269 Results Found

The purpose of this study is to explore hotspots or clusters of gastrointestinal tumors (GI) and their spatiotemporal distribution characteristics and the changes over time in 293 villages and communities in Jianze County, central China, through the...

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. T...

Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding cont...

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-d...

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. W...

Target detection in synthetic aperture radar (SAR) imagery remains a significant technical challenge, particularly in scenarios involving multi-target interference and clutter edge effects that cannot be disregarded, notably in high-resolution imagin...

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Trad...

The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) t...

The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization...

An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In rece...

Artificial intelligence (AI) and machine learning (ML) models have become essential tools used in many critical systems to make significant decisions; the decisions taken by these models need to be trusted and explained on many occasions. On the othe...

Groundwater droughts are one of the natural disasters that raise serious water issues for humans, and are increasing in frequency due to global climate change. In order to identify groundwater droughts, we recorded groundwater level fluctuations upst...

Shipping, as an important part of the global supply chain, has always been quite sensitive to maritime accidents. Fatality and injury are important metrics indicating an accident’s severity. Understanding the driving factors of fatality and inj...

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and...

In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments&mdas...

Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomal...

Background: The goal of this study is to identify geographic areas for priority actions in order to control COVID-19 among the elderly living in Residential Care Homes (RCH). We also describe the evolution of COVID-19 in RHC throughout the 278 munici...

Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instabil...

Red foxes are a well-established species of urban ecosystems in the UK and worldwide. Understanding the spatial ecology of foxes in urban landscapes is important for enhancement of urban biodiversity and effective disease management. The Resource Dis...

This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods...

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based appro...

An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, c...

The nature of the kernel density estimator (KDE) is to find the underlying probability density function (p.d.f) for a given dataset. The key to training the KDE is to determine the optimal bandwidth or Parzen window. All the data points share a fixed...

A semi-parametric mixture model, combining kernel density estimation (KDE) and the generalized Pareto distribution (GPD), is applied to analyze the statistical characteristics of earthquake magnitudes. Data below a threshold are fitted using KDE, whi...

This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabil...

The paper presents the results of the investigation of the applicability of spatiotemporal kernel density estimation (KDE) methods for density mapping of violent crime in Lithuania. Spatiotemporal crime research helps to understand and control specif...

Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high...

In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyz...

Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the...

A comprehensive and accurate wind power forecast assists in reducing the operational risk of wind power generation, improves the safety and stability of the power system, and maintains the balance of wind power generation. Herein, a hybrid wind power...

The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joi...

We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Den...

We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate a probability density function (PDF) of a single variable. Sampled data is partitioned into blocks using a branching tree algorithm that minimizes devia...

Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locationa...

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound...

In the field of intelligent transportation systems, pedestrian detection has become a problem that is urgently in need of a solution. Effective pedestrian detection reduces accidents and protects pedestrians from injuries. A pedestrian-detection algo...

Accurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation...

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analys...

Low-lying coastal communities are often threatened by compound flooding (CF), which can be determined through the joint occurrence of storm surges, rainfall and river discharge, either successively or in close succession. The trivariate distribution...

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...

With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and s...

Fire Service is the fundamental civic service to protect citizens from irrecoverable, heavy losses of lives and property. Hotspot analysis of structure fires is essential to estimate people and property at risk. Hotspot analysis for the peak period o...

This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are sel...

Accurate probabilistic load forecasting is essential for secure power system operation and efficient energy management, particularly under increasing renewable integration and demand-side complexity. However, traditional forecasting methods often str...

While open-set recognition algorithms have been extensively explored in computer vision, their application to environmental sound analysis remains understudied. To address this gap, this study investigates how to effectively recognize unknown sound c...

The aim of this study was to monitor social mobility using mobile users’ address searches before and during the outbreak of COVID-19. Mobile Google users’ address inquiries between the dates of 15 February 2020 and 27 July 2020 in the historical peni...

Constructing an effective importance sampling density is crucial for structural reliability analysis via importance sampling (IS), particularly when dealing with performance functions that have multiple design points or disjoint failure domains. This...

Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods for modeling uncertainties in load demand and solar generation, rangin...

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous...

We introduce a distributional CNN-LSTM framework for probabilistic multivariate modeling and heterogeneous treatment effect (HTE) estimation. The model jointly captures complex dependencies among multiple outcomes and enables precise estimation of in...