Kernel Density Function Research Articles

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

Seismic fragility analysis of long-span rigid-frame bridge on mountainous soft clay site

In order to assess the damage condition of bridge components for a large-span rigid bridge in a soft clay site in a mountainous area in China southwest, a finite element model of a large-span rigid bridge is established based on the OpenSees software, and the joint probability density distribution function of the ground motion strength and seismic demand and the marginal distribution function of the ground motion are introduced into the kernel density function. As a basis to get the method of calculating the fragility of the bridge members, and the method is verified for its feasibility, on this basis, the damage condition of the bridge components are analyzed, and finally the damage condition of the bridge system are analyzed by the first-order bounds method and the improved PCM method (IPCM). The results showed that: (1) Kernel density method (KDE) can effectively calculate the damage probability of each component, for example, under ground motions with PGA equal to 0.2 g, the probability of slight damage of the 1# pier is 29%, that of the intermediate consolidation pier (2# pier ~ 4# pier) is about 90%; the probability of slight damage of the 1# bearing is 48%, and that of the 2# bearing is 87%. (2) Reasonable value of the expansion joints can effectively reduce the probability of main beam collision. In this investigation, the value is taken as 0.18 m ~ 0.24 m. (3) The bridge system is more likely to be damaged than a single component in the system, and the damage probability of a single component cannot be used as a criterion for the bridge system in the actual working condition. Comparing the first-order boundary law with the IPCM method, the IPCM method has higher accuracy.

Medical Data for the Nadaraya-Watson Kernel Method in Nonparametric Regression Models Based on Hyperbolic Secant Function

Nonparametric kernel estimates used in this work aim to compare different treatment options by examining the recorded medical data. The following use of the suggested strategy depends on a kernel function and a parameter called bandwidth. The Nadaraya-Watson kernel (NWK) estimation is a necessary nonparametric kernel estimator used in regression models. A new Nadaraya-Watson regression estimate depends on the hyperbolic secant kernel (HSK) with fixed bandwidth (FNW) and Variable Bandwidth (VNW) is proposed.. We calculated some properties of the unknown regression function estimator, including bias, variance, optimal bandwidth, and a global measure of error criterion mean square error. Finally, simulation and three real data sets are used to evaluate its performance. Results from simulation and real data showed that the VNW using HSK is more effective than the FNW based on Average Mean Square Error Criterion. Also, Nadaraya-Watson using HSK function is more effective than Nadaraya-Watson using the Gaussian kernel density function.

A fast data-driven fault detection and location method for unknown distributed thermal processes

The distributed thermal processes are widely present in various industrial operations, but the presence of time delay effect and unknown model information make achieving fast and timely fault detection challenging. Furthermore, the involvement of spatiotemporal coupled data in measurement processes further exacerbates the situation. To solve these problems, this paper proposes a data-driven fault detection and location method for thermal processes described by distributed parameter systems (DPS). Firstly, a Time/Space separation is employed to decoupling the spatio-temporal data into spatial basis functions and temporal coefficients. Subsequently, dynamic partial least squares (DPLS) is employed to obtain the temporal information of the model with fault-free data. Monitoring statistics in both the temporal and spatial domains are then constructed, and their thresholds are estimated by kernel density function. Finally, an online strategy for fault detection and location is presented. The method was validated on the catalytic rod, and an experimental snap curing oven.

Cloud maps highlighting dynamic characteristics of surface signal to improve time-varying wear evaluation accuracy

This paper proposes a high accuracy time-varying wear evolution method with cloud maps highlighting dynamic characteristics of surface signal. Firstly, the cloud map method is established by arrangement of measured data, thus time-varying wear evolution process is visualized on a series of plane figures for preliminary qualitative analysis. Then, high accuracy recognition of time-varying wear states is realized by cloud map shape parameters, including kurtosis and 1-D kernel density function highlighting distribution information of surface signal. The relative recognition degree by cloud map shape parameters are higher than those of friction coefficient, Root-Mean-Square (RMS) deviation and fractal dimension. Finally, high accuracy time-varying wear life prediction is realized by cloud map size growing speed highlighting waviness information of surface signal. The relative prediction error is reduced from more than 20% to less than 10% compared with friction coefficient and roughness.

Asymptotic properties of kernel density and hazard rate function estimators with censored widely orthant dependent data

Asymptotic properties of kernel density and hazard rate function estimators with censored widely orthant dependent data

Deep vs shallow: GPS tags reveal a dichotomy in movement patterns of loggerhead turtles foraging in a coastal bay

BackgroundIndividual variation in movement strategies of foraging loggerhead turtles have been documented on the scale of tens to hundreds of kilometers within single ocean basins. Use of different strategies among individuals may reflect variations in resources, predation pressure or competition. It is less common for individual turtles to use different foraging strategies on the scale of kilometers within a single coastal bay. We used GPS tags capable of back-filling fine-scale locations to document movement patterns of loggerhead turtles in a coastal bay in Northwest Florida, U.S.A.MethodsIridium-linked GPS tags were deployed on loggerhead turtles at a neritic foraging site in Northwest Florida. After filtering telemetry data, point locations were transformed to movement lines and then merged with the original point file to define travel paths and assess travel speed. Home ranges were determined using kernel density function. Diurnal behavioral shifts were examined by examining turtle movements compared to solar time.ResultsOf the 11 turtles tagged, three tracked turtles remained in deep (~ 6 m) water for almost the entire tracking period, while all other turtles undertook movements from deep water locations, located along edges and channels, to shallow (~ 1–2 m) shoals at regular intervals and primarily at night. Three individuals made short-term movements into the Gulf of Mexico when water temperatures dropped, and movement speeds in the Gulf were greater than those in the bay. Turtles exhibited a novel behavior we termed drifting.ConclusionsThis study highlighted the value provided to fine-scale movement studies for species such as sea turtles that surface infrequently by the ability of these GPS tags to store and re-upload data. Future use of these tags at other loggerhead foraging sites, and concurrent with diving and foraging data, would provide a powerful tool to better understand fine-scale movement patterns of sea turtles.

Moderate anthropogenic impacts alter temporal niche without affecting spatial distribution of ocelots in the Amazon rainforest

AbstractThe Amazon rainforest faces escalating human disturbances such as logging, mining, agriculture, and urbanization, leading to the conversion of primary forest into matrix habitat. This transformation's impact on mesocarnivores, specifically ocelots (Leopardus pardalis), is still largely unknown. In 2021, we deployed camera traps across a 174 km2 study area in Las Piedras, Madre de Dios, Peru, containing mixed land use and protected forests. Utilizing kernel density functions, we compared temporal activity patterns and employed spatially explicit capture‐recapture (SECR) models to assess density, encounter probability, and movement by habitat and trail type, as well as sex. Of the 293 captures, we identified 39 ocelots (21 females, 18 males), estimating an average density of 31.46 (SE 5.15) individuals per 100 km2 with no significant difference between protected and mixed‐use areas. Baseline detection and movement varied by sex, with male and female home ranges estimated at 17.14 and 4.10 km2, respectively. Ocelot temporal activity patterns differed between the protected area and the mixed‐use area, with increased nocturnality in areas of higher human impact. Our SECR results highlight that matrix habitats can support ocelot populations, emphasizing the need for further research on ocelot demographics in human‐modified Amazon rainforest areas facing varying degrees of anthropogenic disturbance.Abstract in Spanish is available with online material.

An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.

Growing crisis of urban rat infestation in Karaj city, Iran: Insights from hotspot mapping and social network analysis

To control the increasing growth of rat populations in metropolitan areas, several factors need to be considered to mitigate the risks posed by rats. Identifying rat density hotspots with more efficient methods and assessing people's experiences and attitudes toward them can be useful in prioritizing rat control efforts. We focused on the two most common species of rats (Rattus rattus and Rattus norvegicus) in the metropolis of Karaj, the fourth most populous and the largest immigrant-friendly city in Iran. The urban rat infestation is one of the problems caused by the high population growth in Karaj and the insufficient development of urban infrastructure and waste collection. It has led to discontent and public concern about rat infestations and there is a need to address the associated threats. We applied the Kernel Density Function and Optimized Hotspot Analysis to the presence points of the aforementioned types of rats and found that while Optimized Hotspot projects a macro view of hotspots at the city level, the Kernel Density Function prioritizes neighborhoods for initial actions. In addition, social network analysis with complementary regression models was conducted to determine people's experiences. The results show that fear of rats and concern about their presence form the basis of people's attitudes toward rats and lead to feelings of hatred. Moreover, economic harm reinforces people's motivation to control or eliminate this species. Despite their unpleasant feelings toward rats, people have a great influence on the growth of rat infestations by creating food sources through improper waste disposal. They prefer to use traps and poison instead of disposing of household waste and following the principles of cleanliness as preventive measures and believe that the lack of proper garbage collection by the municipality is the most important reason for the growth of rat populations. They are not sufficiently aware of their position in addressing this crisis. Our findings argue for the formation of a stronger network between the relevant authorities and residents, especially those living in the hotspots, to educate them about their role in rat control and to gain their cooperation in improving the city's waste management system.

Spatial and temporal monitoring of wildfires in Golestan province using remote sensing data

Wildfires are one of the most significant factors of ecosystem change. Knowing the wildfire regime (frequency, intensity, and distribution pattern) is essential in wildfire management. This research aims to analyze the spatiotemporal pattern of wildfires in Golestan in 2001-2021 using MODIS data, burned area product (MCD64A1). For this purpose, the annual and monthly frequency, as well as the trend of wildfires based on types of forest, pasture, and crop cover, were statistically analyzed. The local Moran pattern analysis method and kernel density function were used to analyze the spatial dynamics of wildfire. The results showed that 18,462 wildfires occurred in Golestan, the highest of which was in 2010, with 2,517 wildfires (13.8%). The lowest number of wildfires, with only 57 events (0.5%), was in 2001. Based on the local Moran model results and the kernel density function, the wildfires' extent and intensity were greater in the plains and foothills to the south and southeast of Golestan. The lowest extent and intensity of the wildfire corresponded to the eastern parts of the province. The frequency of wildfires was higher in the hot period of the year (spring and summer). However, the period of occurrence of wildfire and the peak of wildfire changes in different uses. The wildfire zones in June were wider and more intense than in other months. The frequency and spatial extent of wildfires in agricultural lands from May to July, pasture lands in July, August, and September, and forest lands in November and December were more than in other months. Weather conditions play a significant role in the occurrence of wildfire in the forest lands of Golestan. The results of this research help understand wildfire risk areas and provide a scientific basis for predicting and controlling wildfires and reducing carbon emissions related to them.

The immediate effects of vision-zero corridor upgrades on pedestrian crashes in New York: A before-and-after spatial point process approach

The long-term effects of the Vision-Zero (VZ) approach in Scandinavia are well documented. In contrast, information regarding the immediate effects of VZ at the starting phase upon gradual implementation is scarce. Taking New York City as the case study, we analyzed both the local and global effects of the Vision-Zero gradual implementation on pedestrian crashes in the early stage of implementation starting from 2014. The data analysis comprised 8,165 pedestrian injury crashes. Using location data, the crashes were matched to VZ infrastructure improvement location, start and completion dates. The experimental design included a treatment and two types of control conditions, and we controlled for well-known covariates including traffic exposure, land use, and risk-prone areas. We estimated a Geyer Saturation model and kernel density function for modeling the effect of Vision-Zero on crash intensity and dispersion two years before and after the implementation of Vision-Zero. The results reveal a significant global decrease of 6.1 % (p = 0.004) in pedestrian crash incidence in the treated sections compared with the control group two years after the treatment, and a greater dispersion of pedestrian injuries following the policy implementation.

Research on the Path of High-Quality Development of Rural Tourism Empowered by Digital Economy

Abstract The digital economy is a part of the current industrial reform that should not be ignored. Moreover, rural tourism is an important way to realize rural revitalization and common prosperity, and the correlation and interaction between the two are crucial to rural development. In this paper, we explore the interactive influence between the two, use the entropy value method, kernel density function, global spatial autocorrelation analysis, and other measurement methods to measure the comprehensive development level of the two, and accordingly establish the coupling and coordination model for the high-quality development of digital economy and rural tourism. The spatiotemporal characterization of the high-quality development of the digital economy and rural tourism in 30 provinces in China is carried out, and the coupled and coordinated development of the digital economy and rural tourism in 16 cities and towns in Yunnan Province is explored as an example, according to which suggestions for the corresponding development paths are put forward. The digital economy development index of China’s seven major geographic regions during the five years of 2019-2023 is the highest in South China (0.473) and the lowest in Northwest China (0.219). Southwest China (0.518) has the highest rural tourism high-quality development index value, while Northwest China (0.386) has the lowest value. The degree of coupling and coordination of digital economy and rural tourism high-quality development in Yunnan Province increased by 0.0903 during the 5 years. The coupling and coordination of digital economy and rural tourism high-quality development in Yunnan Province in 2014, 2016, 2018, and 2020 Moran index was -0.0885, 0.0916, 0.1527, 0.1729, with the Z-value showing an increasing trend and the P-value showing a decreasing Trend. The coordination between the digital economy and rural tourism development in Yunnan Province has improved.

Research on the Mechanism of Digitalized Innovation and Development of Yellow River Culture Based on GIS Analysis

Abstract The Yellow River Basin is a complex spatial system integrating ecology, economy, culture and society. The research in this paper takes the innovation of cultural heritage in the basin as the fundamental orientation, and analyzes the influence mechanism of digitization on the innovative development of Yellow River culture through GIS. The inter-regional heterogeneity of the digital transformation of the Yellow River culture is examined through the use of the kernel density function. To examine the spatial correlation between the Yellow River basin and the development of cultural digitalization innovations, the gravity model is utilized. The Yellow River cultural industry’s innovation adaption capacity is measured through two indicators: innovation input capacity and innovation absorption capacity. Based on the analysis of the spatial distribution pattern and the trend of spatio-temporal change, it is tentative to observe that there is a possible spatial correlation between the two. The results show that the field energy variation of the Yellow River cultural memory space ranges from 0.0928 to 0.5384, and the distribution has significant spatial dependence. The logarithm of the digitization level of the Yellow River culture in 2019 is mainly concentrated around -2.2, and the overall digital development of cities along the Yellow River has not been polarized. A new framework and reference for protecting cultural heritage and promoting civilization innovation in the Yellow River basin is provided by this paper.

Belief Fisher–Shannon information plane: Properties, extensions, and applications to time series analysis

This paper introduce the belief Fisher–Shannon (BFS) information plane based on basic probability assignment concept. Moreover, upper and lower bounds of BFS information are also presented. In addition, BFS information plane is extended to belief Fisher–Rényi and fractional belief Fisher–Shannon ones, whose are based on Generalized Rényi and fractional Deng entropies, respectively. This study is exemplified by a simulation study of Logistic, Chebyshev, and Hénon chaotic map time series; and two applications related to fish condition factor and ozone pollutant time series. Time series were discretized using Freedman–Diaconis rule as a histogram estimator. Results indicate that proposed information planes based on histogram estimator provides a more efficient method with respect to parametric and non-parametric methods, such as one based on kernel density function. Therefore, our findings suggest that proposed information planes could be an appropriate tool to better investigate the complex dynamics of these signals.

Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels

Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of “carbon peak and carbon neutrality” as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China’s energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China’s energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China’s total energy carbon emissions show a “rising-declining-rising” trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China’s Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China’s energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China’s catch-up provinces develop their economies at the expense of the environment and energy consumption.

Spatio-temporal characteristics of land ecological security and analysis of influencing factors in cities of major grain-producing regions of China

The increasingly critical ecological environment in the major grain-producing regions (MGPRs) of China has attracted widespread attention, but there is limited research at the city level. This study presented a land ecological security (LES) evaluation index system based on the pressure, state, response (PSR) model. It used the panel entropy weight method, kernel density function method, Moran index model, and spatial econometric model to measure LES of 158 cities in MGPRs from 2005 to 2020, and analyzed the spatial and temporal evolutionary trends, spatial divergence characteristics, and influencing factors of LES. The results are as follows. (1) From 2005 to 2020, the overall trend of urban LES level was rising, but it experienced an N-shape fluctuation trajectory of “rise-fall-rise”. The state index and response index were on an upward trend, while the pressure index had a certain degree of decline. (2) LES was characterized by the spatial distribution of “high in the north and low in the south” and “high in the east and low in the west”, while spatial heterogeneity became increasingly significant, with greater spatial differences within the northern cities and within the central cities. (3) There was a significant spatial spillover effect of LES, but the effect decreased with the expansion of spatial divergence. The spatial association patterns mainly exhibited “high-high” (HH) and “low-low” (LL) agglomerations. (4) The response layer was the main obstacle subsystem, while green space per capita, environmental governance investment, precipitation per capita, arable land per capita, and agricultural development were the main obstacle factors. (5) Economic development, fiscal decentralization, industrial advancement, and innovation input were the positive factors of LES, whereas financial development and industrial development were the negative factors of LES. The findings provide effective policy reference for the sustainable utilization of urban land resources.

Impact of Covid-19 on pedestrian safety

The Covid-19 pandemic has affected everyday life in many areas, including transport. As a result of the reduction in traffic and epidemic measures, the number of traffic accidents and the number of people injured in accidents have decreased worldwide. However, the available research results are mainly limited to the investigation of the number of accidents and injuries. The aim of our research was to determine the impact of Covid-19 on pedestrian accidents, focusing also on the characteristics of injured pedestrians, and the willingness of drivers to give priority to pedestrians. We performed a multi-aspect analysis and used statistical methods to examine the characteristics of pedestrian accidents and injured pedestrians in Budapest between 2018 and 2021, and the changes caused by the epidemic. During the analysis of the accidents, we also created maps using the Kernel Density Function based on the severity index of the accident outcome. Pedestrian and driver behaviour were assessed based on the rates of giving the priority to pedestrians at pedestrian crossings, using the results of our own measurements before and during the Covid-19 period. As the main result of our research, we found that although the epidemic and related measures reduced the number of pedestrian accidents, there was no significant change in accident characteristics. In the case of injured pedestrians, we showed a significant change in the reduction of injuries between the ages of 0 and 18 and in case of the injuries of foreign citizens. In addition, we found that the willingness to give priority for pedestrians was negatively affected by the period of Covid-19.

The number of larval instars in the flax weevil (Anagotus fairburni) (Coleoptera: Curculionidae)

ABSTRACT The flax weevil Anagotus fairburni is a large flightless beetle, that is one of the members of the endemic insect ‘megafauna’ of New Zealand. It is a protected species that currently persists only on predator-free islands or in remote and difficult to access alpine areas. Little is documented about the ecology of the flax weevil. In this study we estimated the number of instars in the A. fairburni life cycle by measuring the head capsule widths of larvae collected in the field on Mana Island Scientific Reserve. We used kernel density function estimates to predict average head-capsule widths and the number of larval instars. We then used Brooks-Dyar’s law on the head capsule width data and analysed Brooks and Crosby indexes to refine the estimated number of instars based on imperfect data. Results from sampling of 86 larvae suggested four instar groupings, but further analysis based on Brooks-Dyar’s law found that A. fairburni likely passes through 6 or 7 larval stages prior to pupation, with some uncertainty for smaller instars. Our method provides new data on ecology of an endemic species and provides a framework for further work on similar endangered species where data is imperfect or difficult to gather.

Implementing a novel fault prognosis technique based on nonlinear fault observer and online parameters estimation

<span lang="EN-US">This research presents a methodology for predicting errors of parameters, as the algorithm tries to monitor the parameters in order to maintain or replace them when needed to avoid excessive expenses. The presented implementation mechanism is based on monitoring parameters according to a specific number of batches and each batch consists of a number of iterations, which in turn are a number of samples. The proposed algorithm involves designing a new nonlinear observer and writing a secondary algorithm for parameter estimation based on the online nonlinear recursive least squares algorithm associated with the observer states. In addition, the algorithm presents an attempt to find a relationship between the error states and the state of the parameters by creating a new function to determine the weight of the error according to four components; parameter changes, output residuals, output errors and the error diagnosed by the new observer. The algorithm also includes introducing the probability form of the weights using the kernel density function for the average and maximum weights for each batch. Finally, relying on the results, it is possible to take the appropriate decision to maintain or change the parameters as shown a non-linear direct current motor model case study.</span>