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Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503). [ABSTRACT FROM AUTHOR]

Abstract: The constitutive relations of the mass loaded paper honeycomb panel is expressed as a linear differential equation. Using the Laplace transform method, we can model the paper honeycomb panel as a linear material with viscoelastic property, the relaxation kernel is expressed as the sum of complex exponentials. A parameters identification method is formulated using the substitute method. A free response experiment system is set up, the free response data are recorded and used to identify the stiffness and damping coefficients as well as the viscoelastic parameters. The identified parameters of the model under different honeycomb core height condition are given in this paper. The model and the identified parameters can be used to simulate the vibration transmissibility of the mass loaded paper honeycomb panel. The response of the system excited by the harmonic inputs is calculated using the one-term harmonic balance method. The comparison of the simulated transmissibility curve and the experimental data indicates that the model presented in this paper is accurate, the parameters presented can be used to predict the vibration transmissibility property of the mass loaded paper honeycomb panel system. [Copyright &y& Elsevier]

This paper, constituting an extension to the conference paper [1], corrects the proof of the Theorem 2 from the Gower's paper [2, page 5]. The correction is needed in order to establish the existence of the kernel function used commonly in the kernel trick e.g. for k-means clustering algorithm, on the grounds of distance matrix. The correction encompasses the missing if-part proof and dropping unnecessary conditions. [ABSTRACT FROM AUTHOR]

Several control charts for individual observations are compared. Traditional ones are the well-known Shewhart individuals control charts based on moving ranges. Alternative ones are non-parametric control charts based on empirical quantiles, on kernel estimators, and on extreme-value theory. Their in-control and out-of-control performance are studied by simulation combined with computation. It turns out that the alternative control charts are not only quite robust against deviations from normality but also perform reasonably well under normality of the observations. The performance of the Empirical Quantile control chart is excellent for all distributions considered, if the Phase I sample is sufficiently large. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Early screening of breast cancer through image recognition technology can significantly increase the survival rate of patients. Therefore, breast cancer pathological image is of great significance for medical diagnosis and clinical research. In recent years, numerous deep learning models have been applied to breast cancer image classification, with deep CNN being a typical representative. Due to the use of multi-depth small convolutional kernels in mainstream CNN architectures such as VGG and Inception, the obtained image features often have high dimensionality. Although high dimensionality can bring more fine-grained features, it also increases the computational complexity of subsequent classifiers and may even lead to the curse of dimensionality and overfitting. To address these issues, a novel embedded kernel CNN principal component feature fusion (CNN-PCFF) algorithm is proposed. The constructed kernel function is embedded in the principal component analysis to form the multi-kernel principal component. Multi-kernel principal component analysis is used to fuse the high dimensional features obtained from the convolution base into some representative comprehensive variables, which are called kernel principal components, so as to achieve the purpose of dimensionality reduction. Any type of classifier can be added based on multi-kernel principal components. Through experimental analysis on two public breast cancer image datasets, the results show that the proposed algorithm can improve the performance of the current mainstream CNN architecture and subsequent classifiers. Therefore, the proposed algorithm in this paper is an effective tool for the classification of breast cancer pathological images. [ABSTRACT FROM AUTHOR]

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the most optimized Kernel Function in Support Vector Machine (SVM) to be utilized on Received Signal Level ( RSL ) dataset for High Speed Rail (HSR). Commonly, RSL is rapidly fluctuated over a large distance and it becomes weakened when it reaches the receiver. Therefore, Kernel Function in SVM is utilized for a more secure, reliable and stable high dimensional mapping especially when the datasets cannot be linearly separable. The most optimized kernel can handle non-linear decision boundaries. In this research, continuous features in RSL dataset are discretized before mapping them into the appropriate kernels. Accordingly, this paper presents a comparison of four well-known Kernel Functions which are Default Function Kernel, Linear Function Kernel, Radial Basis Function Kernel and Poly Kernel Function Kernel. It is observable that Linear Kernel is proven to be more accurate than other Kernels Functions as it has yielded 89% of accuracy and precision as much as 85%. An excellent Recall score and F1 score as much as 85% has also satisfyingly achieved with a minimum RMSE number as much as 0.32. Finally, choosing the most optimal Kernel Functions will definitely help to comprehend the behavior of datasets especially in investigating the HSR scientific research. [ABSTRACT FROM AUTHOR]

In this paper, we study the weighted spaces Lp (ω,Rd ) boundedness of certain class of maximal operators when their kernels belong to the space Lq (Sd−1 ), q > 1. Our results in this paper are improvements and extensions of some previously known results. [ABSTRACT FROM AUTHOR]

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil. [ABSTRACT FROM AUTHOR]

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive σ algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to obtaining analytical solutions for fuzzy fractional differential equations in the context of weighted Caputo-type generalized Hukuhara derivatives. The paper proposes the use of non-singular kernels to improve the accuracy of fractional calculus in fuzzy space and establishes the uniqueness of solutions for fuzzy fractional differential equations. The paper also introduces the concept of fuzzy Laplace transforms to facilitate the solution of these equations. Practical examples, such as the fuzzy fractional Newton’s law of heating and cooling, are provided to demonstrate the effectiveness of the proposed method. Overall, this paper contributes to the development of practical solutions for real-world problems in fuzzy space and enhances the accuracy of fractional calculus in this context. [ABSTRACT FROM AUTHOR]

This paper introduces a new bridge damage indicator, the moving reference influence function (MRIF), to detect bridge bearing damage using deflections inferred from vehicle accelerations. Recently, vehicle acceleration has been used to find the apparent profile (AP) of a bridge when a vehicle passes. This AP consists of bridge profile elevations and bridge deflection components. To describe the relationship between these deflection components and load, a MRIF is proposed for the first time in this paper. An error minimization process is used to find the MRIF and the road surface profile on the bridge. The vehicle acceleration signals used in the paper are assumed to be collected from a partially instrumented vehicle fleet. In the fleet, only the first axle acceleration is collected from each vehicle. To simplify the minimization process, both the MRIF and the bridge profile are represented by kernel density functions. The results show that the bridge profile can be accurately obtained and that bridge bearing damage can be identified from the MRIF. Both area and skewness of the MRIF are damage sensitive and can be used together to find the location and severity of bridge bearing damage. [ABSTRACT FROM AUTHOR]

Strong earthquake geological hazards cause significant social and economic losses. The assessment of post-earthquake socioeconomic development levels is one of the important bases from which to measure the recovery capacity of hazard areas. However, the long-term impact of geological hazards is rarely considered in the assessment of the socioeconomic development level of a mountainous earthquake-stricken area. The purpose of this paper is to study the complex relationship between earthquake geological hazard effects and socioeconomic development in the long-term post-earthquake development and reconstruction of mountainous extremely earthquake-stricken areas, to provide a reference for the study area to achieve regional sustainable development goals and high-quality development. On this basis, using the economic, social, ecological environment and other relevant data from 2008 to 2016, and using unsupervised machine learning algorithms, a socioeconomic development level evaluation model based on spectral clustering was established, and the effects of different kernel function scale parameters on the model were analyzed. The optimal parameters were determined, and the spatiotemporal analysis of the socioeconomic development level of the study area was carried out. The research results show that the performance of the evaluation model is optimal when the output category is 4 and the scale parameter is 0.26, and the scale parameter of the kernel function is an important indicator that affects the accuracy of the model. From 2008 to 2016, the socioeconomic development level of Qingchuan has always been at a very low level, Wenchuan and Beichuan have always been at a low level, Shifang and Mianzhu have always been at a high level, and Pengzhou and Dujiangyan have always been at a very high level. The socioeconomic development level of Anxian, Maoxian and Pingwu fluctuates greatly over time. Another interesting finding is that the socioeconomic development level of the mountainous earthquake-stricken area has a strong correlation with its own industrial structure and landslide hazard effects. The work done in this paper is of great significance for understanding the temporal and spatial effects of hazards and understanding the complexities of regional disaster systems. [ABSTRACT FROM AUTHOR]

In the cloud computing domain, significant strides have been made in performance prediction for cloud workflows, yet link prediction for cloud workflows remains largely unexplored. This paper introduces a novel challenge: joint node and link prediction in cloud workflows, with the aim of increasing the efficiency and overall performance of cloud computing resources. GNN-based methods have gained traction in handling graph-related tasks. The unique format of the DAG presents an underexplored area for GNNs effectiveness. To enhance comprehension of intricate graph structures and interrelationships, this paper introduces two novel models under the DAGCN framework: DAG-ConvGCN and DAG-AttGCN. The former synergizes the local receptive fields of the CNN with the global interpretive power of the GCN, whereas the latter integrates an attention mechanism to dynamically weigh the significance of node adjacencies. Through rigorous experimentation on a meticulously crafted joint node and link prediction task utilizing the Cluster-trace-v2018 dataset, both DAG-ConvGCN and DAG-AttGCN demonstrate superior performance over a spectrum of established machine learning and deep learning benchmarks. Moreover, the application of similarity measures such as the propagation kernel and the innovative GRBF kernel-which merges the graphlet kernel with the radial basis function kernel to accentuate graph topology and node features-reinforces the superiority of DAGCN models over graph-level prediction accuracy conventional baselines. This paper offers a fresh vantage point for advancing predictive methodologies within graph theory. [ABSTRACT FROM AUTHOR]

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic-algorithms and allows to obtaining thresholds of landslide activation from the set of historical occurrences and from the rainfall series. GASAKe can be applied to either single landslides or set of similar slope movements in a homogeneous environment. Calibration of the model is based on genetic-algorithms, and provides for families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from these latter, the corresponding mobility functions (i.e. the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to hydro-geological complexity of the site. Generally, smaller values are expected for shallow slope instabilities with respect to large-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing recorded or forecasted rainfall series. Example of application of GASAKe to a medium-scale slope movement (the Uncino landslide at San Fili, in Calabria, Southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, Southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of landslide occurrence. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e. neither missing nor false alarms) has been achieved against 5 activations. As for temporal validation, the experiments performed by considering the extra dates of landslide activation have also proved satisfactory. In view of early-warning applications for civil protection purposes, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model obtained against different types of slope instabilities, characterized by several historical activations. Nevertheless, further refinements are still needed for applications to landslide risk mitigation within early-warning and decision-support systems. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries are key elements of electric vehicles and energy storage systems, and their accurate State of Charge (SOC) estimation is momentous for battery energy management, safe operation, and extended service life. In this paper, the Multi-Kernel Relevance Vector Machine (MKRVM) and Whale Optimization Algorithm (WOA) are used to estimate the SOC of lithium–ion batteries under different operating conditions. In order to better learn and estimate the battery SOC, MKRVM is used to establish a model to estimate lithium–ion battery SOC. WOA is used to automatically adjust and optimize weights and kernel parameters of MKRVM to improve estimation accuracy. The proposed model is validated with three lithium–ion batteries under different operating conditions. In contrast to other optimization algorithms, WOA has a better optimization effect and can estimate the SOC more accurately. In contrast to the single kernel function, the proposed multi-kernel function greatly improves the precision of the SOC estimation model. In contrast to the traditional method, the WOA-MKRVM has a higher precision of SOC estimation. [ABSTRACT FROM AUTHOR]

Strong impacts and vibrations exist in various structures of rice combine harvesters in harvesting, so the bolt connection structure on the harvesters is prone to loosening and failure, which would further affect the service life and working efficiency of the working device and structure. In this paper, based on the vibration signal acquisition experiment on the bolt and connection structure of the vibrating screen on the harvester, failure feature identification is studied. According to the sensitivity analysis results and the primary extraction of the time-frequency feature, most features have limitations on the identification of failure features of vibrating screen bolts. Therefore, based on the establishment of a high-dimensional feature matrix and multivariate fusion feature matrix, the validity of the feature set was verified based on the whale optimization algorithm. And then, based on the SVM method and high-dimensional mapping of the kernel functions, the high-dimensional feature matrix is trained by the LIBSVM classification decision model. The identify success rates of time domain feature matrix A, frequency domain feature matrix B, WOA-VMD energy entropy matrix C, and normalized multivariate fusion feature matrix G are 64.44%, 74.44%, 81.11%, and more than 90%, respectively, which can reflect the applicability of the failure state identification of the normalized multivariate fusion feature matrix. This paper provided a theoretical basis for the identification of a harvester bolt failure feature. [ABSTRACT FROM AUTHOR]

In this paper, several classes of rough Marcinkiewicz integral operators along surfaces of revolution on product spaces are investigated. We prove the L p boundedness of these operators when their kernels functions belong to a class of functions related to a class of functions introduced by Grafakos-Stefanov. The results in this paper extend and improve several known results on Marcinkiewicz integrals over a symmetric space. [ABSTRACT FROM AUTHOR]

With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various non-sparse, high-energy occlusions in real-world images can lead to erroneous calculations of sample relationships, invalidating the existing distance-based manifold dimensionality reduction techniques. Many types of noise are difficult to capture and filter in the original domain but can be effectively separated in the frequency domain. Inspired by this idea, a novel approach is proposed in this paper, which obtains the high-dimensional manifold structure according to the correlationships between data points in the frequency domain and accurately maps it to a lower-dimensional space, named Frequency domain-based Manifold Dimensionality Reduction (FMDR). In FMDR, samples are first transformed into frequency domains. Then, interference is filtered based on the distribution in the frequency domain, thereby emphasizing discriminative features. Subsequently, an innovative kernel function is proposed for measuring the similarities between samples according to the correlationships in the frequency domain. With the assistance of these correlationships, a graph structure can be constructed and utilized to find the mapping in a low-dimensional space. To further demonstrate the effectiveness of the proposed algorithm, FMDR is employed for the semi-supervised classification problems in this paper. Experiments using public image datasets indicate that, compared to baseline algorithms and state-of-the-art methods, our approach achieves superior recognition performance. Even with very few labeled data, the advantages of FMDR are still maintained. The effectiveness of FMDR in dimensionality reduction and feature extraction of images makes it widely applicable in fields such as image processing and image recognition. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of the multi-dimensional integral transform with the Fox H-function in the kernel in weighted spaces with integrable functions in the domain R + n with positive coordinates. Due to the generality of the Fox H-function, many special integral transforms have the form studied in this paper, including operators with such kernels as generalized hypergeometric functions, classical hypergeometric functions, Bessel and modified Bessel functions and so on. Moreover, most important fractional integral operators, such as the Riemann–Liouville type, are covered by the class under consideration. The mapping properties in Lebesgue-weighted spaces, such as the boundedness, the range and the representations of the considered transformation, are established. In special cases, it is applied to the specific integral transforms mentioned above. We use a modern technique based on the extensive use of the Mellin transform and its properties. Moreover, we generalize our own previous results from the one-dimensional case to the multi-dimensional one. The multi-dimensional case is more complex and needs more delicate techniques. [ABSTRACT FROM AUTHOR]

The goal of this paper is to establish Green function estimates for a class of purely discontinuous symmetric Markov processes with jump kernels degenerate at the boundary and critical killing potentials. The jump kernel and the killing potential depend on several parameters. We establish sharp two-sided estimates on the Green functions of these processes for all admissible values of the parameters involved. Depending on the regions where the parameters belong, the estimates on the Green functions are different. In fact, the estimates have three different forms. As applications, we prove that the boundary Harnack principle holds in certain region of the parameters and fails in some other region of the parameters. Together with the main results of our previous paper [Potential Anal., online, 2021], we completely determine the region of the parameters where the boundary Harnack principle holds. [ABSTRACT FROM AUTHOR]

We determine the spreading speed of an epidemic model with nonlocal diffusion and free boundary. The model is evolved from a degenerate reaction-diffusion model of Capasso and Maddalena (J Math Biol 13:173–184, 1981), and was studied in Zhao et al. (Commun Pure Appl Anal 19:4599–4620, 2020) recently, where it was shown that as time goes to infinity, the population of the infective agents either vanishes or spreads successfully. In this paper, we show that when spreading is successful, the asymptotic spreading speed is finite or infinite depending on whether a threshold condition is satisfied by the kernel function governing the spatial dispersal of the agents. The proof relies on a rather complete understanding of the associated semi-wave problem and traveling wave problem. For free boundary models, the case of infinite spreading speed, also known as accelerated spreading, is only recently shown to happen in Du et al. (J Math Pure Appl 154:30–66, 2021) for a single species Fisher-KPP model; this paper is the first to show that it happens to a very different two species model with free boundary. This suggests that accelerated spreading is a rather common phenomenon for free boundary problems with nonlocal diffusion. In contrast, for the corresponding models with local diffusion, the spreading can only proceed with finite speed. [ABSTRACT FROM AUTHOR]

This work presents a new iterative method for optimally selecting a vertical retrieval grid based on the location of the information content while accounting for inter-level correlations. Sample atmospheres initially created to parametrise the RTTOV forward model are used to compare the presented iterative vertical selection method with two other common approaches, which are using levels of equal vertical spacing and selecting levels based on the cumulative trace of the averaging kernel matrix (AKM). This new method is shown to outperform compared methods for synthesized profile retrievals with IASI of temperature, H2O, O3, CH4, and CO. However, the benefits of using the more complicated iterative approach compared to the simpler method of referencing the cumulative trace of the AKM are slight and may not justify the added effort. Furthermore, comparing retrievals using a globally optimized static grid vs. an atmosphere specific one shows that a static grid is likely appropriate for retrievals of O3, CH4, and CO. However, developers of temperature and H2O retrieval schemes may at least consider using adaptive or location specific vertical retrieval grids. [ABSTRACT FROM AUTHOR]

The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the scattered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained. [ABSTRACT FROM AUTHOR]

Due to the increasing interest in wireless networks, the availability of spectrum has become a challenge. With the help of cognitive radio, a promising technology, can be overcome this issue. One of the most challenging tasks in this technique is finding the available spectrum holes. The increasing interest in machine learning techniques for spectrum sensing (SS) has led to the development of several novel methods. In this paper, we use the support vector machine with the kernel transformation that are designed to improve the performance of SS, such as such as Linear kernel, Radial Basis Function or Gaussian kernel, Polynomial kernel and Sigmoid kernel. One of the main reasons why the kernel functions are used is due to the possibility of having a non-linear dataset. The performance of kernel functions is compared in terms of accuracy, precision, recall, f1_score and confusion matrix for different number of users such as 100, 500 and 1000. Among all these, Polynomial kernel SVM has shown better performance of 96%, 97% and 100% accuracy for 100, 500 and 1000 number of users. In addition, this paper presents a comparison of the proposed and existing methods, where the proposed method has shown a better performance. [ABSTRACT FROM AUTHOR]

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%. [ABSTRACT FROM AUTHOR]

Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry. [ABSTRACT FROM AUTHOR]

Rotor imbalances in wind turbines present safety risks and lead to economic losses, and a method to diagnose rotor imbalances is urgently needed. A diagnostic method for rotor imbalances is proposed in this paper. First, a signal reconstruction method is proposed, and a novel index is used to determine the number of components used in signal decomposition in order to effectively address the interference by noise on the sensor. Second, an entropy calculation method is proposed, and the Gaussian kernel function is used to replace the fuzzy functions. The results indicate significant differences for different types of rotor imbalances. Moreover, it exhibits good noise robustness and a low dependence on the data length. Third, a support vector machine with multiscale kernels is proposed, and kernel functions with various characteristics and scales are combined. It has a well-distributed hyperplane and better classification performance, and it is robust to wind conditions. Finally, the method is tested and verified with varying levels of noise and turbulence. The results demonstrate satisfactory performance because the proposed method can effectively identify rotor imbalances under different noise and wind conditions. [ABSTRACT FROM AUTHOR]

The characteristics of nonlinear and strong coupling of a bearingless permanent magnet synchronous motor (BPMSM) greatly affect the improvement of its control performance. In the traditional decoupling control of least squares support vector machine (LSSVM) inverse system, the kernel function parameter σ and regularization parameter c are determined according to the empirical value, but not the nonoptimal value, so large error exists in the decoupling control. Therefore, this paper proposes a decoupling control method of LSSVM inverse system based on improved grey wolf optimization algorithm (IGWO). Firstly, the working principle of the BPMSM is described, and the mathematical model is derived. Secondly, the reversibility of the BPMSM is analyzed, and the σ and c of LSSVM are optimized by IGWO, before establishing a generalized inverse system for decoupling control. Thirdly, the simulation tests of the speed regulation and anti-interference are carried out, which show that the decoupling performance of the proposed method is better than the traditional LSSVM inverse system method. Finally, the dynamic experiments, static experiments, and anti-interference experiments are carried out. The feasibility and superiority of the proposed method are verified according to the built experimental platform. [ABSTRACT FROM AUTHOR]

In this paper, Gaussian Process Regression (GPR)‐based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non‐parametric kernel‐based learning method having the ability to provide correct predictions with uncertainty in measurements. The proposed model provides an hourly and monthly load forecast for an Australian city and four Indian cities in the Maharashtra state. Twelve GPR models are trained with historical datasets including hourly load and environmental data. To evaluate the trained model, the actual and predicted load demand curve is plotted and mean average percentage error (MAPE) is calculated corresponding to different kernel functions of the GPR model. To the best of the author's knowledge, the prediction of load demand using GPR for Indian cities of Maharashtra state has been made for the first time. The calculated MAPE in LF is 0.15% for Australia and 0.002%, 0.209%, 0.077%, and 0.140% for Indian cities viz. Nasik, Bhusawal, Kolhapur, and Aurangabad, respectively. The test results illustrate that minimum MAPE in load prediction is obtained using the proposed model that is GPR with 'Exponential' kernel functions. Furthermore, the comparative analysis with the existing approaches confirms the dominance of the proposed model. [ABSTRACT FROM AUTHOR]

Asymmetry plays a significant role in the transmission dynamics in novel discrete fractional calculus. Few studies have mathematically modeled such asymmetry properties, and none have developed discrete models that incorporate different symmetry developmental stages. This paper introduces a Taylor monomial falling function and presents some properties of this function in a delta fractional model with Green's function kernel. In the deterministic case, Green's function will be non-negative, and this shows that the function has an upper bound for its maximum point. More precisely, in this paper, based on the properties of the Taylor monomial falling function, we investigate Lyapunov-type inequalities for a delta fractional boundary value problem of Riemann–Liouville type. [ABSTRACT FROM AUTHOR]

In recent days, digital communication has become inevitable. The scope of this research is to provide a theoretical framework of automatically classification of question paper. It can be helpful to the library of any University to automate their archival process. A survey has been conducted to understand the existing systems in this research area. After doing the survey we observed that many authors have done significant work in document classification but little work has been done to automatically store the documents in particular folder. Therefore, there is a huge scope to develop working model of the framework suggested in this paper. [ABSTRACT FROM AUTHOR]

Purpose: To find the quasi-static thermoelastic stress and displacement, the proposed model looks at how the microstructures interact with each other and how the temperature changes inside a rod. It uses the fractional-order dual-phase-lag (FODPL) theory to derive analytical solutions for one-dimensional problems in nonsimple media within the MDD framework. The dimensionless equations are used to analyze a finite rod experiencing the heat sources continuously distributed over a finite portion of the rod which vary with time according to the ramp-type function with other sectional heat supplies kept at zero temperature. The study introduces a technique using integral transforms for exact solutions in the Laplace transform domain for different kernel functions. Design/methodology/approach: A novel mathematical model incorporating dual-phase-lags, two-temperatures and Riesz space-fractional operators via memory-dependent derivatives has been established to analyze the effects of thermal stress and displacement in a finite rod. The model takes into account the continuous distribution of heat sources over a finite portion of the rod and their time variation according to the ramp-type function. It incorporates the finite Riesz fractional derivative in two-temperature thermoelasticity with dual-phase-lags via memory effect, and its solution is obtained using Laplace transform with respect to time and sine-Fourier transform with respect to spatial coordinates defined over finite domains. Findings: In memory-dependent derivatives, thermal field variables are strongly influenced by the phase-lag heat flux and temperature gradient. The non-Fourier effects of memory-dependent derivatives substantially impact the distribution and history of the thermal field response, and energy dissipation may result in a reduction in temperature without heat transfer. The temperature, displacement and stress profile exhibit a reduced magnitude with the MDD effect compared to when the memory effect is absent (without MDD). To advance future research, a new categorization system for materials based on memory-dependent derivative parameters, in accordance with the principles of two-temperature thermoelasticity theory, must be constructed. Research limitations/implications: The one-dimensional assumption introduces limitations. For example, local heating of a one-dimensional plate will not extend radially, and heating one side will not heat the surrounding sides. Furthermore, while estimating heat transfer, object shape limits may apply. Originality/value: This paper aims to revise the classical Fourier law of heat conduction and develop analytical solutions for one-dimensional problems using fractional-order dual-phase-lag (FODPL) theory in nonsimple media in the context of MDD. [ABSTRACT FROM AUTHOR]

This paper investigates the capabilities of the resonant cavity system (ResCav) for detecting tampered integrated circuits (ICs) within supply chains. Prior research showcased ResCav's ability to discern minor circuit variations, this study focuses on enhancing supervised classification results and introduces a one-class support vector machine (SVM) approach with a modified radial basis function kernel for novelty detection. Through finer hyperparameter tuning, the system achieves improved classification accuracy, demonstrating its potential to identify nuanced alterations with even higher precision and recall rates. Additionally, the application of a one-class SVM enables the detection of tampered ICs without reliance on labeled datasets, expanding utility in scenarios where access is limited to golden ICs. These advancements in ResCav's capabilities signify progress in failure prevention methodologies, offering an efficient and non-destructive solution crucial for safeguarding against counterfeit and non-conforming components infiltrating critical supply chains. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has grown significantly in recent years, allowing devices with sensors to share data via the internet. Despite the growing popularity of IoT devices, they remain vulnerable to cyber-attacks. To address this issue, researchers have proposed the Hybrid Intrusion Detection System (HIDS) as a way to enhance the security of IoT. This paper presents a novel intrusion detection model, namely QSVM-IGWO, for improving the detection capabilities and reducing false positive alarms of HIDS. This model aims to improve the performance of the Quantum Support Vector Machine (QSVM) by incorporating parameters from the Improved Grey Wolf Optimizer (IGWO) algorithm. IGWO is introduced under the hypothesis that the social hierarchy observed in grey wolves enhances the searching procedure and overcomes the limitations of GWO. In addition, the QSVM model is employed for binary classification by selecting the kernel function to obtain an optimal solution. Experimental results show promising performance of QSVM-IGWO in terms of accuracy, Recall, Precision, F1 score, and ROC curve, when compared with recent detection models. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the fractional hybrid integro-differential equations with Dirichlet boundary conditions. We first prove the existence of a unique solution for the equation using a fixed point technique. Our main goal is to obtain the best approximation using optimal controllers. After studying the stability, we present the reproducing kernel Hilbert space numerical method to obtain approximate solutions to the equation. We finally conclude with numerical results. [ABSTRACT FROM AUTHOR]

Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we mainly consider the local kernel estimators for regression models with a binary response and the predictors including spherical variables. We apply the random forests kernel to nonparametric binary regression models with spherical predictors. Simulation experiments and real examples are used to validate the performance of the new models. Compared with the classical von Mises–Fisher kernel and the linear-spherical kernel, the random forests kernel has better fitting effect and faster computation speed. Compared with other classifiers, the models proposed in this paper have better classification performance in both low and high dimensional cases. [ABSTRACT FROM AUTHOR]

Extended kalman filter (EKF) is widely used to integrated navigation system of global navigation satellite system (GNSS) and strapdown inertial navigation system (SINS). However, non-Gaussian noise and the uncertainty of measurement noise can seriously reduce the performance of EKF, so it is difficult to obtain an optimal GNSS/INS integration solution. At present, non-Gaussian noise processing is still a difficult issue in filter research. In this paper, an adaptive and robust maximum correntropy extended kalman filter (MCEKF) method based on Cauchy kernel function is proposed to solve the above problem. Thanks to the excellent properties of Cauchy kernel function, the proposed method can effectively avoid filter faults and has better stability. However, the performance of MCEKF depends on the select of kernel bandwidth parameter, which is a difficult problem in the engineering application of MCEKF. In this paper, the switching kernel bandwidth algorithm is employed to adaptively estimate the kernel bandwidth parameter to solve the tradeoff between convergence rate and steady-state misalignment in the MCEKF algorithm under constant kernel bandwidth. Finally, the performance of the proposed algorithm is verified by two sets of vehicle-mounted precise point positioning (PPP) and SINS tightly coupled integration experiments in urban environment. The results show that compared with EKF, the proposed method can significantly improve the positioning accuracy, that is, the PPP/SINS positioning accuracy based on GE, GR and GRE system is improved by 25.1 %, 2.5 % and 17.0 % in D196 experiment, and 23.5 %, 16.5 % and 31.8 % in D107 experiment, respectively. The velocity and attitude accuracy of the two methods are similar. The velocity errors of all schemes can reach cm/s level. The roll, pitch and heading errors of navigation-grade inertial sensor are all less than 0.12 degree. The roll and pitch errors of MEMS are less than 1.0 degree, the heading error of the G, GE, GR, and GRE systems are 1.719, 1.464, 1.676, and 1.475 degree, respectively. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a new fractional extension of the interval Hermite–Hadamard ( H H ), H H –Fejér, and Pachpatte-type inequalities for left- and right-interval-valued harmonically convex mappings ( L R I V H convex mappings) with an exponential function in the kernel. We use fractional operators to develop several generalizations, capturing unique outcomes that are currently under investigation, while also introducing a new operator. Generally, we propose two methods that, in conjunction with more generalized fractional integral operators with an exponential function in the kernel, can address certain novel generalizations of increasing mappings under the assumption of L R I V convexity, yielding some noteworthy results. The results produced by applying the suggested scheme show that the computational effects are extremely accurate, flexible, efficient, and simple to implement in order to explore the path of upcoming intricate waveform and circuit theory research. [ABSTRACT FROM AUTHOR]

This paper is devoted to a nonlocal dispersal logistic model with seasonal succession and free boundaries, where the free boundaries represent the expanding front and the seasonal succession accounts for the effect of two different seasons. Technically, this free boundary problem is much more difficult than the case without seasonal succession since the coefficients are all time periodic and piecewise continuous. We prove the existence and uniqueness of global solution, and then examine the long-time dynamical behaviour and the criteria that completely determine when spreading and vanishing can happen, revealing some significant differences from the model without seasonal succession. Moreover, we use a "thin-tail" condition on the kernel function to estimate the asymptotic speeds of (g, h) and the asymptotic spreading speed of u, which is achieved by solving the associated semi-wave problem. [ABSTRACT FROM AUTHOR]

With the application of big data, artificial intelligence, and other related technologies in the field of education, using machine learning to carry out early warning for course learning has become an effective means to improve teaching quality. However, in the scene of early warning, the samples are significantly less than the ordinary samples, and the general clustering or classification methods are difficult to achieve good results. Therefore, this paper proposes an early warning for course learning method based on SMOTE and OCSVM. First, collect and preprocess students' college entrance examination information and online course learning information data. Second, use SMOTE algorithm to expanding the samples. Then, the OCSVM model is designed, the Gaussian kernel function is used, and the Lagrange multiplier is used to solve the optimization problem for the optimization objective. The qualified student samples are selected for learning, and the classifier is trained, so as to classify the student data and realize the early warning of course learning. Select recall and F1_Score to evaluate the model, and comparative experiments are carried out. From the experiment, it is clear that in most cases, the method proposed in this paper is superior to the original sample and traditional methods in recall rate and F1_Score. [ABSTRACT FROM AUTHOR]

The recent trends in collecting huge datasets have posed a great challenge that is brought by the high dimensionality and aggravated by the presence of irrelevant dimensions. Machine learning models for regression is recognized as a convenient way of improving the estimation for empirical models. Popular machine learning models is support vector regression (SVR). However, the usage of principal component analysis (PCA) as a variable reduction method along with SVR is suggested. The principal component analysis helps in building a predictive model that is simple as it contains the smallest number of variables and efficient. In this paper, we investigate the competence of SVR with PCA to explore its performance for a more accurate estimation. Simulation study and Renal Failure (RF) data of SVR optimized by four different kernel functions; linear, polynomial, radial basis, and sigmoid functions using R software, version (Rx64 3.2.5) to compare the behavior of e-SVR and v-SVR models for different sample sizes ranges from small, moderate to large such as: 50, 100, and 150. The performance criteria are root mean squared error (RMSE) and coefficient of determination R2 showed the superiority of e-SVR over v-SVR. Furthermore, the implementation of SVR after employing PCA improves the results. Also, the simulation results showed that the best performing kernel function is the linear kernel. For real data the results showed that the best kernels are linear and radial basis function. It is also clear that, with e-SVR and v-SVR, the RMSE values for almost kernel functions decreased with increasing sample size. Therefore, the performance of e-SVR improved after applying PCA. In addition sample size n = 50 gave good results for linear and radial kernel. [ABSTRACT FROM AUTHOR]

In the study of stochastic systems, the committor function describes the probability that a system starting from an initial configuration x will reach a set B before a set A. This paper introduces an efficient and interpretable algorithm for approximating the committor, called the "fast committor machine" (FCM). The FCM uses simulated trajectory data to build a kernel-based model of the committor. The kernel function is constructed to emphasize low-dimensional subspaces that optimally describe the A to B transitions. The coefficients in the kernel model are determined using randomized linear algebra, leading to a runtime that scales linearly with the number of data points. In numerical experiments involving a triple-well potential and alanine dipeptide, the FCM yields higher accuracy and trains more quickly than a neural network with the same number of parameters. The FCM is also more interpretable than the neural net. [ABSTRACT FROM AUTHOR]