Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Prediction of Potential Sorghum Suitability Distribution in China Based on Maxent Model

It is increasingly relevant to study the effects of climate change on species habitats. Using a maximum entropy model, 22 environmental factors with significant effects on sorghum habitat distribution in China were selected to predict the potential habitat distribution of sorghum in China. The potential distribution of sorghum under baseline climate conditions and future climate conditions (2050s and 2070s) under two climate change scenarios, RCP4.5 and RCP8.5, were simulated, and the receiver operating curve under the accuracy of the model was evaluated using the area under the receiver operating curve (AUC). The results showed that the maximum entropy model predicted the potential sorghum habitat distribution with high accuracy, with Bio2 (monthly mean diurnal temperature difference), Bio6 (minimum temperature in the coldest month), and Bio13 (rainfall in the wettest month) as the main climatic factors affecting sorghum distribution among the 22 environmental factors. Under the baseline climate conditions, potential sorghum habitats are mainly distributed in the southwest, central, and east China. Over time, the potential sorghum habitat expanded into northern and southern China, with significant additions and negligible decreases in potential sorghum habitat in the study area, and a significant increase in total area, with the RCP8.5 scenario adding much more area than the RCP4.5 scenario.

基于Attention-LSTM的短期电力负荷预测

电力负荷预测的准确性受到多种因素的干扰,如气候变化、经济发展以及区域差异等,这些因素使得电力负荷呈现出显著的不稳定性和复杂的非线性特征,从而增加了提高预测精度的难度。为了应对这一挑战,本文创新性地引入了一种结合自注意力机制与长短期记忆网络(LSTM)的预测方法。通过在美国某一地区的实际用电负荷数据验证模型,实验结果表明,该方法的决定系数(R2)为0.96,平均绝对误差(MAE)为0.023,均方根误差(RMSE)为0.029,提升了预测的准确性。这不仅证明了所提模型在提高电力负荷预测精度方面的有效性,也为其在船舶电力负荷预测的应用奠定了一定的基础。

F-TabNet模型在工程评标优化中的应用研究

为提高工程项目评标的准确性和合理性,本研究采用模糊综合评价方法(fuzzy comprehensive appraisal,FCA)处理评价指标体系中的模糊因素,并结合TabNet模型对评价方法进行了创新性的优化研究。该研究不仅涉及特征选择、模型训练与预测,还提出了一个新颖的F-TabNet评价模型,该模型有效地利用TabNet网络的强大表达能力。研究成果验证了F-TabNet评价方法在工程项目评标中的应用价值,该方法不仅能够准确预测评标结果,而且具备较强的解释性,这对于推动项目评标决策的科学性和合理化具有重要意义。

THEORETICAL BASIS AND GENERAL OPTIMAL FORMULATIONS OF ISOPARAMETRIC GENERALIZED HYBRID/MIXED ELEMENT MODEL FOR IMPROVED STRESS ANALYSIS

By the modified three-field Hu-Washizu principle, this paper establishes a theoretical founda- tion and general convenient formulations to generate convergent stable generalized hybrid/mixed cle- ment (GH/ME) model which is invariant with respect to coordinate, insensitive to geometric distortion and suitable for improved stress computation. In the two proposed formulations, the stress equilibrium and orthogonality constraints are imposed through incompatible displacement and internal strain modes respectively. The proposed model by the general formulations in this paper is characterized by including as- sumed stress/strain, assumed stress, variable-node, singular, compatible and incompatible GH/ME models. When using regular meshes or the constant values of the isoparametric Jacobian Det in the assumed strain in- terpolation, the incompatible GH/ME model degenerates to the hybrid/mixed element model. Both general and concrete guidelines for the optimal selection of element shape functions are suggested. By means of the GH/ME theory in this paper, a family of new GH/ME can be and have been easily constructed. The software can also be developed conveniently because all the standard subroutines for the corresponding isoparametric displacement elements can be utilized directly.

A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations

A new model for predicting the total tree height for harvested stems from cut-to-length(CTL)harvester data was constructed for Pinus radiata(D.Don)following a conceptual analysis of relative stem profi les,comparisons of candidate models forms and extensive selections of predictor variables.Stem profi les of more than 3000 trees in a taper data set were each processed 6 times through simulated log cutting to generate the data required for this purpose.The CTL simulations not only mimicked but also covered the full range of cutting patterns of nearly 0.45×106 stems harvested during both thinning and harvesting operations.The single-equation model was estimated through the multipleequation generalized method of moments estimator to obtain effi cient and consistent parameter estimates in the presence of error correlation and heteroscedasticity that were inherent to the systematic structure of the data.The predictive performances of our new model in its linear and nonlinear form were evaluated through a leave-one-tree-out cross validation process and compared against that of the only such existing model.The evaluations and comparisons were made through benchmarking statistics both globally over the entire data space and locally within specifi c subdivisions of the data space.These statistics indicated that the nonlinear form of our model was the best and its linear form ranked second.The prediction accuracy of our nonlinear model improved when the total log length represented more than 20%of the total tree height.The poorer performance of the existing model was partly attributed to the high degree of multicollinearity among its predictor variables,which led to highly variable and unstable parameter estimates.Our new model will facilitate and widen the utilization of harvester data far beyond the current limited use for monitoring and reporting log productions in P.radiata plantations.It will also facilitate the estimation of bark thickness and help make harvester data a potential source of taper data to reduce the intensity and cost of the conventional destructive taper sampling in the fi eld.Although developed for P.radiata,the mathematical form of our new model will be applicable to other tree species for which CTL harvester data are routinely captured during thinning and harvesting operations.

Evaluation of creep models for frozen soils

To model the creep behavior of frozen soils, three creep stages have to be reasonably described （i.e., primary, secondary and tertiary stages）. Based on a series of uniaxial creep test results, three creep models were evaluated. It was shown that hypoplastic creep model has high prediction accuracy for both creep strain and strain rate in a wide stress range. The elementary rheological creep model can only be used for creep strains at low stress levels, because of the restraints of its mathematical construction. For the soft soil creep model, the progressive change from the primary to secondary and tertiary stages cannot be captured at high stress levels. Therefore, the elementary rheological and soft soil creep models can only be used for low stress levels without a tertiary stage; while the hypoplastic creep model is applicable at high stress levels with the three creep stages.

Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing.The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator(URLs)analysis.The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions,which deals with the detection of phishing.Contrarily,the URLs in both classes from the login page due,considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs.In addition,some model reduces the accuracy rather than training the base model and testing the latest URLs.In addition,a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign.A new dataset called the MUPD dataset is used for evaluation.Lastly,a prediction model,the Dense forward-backwards Long Short Term Memory(LSTM)model(d−FBLSTM),is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5%on the initiated login URL dataset.

Prediction of the Shearing Property of Worsted Fabrics Using BP Neural Network

In this paper, three layers of BP neural network were used to model the shearing properties of worsted fabrics. We train the neural network models with 27 kinds of fabrics, and then use 6 kinds of fabrics to validate the accuracy of the model. The result shows that the predicted accuracy of the models is about 85%.

Comparisons of Oil Production Predicting Models

Feasibility of oil production predicting results influences the annual planning and long-term field development plan of oil field, so the selection of predicting models plays a core role. In this paper, three different predicting models are introduced, they are neural network model, support vector machine model and GM (1, 1) model. By using these three different models to predict the oil production in XINJIANG oilfield in China, advantages and disadvantages of these models have been discussed. The predicting results show: the fitting accuracy by the neural network model or by the support vector machine model is higher than GM (1, 1) model, the prediction error is smaller than 10%, so neural network model and support vector machine model can be used to short-term forecast of oil production;predicting accuracy by GM (1, 1) model is not good, but the curve trend with GM (1, 1) model is consistent with the downward trend in oil production, so GM (1, 1) predicting model can be used to long-term prediction of oil production.

基于预知维修的小麦播种机运行监控系统设计

为了减少播种机故障频率,提升小麦播种机的播种效率和播种质量,基于预知维修对小麦播种机的运行监控系统进行了设计。系统的主要组成包括主控单片机、检测系统、显示监控系统、报警系统及电源。为了对播种机进行预知维修,将灰色模型和神经网络模型结合,建立了动态灰色神经网络模型,并进行了算法设计。为了验证小麦播种机监控系统性能和预知维修算法的有效性,对其进行了监测精度和趋势预测试验,结果表明:监测系统的监测精度较高,播种机可有效对数据趋势进行预测。

智能手机GNSS数据质量分析与随机模型建立

为提高移动智能终端定位精度,利用小米8和华为Mate20手机采集多个卫星系统的观测数据并进行质量分析,通过不同卫星系统的伪距残差分别对信噪比模型参数进行估计,最后进行静态伪距单点定位实验。结果表明,手机端观测的BDS和GPS伪距精度接近,且高于GLONASS;相较于高度角,伪距残差与信噪比的相关性更强;在信噪比模型参数估计中,小米8拟合效果优于华为Mate20,BDS拟合效果差于其他卫星系统;参数拟合后的信噪比模型在水平和高程方向的定位精度分别为2.37 m和8.38 m,相较于高度角模型,定位精度提升约10%。

面向切削力修正的机床主轴回转精度预测实验分析

为了提高机床控制精度,设计一种可以精确预测面向切削力修正的主轴回转精度分析方法。为验证回转精度预测准确性,建立一套不需要通过标准球实现的主轴回转精度分析系统,可以针对具体切削工况开展主轴回转精度测试。研究结果表明:随着进给速率和切削深度改变,形成了具有规律性的同步误差,切深受到同步误差因素的影响程度最大,而进给速度次之。受切削载荷影响,主轴回转精度显著改变,同步误差和切削载荷具有正相关关系。在变进给及变切宽条件下,测试结果与仿真结果相近,最大误差为0.2μm。设定切宽12 mm与进给速度1200 mm/min时,分离得到的圆度误差与100 r/min空转时圆度误差相比相吻合。

2017—2022年浙江省其他感染性腹泻病ARIMA模型预测精度分析

目的 比较分析2017—2022年浙江省其他感染性腹泻病自回归移动平均(autoregressive integrated moving average, ARIMA)模型预测精度,探索更为精准的预测模型以指导传染病防控。方法 收集2011年4月—2022年12月的浙江省其他感染性腹泻病发病率资料,将2011年4月—2021年的数据分为6个时间段,拟合优选出各数据段的最优ARIMA模型,分别对2017—2022年浙江省其他感染性腹泻病的发病率进行预测,以对应年度实际发病率验证模型,比较各数据段最优模型的年平均相对误差和月相对误差。结果 2011年4月—2022年12月,浙江省总计报告其他感染性腹泻病1 272 546例,年均发病率为186.97/10万。拟合的6个最优ARIMA模型对2017—2022年预测值与实际值的年平均相对误差分别为19.27%、28.59%、12.46%、77.87%、16.53%、40.21%,最小的月相对误差分别为0.69%、1.16%、0.57%、3.27%、0.45%、8.07%。结论 虽然ARIMA模型预测其他感染性腹泻病有时会存在较大误差,但短期预测仍可以为该病的早期精准防控提供参考依据。

发泡聚乙烯最大加速度-静应力曲线快速获取方法研究

目的研究快速、准确预测最大加速度-静应力曲线的方法。方法首先利用落锤冲击试验机获取了5个不同高度下,5种不同厚度的发泡聚乙烯的最大加速度-静应力曲线。在此基础上,分析对比文中3种不同的改进拟合法与已有的动应力与动能量多项式拟合法的区别。结果研究发现,当不区分高度的情况下,以最大加速度因子为函数值,以跌落高度、衬垫厚度、静应力为变量进行拟合时,其代表预测精度R^(2)的平均为0.835,相比动应力与动能量多项式拟合法的0.2996要高。但曲线右侧的预测精度偏低。引入以静应力为变量的多项式作为修正因子后,R^(2)的平均值为0.934。预测精度有所提高,右侧的预测偏差减小,但仍存在。在区分高度的情况下,以带有修正因子的公式进行预测时,R^(2)的平均值为0.984,曲线向右侧预测偏差逐渐增大的现象明显改善。结论区分高度情况下,利用带修正因子的预测公式可以快速且较准确地预测最大加速度-静应力曲线,可以为冲击防护设计及相关软件的开发提供一定的帮助。

基于自适应AR模型巡航飞行参数预测研究

为更准确实现飞行参数趋势预测,提出一种基于自适应自回归(AR)模型的稳定巡航飞行参数预测方法。根据稳定巡航参数筛选条件,获取建模所需飞行参数。利用卡尔曼滤波原理估计AR模型参数,并与飞行参数构建系统方程,利用无迹卡尔曼滤波实时更新、修正AR模型参数估计值,将自适应AR模型的预测值与曲线拟合模型和灰色模型的预测值进行对比。以波音B777-300ER飞机的快速存取记录器数据样本进行仿真验证,结果表明:自适应AR模型在数据预测和收敛速率方面均更优,可有效降低预报模型随步数增加导致的精度误差,提高参数预测准确性。研究在飞机维修保障、状态监控与预测等方面具有重要作用。

基于Delft3D的珠江前航道尸体模型漂移轨迹预测

为预测珠江前航道中尸体的漂移轨迹,帮助当地水警确定溺亡者尸体的具体位置,利用Delft3D构建珠江前航道水动力模型,并使用尸体模型进行了多次现场漂移试验。通过对珠江前航道表层流速进行拟合,建立了尸体模型漂移的预测模型,R^(2)为0.88。研究结果表明,珠江前航道释放的尸体模型受潮汐和径流作用沿河道做往复运动,并呈逐渐漂向下游的趋势;漂移模型验证中尸体模型的漂移速度和方向基本与潮流一致,模拟结果的误差在1 km以内,终点距离误差率小于15%;实例验证中成年女性尸体的模拟结果误差约为300 m;游船所引起的波浪力等外力导致漂移轨迹呈现出南北方向性的偏差,需要进一步提升模拟效果。该漂移模型的推导模式同样适用于其他感潮河段,可使尸体的漂移轨迹变得可测,为尸体打捞工作和警方的案件处理提供了便利和参考。

基于异构集成模型的连续刚构桥预拱度预测

预拱度在大跨度悬臂桥梁的施工线形监控中扮演着重要角色,提高预测精度能够确保施工阶段和成桥状态的线形尽可能符合设计要求。为获得更好的预测性能,本文提出了一种基于自适应集合加权的SSA-BPNN-RF(sparrow search algorithm-back propagation neural network-random forest)异构集成模型。该模型利用不同算法之间的协作来提高预测性能,为了验证该模型的可行性,将训练好的模型应用于湖南某连续刚构桥预拱度预测,并与BPNN、RF、BPNN-RF、SSA-BPNN和SSA-RF 5种预测模型进行对比。研究结果表明:SSA-BPNN-RF异构集成模型在平均绝对误差、均方根误差和拟合度等评价指标上表现最佳。此外,BPNN-RF集成和SSA分别对BPNN和RF都有积极的影响,进一步验证了异构集成的有效性。因此,SSA-BPNN-RF异构集成模型具有高精度和更好的适应性,在工程实践中具有重要的指导意义。

基于机器学习的数字土壤制图研究进展

通过数字土壤制图可以高效、精准地获取土壤信息。近年来,随着计算机学科的快速发展和土壤-景观模型被广泛认同,采用机器学习方法进行数字土壤建模已成为数字土壤制图的主流思路,这为土壤空间分布的定量解释提供了一种不同于地统计学、专家知识和个体代表性等传统制图技术的新模式。本文综述了国内外数字土壤制图领域的研究成果,从利用机器学习技术进行土壤制图的基本理论、制图方法、展望三个方面完整系统地阐述了数字土壤制图领域的主要进展,其中数字土壤制图方法包括特征信息的选择、制图模型的选择和土壤图的精度验证。研究结果为全面、实时和精确获取土壤信息空间分布提供参考。