Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Development of a Quantitative Prediction Support System Using the Linear Regression Method

The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company’s areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.

Research on a non-linear chaotic prediction model for urban traffic flow

In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.

An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

In this paper, we propose an adaptive strategy based on the linear prediction of queue length to minimize congestion in Barabaisi-Albert （BA） scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion.

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.

Multi-Level Inverter Linear Predictive Phase Composition Strategy for UPQC

The power system is facing numerous issues when the distributed gen-eration is added to the existing system.The existing power system has not been planned with flawless power quality control.These restrictions in the power trans-mission generation system are compensated by the use of devices such as the Static Synchronous Compensator(STATCOM),the Unified Power Quality Con-ditioner(UPQC)series/shunt compensators,etc.In this work,UPQC’s plan with the joint activity of photovoltaic(PV)exhibits is proposed.The proposed system is made out of series and shunt regulators and PV.A boost converter connects the DC link to the PV source,allowing it to compensate for voltage sags,swells,vol-tage interferences,harmonics,and reactive power issues.In this paper,the fea-tures of a seven-level Cascaded H-Bridge Multi-Level idea are applied to shunt and series active filter changeovers to reduce Total Harmonic Distortion and com-pensate for voltage issues.Despite its power quality capacity for common cou-pling,the proposed system can inject the grid’s dynamic power.During voltage interference,it can also provide a piece of delicate burden power.The simulation is carried out with the help of MATLAB/SIMULINK programming,and the results are compared to those of other conventional methods.

Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature.

The Study of Contact Pressure Analyses and Prediction of Dynamic Fatigue Life for Linear Guideways System

The application of the linear guideways is very extensive, such as automation equipment, heavy-duty carry equipment, heavy-cut machining tool, CNC grinding machine, large-scale planning machine and machining center with the demand of high rigidity and heavy load. By means of the study of contact behavior between the roller/guideway and roller/slider, roller type linear guideways can improve the machining accuracy. The goal of this paper is to construct the fatigue life model of the linear guideway, with the help of the contact mechanics of rollers. In beginning, the analyses of the rigidity of a single roller compressed between guideway and slider was conducted. Then, the normal contact pressure of linear guideways was obtained by using the superposition method, and verified by the FEM software (ANSYS workbench). Finally, the bearing life theory proposed by Lundberg and Palmgren was used to describe the contact fatigue life.

基于LPV-MPC的风电机组功率-载荷协调控制策略

大型风电机组变桨控制是一个复杂的非线性控制任务,如何在系统约束和风速扰动的条件下实现功率调节和载荷降低成为难题。针对该问题,文章采用机理与参数辨识方法构建了一个面向控制的线性变参数(LPV)模型,LPV模型引入间隙度量理论来降低模型复杂度,为控制设计提供了更加精确的线性模型。设计了时变卡尔曼滤波器,实现了风机的最优状态估计,基于获得的最优状态,提出了自适应模型预测(MPC)变桨控制器,MPC控制器能够根据实时风速变化调整预测模型,在满足系统约束的条件下实现大型风电机组功率-载荷的协调控制。OpenFAST对比仿真实例表明了模型与控制器的有效性。

LINEAR PREDICTION APPROACH IN AIRBORNE ADAPTIVE ARRAYS

To cope with the time-varying and Dopper-broadened clutter in airborne phase array radars, it is required that the signal processing should be adaptive and two-dimensional both in time and in space. However, the optimum two-dimensional adaptive processing is hard to realize real-timely because it requires a large amount of computation. From the idea of approximating the clutter process by using an auto regressive process, a linear prediction approach is proposed to realize the adaptive space-time processing of airborne adaptive array signals. The research shows that the clutter process can be well approximated by a low-order AR process, so a low-order linear prediction receiver can get a sub-optimum performance at a very low expense. Besides, the low-order linear prediction receiver has additional degrees of freedom to cope with other colored noises and interferences. In consideration of the many advantages of the linear prediction receiver in both algorithms and realizations, it has a good prospect in its application to air borne adaptive array signal processing.

KLT-based local linear prediction of chaotic time series

In the reconstructed phase space, based on the Karhunen-Loeve transformation （KLT）, the new local linear prediction method is proposed to predict chaotic time series. ＆ noise-free chaotic time series and a noise added chaotic time series are analyzed. The simulation results show that the KLT-based local linear prediction method can effectively make one-step and multi-step prediction for chaotic time series, and the one-step and multi-step prediction accuracies of the KLT-based local linear prediction method are superior to that of the traditional local linear prediction.

Pyramid Linear Prediction Coding for Images

Motivated by wavelet transform, this paper presents a pyramid linear prediction coding (PLPC) algorithmfor digitial images.The algorithm otltpots the rough colltour of an image and a prediction ermr sequence. In contrastto the conventional linear prediction method, PLPC exhibits very little sensitivity to channel ermrs and provides amore efficient compression performance. The results of simulations with Lena 512 X 512 and bitrates ranging from0.17 to 3.2 (lossless)bits/pixel are given to show that the PLPC method is very suitable for the human visualperception.

ASP-Based Programs of Best Linear Unbiased Prediction-Estimated Breeding Values in Breeding Stock

In order to improve the breeding effect of livestock, the data were read from an Excel file with Active Server Page （ASP） programs, and the breeding values of breeding stock were calculated by best linear unbiased prediction （BLUP） method.

Estimators of Linear Regression Model and Prediction under Some Assumptions Violation

The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ-0.49).

Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method

The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

Linear extrapolation for prediction of tensile creep compliance of polyvinyl chloride

The universal creep equation is successful in relating the creep （ε） to the aging time （t） , coefficient of retardation time （β） , and intrinsic time （ to ）. This relation was used to treat the creep experimental data for polyvinyl chloride （ PVC ） specimens at a given stress and different aging times. The βgs found by the “polynomial fitting” method in this work instead of the “middle - point” method reported in the literature. The unified master line was constructed with the treated data and curves according to the universal equation. The master line can be used to predict the long- term creed behavior and lifetime by extrapolating.

Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.

AN IMPROVED 2.4kb/s MIXED EXCITATION LINEAR PREDICTION VOCODER

This letter presents two improvements on 2.4 kb/s Mixed-Excitation Linear Prediction (MELP) vocoder. The one is a new parameter Redzc named energy to differential zerocrossing rate which is used in adaptation of V/UV decision of transitional segments and low energy level speech segments. The other is a multi-path searching method for Multi-Stage Vector Quantization (MSVQ) of line spectral frequency. Subjective tests show that the intelligiblity and naturallity of improved MELP vocoder are preferable to those of the original one.