best fit, as determined with Akaike's information criterion, and historical AF sea lice abundance. This estimated dispersal distance of 10 km was similar to previous studies in Norway, Scotland and in New Brunswick. The infestation pressures estimated from empirical AF sea lice abundance within and among sites significantly increased the abundance of AF sea lice (p < .001). This study concludes that sea lice burdens within Atlantic salmon farms in the Bay of Fundy, NB, are affected by within site management and could be improved by synchronizing treatments between sites.

Social Mobility Patterns in the World's Populated Cities Through COVID-19

Objective: identify social mobility patterns in the world's most populated cities from the ravaging pandemic of COVID-19 and the confinement and social distancing measures. Method: ternary diagrams to examine the simultaneous movement to different places (grocery, services, parks, workplaces, residence, and transit). Specifically, we use crosshair ternary plots and a Gaussian Kernel Density Estimator (KDE) for ternary density diagrams. Results: for the most part, the mobility reduction was between 40% and 60% in the selected cities. Nevertheless, there were more significant transit cases, but not workplaces or residences, suggesting that the informal market may absorb part of the labor work. Limitations and implications: the main limitation of this analysis is in scaling the data since the mobility statistics represent negative percentages. Main contribution: the work's principal contribution and originality lie in using ternary diagrams, allowing the identification of social mobility patterns in the largest cities and understanding how displacement of populations has changed since COVID-19.

Robust resistance to noise and outliers: Screened Poisson Surface Reconstruction using adaptive kernel density estimation

Screened Poisson Surface Reconstruction has a good performance among the state-of-art surface reconstruction algorithms in obtaining a triangle mesh from oriented points. In order to better deal with nonuniform point clouds, Screened Poisson Surface Reconstruction uses B-spline functions with a fixed support for kernel density estimation to construct a vector field for solving the screened Poisson equation. In this paper, an adaptive bandwidth Gaussian kernel density estimator is applied, which reduces the bandwidth where the density is low, and increases the bandwidth where the density is high. Experiments show that such an estimator that makes use of both global and local points distribution can effectively remove noise and outliers in the reconstruction.

Gene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method

Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expression of 7129 genes of 25 patients with acute myeloid leukemia (AML), and 47 patients with lymphoblastic leukemia (ALL) achieved by the microarray technology were used in this study. Then, the important genes were identified using a sparse feature selection method to diagnose AML and ALL tissues based on the machine learning methods such as support vector machine (SVM), Gaussian kernel density estimation based classifier (GKDEC), k-nearest neighbor (KNN), and linear discriminant classifier (LDC). Results: Diagnosis of ALL and AML was done with the accuracy of 100% using 8 genes of microarray data selected by the sparse feature selection method, GKDEC, and LDC. Moreover, the KNN classifier using 6 genes and the SVM classifier using 7 genes diagnosed AML and ALL with the accuracy of 91.18% and 94.12%, respectively. The gene with the description “Paired-box protein PAX2 (PAX2) gene, exon 11 and complete CDs” was determined as the most important gene in the diagnosis of ALL and AML. Conclusion: The experimental results of the current study showed that AML and ALL can be diagnosed with high accuracy using sparse feature selection and machine learning methods. It seems that the investigation of the expression of selected genes in this study can be helpful in the diagnosis of ALL and AML.

X-Entropy: A Parallelized Kernel Density Estimator with Automated Bandwidth Selection to Calculate Entropy.

X-Entropy is a Python package used to calculate the entropy of a given distribution, in this case, based on the distribution of dihedral angles. The dihedral entropy facilitates an alignment-independent measure of local protein flexibility. The key feature of our approach is a Gaussian kernel density estimation (KDE) using a plug-in bandwidth selection, which is fully implemented in a C++ backend and parallelized with OpenMP. We further provide a Python frontend, with predefined wrapper functions for classical coordinate-based dihedral entropy calculations, using a 1D approximation. This makes the package very straightforward to include in any Python-based analysis workflow. Furthermore, the frontend allows full access to the C++ backend, so that the KDE can be used on any binnable one-dimensional input data. In this application note, we discuss implementation and usage details and illustrate potential applications. In particular, we benchmark the performance of our module in calculating the entropy of samples drawn from a Gaussian distribution and the analytical solution thereof. Further, we analyze the computational performance of this module compared to well-established python libraries that perform KDE analyses. X-Entropy is available free of charge on GitHub (https://github.com/liedllab/X-Entropy).

Discrete and Smoothed Resampling Methods for Interval-Valued Fuzzy Numbers

In this article, we propose two new resampling algorithms for the simulation of bootstrap-like samples of interval-valued fuzzy numbers (IVFNs). These methods (namely, the $d$ -method and the $s$ -method) reuse a primary sample (an initial set) of IVFNs to generate a secondary sample, which also consists of this type of fuzzy numbers, and simultaneously utilize existing dependencies in pairs of some characteristic points of IVFNs. During a corresponding resampling step, a nonparametric approach is used. Additionally, we apply a widely used assumption about the Gaussian kernel densities. The proposed methods in some way resemble Efron's bootstrap, but, contrary to this classical approach, they generate “not exactly the same as previous” IVFNs, so it leads to a greater diversity of the obtained secondary sample. We also numerically check the quality of the introduced methods using a few more statistically oriented approaches together with four similarity measures and three types of IVFNs.

The environment of QSO triplets at 1 ≲ z ≲ 1.5

ABSTRACT We present an analysis of the environment of six QSO triplets at 1 ≲ z ≲ 1.5 by analysing multiband (r, i, z, or g, r, i) images obtained with Megacam at the CFHT telescope, aiming to investigate whether they are associated or not with galaxy protoclusters. This was done by using photometric redshifts trained using the high-accuracy photometric redshifts of the COSMOS2015 catalogue. To improve the quality of our photometric redshift estimation, we included in our analysis near-infrared photometry (3.6 and 4.5$\, \mu$m) from the unWISE survey available for our fields and the COSMOS survey. This approach allowed us to obtain good photometric redshifts with dispersion, as measured with the robust σNMAD statistics (which scales as (1 + z)−1), of ∼0.04 for our six fields. Our analysis setup was reproduced on lightcones constructed from the Millennium Simulation data and the latest version of the L-GALAXIES semi-analytic model to verify the protocluster detectability in such conditions. The density field in a redshift slab containing each triplet was then analysed with a Gaussian kernel density estimator. We did not find any significant evidence of the triplets inhabiting dense structures, such as a massive galaxy cluster or protocluster.

A proposed data acquisition system and algorithm for signal processing of moving-coil geophone\u2019s output

The study of different types of vibrational and seismic movements is important for exploration in strata monitoring, machine health monitoring, earthquake detection, etc. In order to study these vibrational movements, it needs to be acquired first for analysis. Data acquisition using the seismic sensors is a challenging task. This paper presents a data acquisition system developed to acquire seismic signals from a moving-coil geophone. The paper also discusses a signal interpretation algorithm that is devised to perform automatic detection of a seismic event occurrence by separating through the waveform and non-waveform components in the sensor’s output using Gaussian naive Bayes classifier and Kernel density estimation technique. The proposed method is effective in the identification of a useful signal and identification of its nature of origin. Accuracy of the algorithm was 99% for the waveform classification. Sensitivity of the data acquisition system for the seismic sensors was 1.589 µm s–1. Further, the developed data acquisition system and the algorithm can be used in mines for seismological studies aimed at separating the vibration signal generated due to explosion and the one caused due to Earth’s tectonic and seismic activities.

Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.