Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

基于Gridsearch-SVM梯形区域极点分类的故障诊断

针对一类线性定常系统,基于梯形区域极点配置,给出了执行器部件故障诊断的一种方法。首先,利用极点观测器,通过测量系统的状态,得到极点的动态信息;其次,根据模拟各通道执行器故障,实时采集闭环系统的极点信息,形成极点分类数据库;最后,利用支持向量机算法(Support Vector Machine,SVM)根据不同通道发生故障时极点所处位置不同,设计极点分类器,对极点进行分类,实现对系统的故障诊断。针对SVM中惩罚因子和核宽度系数需要依靠先验知识的缺陷,采用Grid search优化其参数,缩小寻优范围。仿真结果表明设计方案的可行性以及故障诊断的有效性。

基于Grid-Search_PSO优化SVM回归预测矿井涌水量

为了解决矿井涌水量预测难题,在Grid-Search_PSO优化SVM参数的基础上,采用SVM非线性回归预测法,对大海则煤矿1999~2008年7月份的矿井涌水量进行了预测。分析对比SVM回归预测法和ARIMA时间序列预测法预测结果的数据误差,发现SVM回归法预测值与实测值之间的偏差比ARIMA时间序列法要小很多。可见在影响矿井涌水量各种因素值具备的情况下,SVM非线性回归预测所建立的模型能够更准确地预测矿井的涌水量,在矿井安全生产中具有很大的应用价值。

Nearest neighbor search algorithm based on multiple background grids for fluid simulation

The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.

Grid-Search和PSO优化的SVM在Shibor回归预测中的应用研究

作为一种动态和非稳定时间序列,Shibor发展变化是随机波动的,难以准确预测Shibor的波动性.支持向量机(SVM)在回归预测非线性时间序列方面有很好地预测效果,SVM的预测精度和泛化能力的核心是参数的优化选择,分别用网格搜索法(Grid-Search)和粒子群(PSO)算法来优化SVM的参数c和g.从而将参数优化后的SVM非线性回归预测法与基于传统ARIMA时间序列预测结果进行对比分析.实验表明,优化后的SVM回归预测方法比ARIMA时间序列方法更精确,在实际中具有很大的应用价值.

METADATA EXPANDED SEMANTICALLY BASED RESOURCE SEARCH IN EDUCATION GRID

With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Improved Interleaved Single-Ended Primary Inductor-Converter forSingle-Phase Grid-Connected System

The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial uses.The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power.An improved interleaved Single-ended Primary Inductor-Converter(SEPIC)converter is employed in proposed work to extricate most of power from renewable source.This proposed converter minimizes ripples,reduces electromagnetic interference due tofilter elements and the contin-uous input current improves the power output of PV panel.A Crow Search Algo-rithm(CSA)based Proportional Integral(PI)controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI controller.The optimized PI controller reduces ripples present in Direct Current(DC)vol-tage,maintains constant voltage at proposed converter output and reduces over-shoots with minimum settling and rise time.This voltage is given to single phase grid via 1�Voltage Source Inverter(VSI).The command pulses of 1�VSI are produced by simple PI controller.The response of the proposed converter is thus improved with less input current.After implementing CSA based PI the efficiency of proposed converter obtained is 96%and the Total Harmonic Distor-tion(THD)is found to be 2:4%.The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.

Managing of Smart Micro-Grid Connected Scheme Using Group Search Optimization

This article introduces a group search optimization (GSO) based tuning model for modelling and managing Smart Micro-Grids connected system. In existing systems, typically tuned PID controllers are engaged to point out the load frequency control (LFC) problems through different tuning techniques. Though, inappropriately tuned PID controller may reveal pitiable dynamical reply and also incorrect option of integral gain may even undermine the complete system. This research is used to explain about an optimized energy management system through Group Search Optimization (GSO) for building incorporation in smart micro-grids (MGs) with zero grid-impact. The essential for this technique is to develop the MG effectiveness, when the complete PI controller requires to be tuned. Consequently, we proposed that the proposed GSO based algorithm with appropriate explanation or member representation, derivation of fitness function, producer process, scrounger process, and ranger process. An entire and adaptable design of MATLAB/SIMULINK also proposed. The related solutions and practical test verifications are given. This paper verified that the proposed method was effective in Micro-Grid (MG) applications. The comparison results demonstrate the advantage of the proposed technique and confirm its potential to solve the problem.

基于自动终止准则改进的kd-tree粒子近邻搜索研究

对于大规模运动模拟问题而言,近邻点的搜索效率将对整体的运算效率产生显著影响。本文基于关联性分析建立kd-tree的最大深度dmax与粒子总数N的自适应关系式,提出了kd-tree自动终止准则,即ATC-kd-tree,同时还考虑了叶子节点大小阈值n_(0)对近邻搜索效率的影响。试验表明,ATC-kd-tree具有更高的近邻搜索效率,相较于不使用自动终止准则的kd-tree搜索效率最高提升46%,且适用性更强,可求解不同N值的近邻搜索问题,解决了粒子总数N发生改变时需要再次率定最大深度dmax的问题。同时,本文还提出了网格搜索法组合坐标下降法的两步参数优化算法GSCD法。通过2维阿米巴虫形状的参数优化试验发现,GSCD法可更为快速地率定ATC-kd-tree的可变参数,其优化效率比网格搜索法最高提升了205%,相较于改进网格搜索法最高提升了90%。研究结果表明,ATC-kd-tree和GSCD法不仅提高了近邻搜索的效率,也为复杂运动中近邻粒子搜索问题提供了一种更为高效的解决方案,能够显著降低计算资源的消耗,进一步提升模拟的精度和效率。

基于机器学习算法的糖尿病预测

糖尿病是一种无法根治的慢性疾病,早发现、早干预、早治疗能够延缓病情进展,提高患者的治疗效率。构建基于决策树、逻辑回归、XGBoost等六种机器学习分类算法的预测模型,实现糖尿病风险预测。该模型以皮马印第安人糖尿病数据集为研究对象,通过数据预处理、数据特征分析构建有效数据集,采用网格搜索方法进行交叉验证寻找算法的最佳参数组合,构建超参数及基于超参数的分类模型,并对模型的预测性能进行评价。实验结果表明,该模型拥有良好的糖尿病风险预测性能。

面向人员岸滩行进的三维路径规划算法研究

针对当前无法为人员岸滩行进提供科学合理的路径规划这一问题,论文基于蚁群算法提出了面向岸滩行进的最优路径规划算法。首先对基本的蚁群算法进行了改良,包括路径搜索方式、信息素更新策略和启发函数的合理设计等,改善了算法的收敛效率;然后定量结合多类岸滩场路径规划影响因子,构建了满足岸滩行进的代价函数;最终实现了面向岸滩行进的算法构建。该算法可为实现复杂地形条件下岸滩行进的最优路径解算和基于蚁群算法的相关三维路径规划分析研究提供参考借鉴。

基于多源数据的特长隧道驾驶疲劳模型

为研究驾驶人在特长隧道内驾驶疲劳演变过程及其影响因素,基于实车试验采集的多源数据,对特长隧道内驾驶疲劳分类判别以及驾驶疲劳影响因素关系模型展开了研究。通过差异显著性分析和相关性分析筛选出闭眼百分率P80、瞳孔直径变异系数和加速度作为疲劳敏感性指标,并分析了各指标随行驶时间累积的变化规律。为构建驾驶疲劳分类判别模型,基于卡罗林斯卡嗜睡量表(Karolinska sleeping scale,KSS)主观疲劳检测结果,将疲劳程度划分清醒状态、半疲劳状态和疲劳状态,采用构造多类分类器的方法将不同疲劳状态样本进行组合分类,利用网格搜索法进行分类模型的参数寻优,并将筛选出的疲劳敏感性指标作为分类模型的输入变量,建立了基于网格搜索法的多分类支持向量机疲劳状态判别模型(GS-M-SVMs模型)。然后根据疲劳状态分类判别模型,利用有序多分类Logistic模型建立了特长隧道疲劳程度与影响因素的关系模型,对特长隧道内驾驶疲劳影响因素进行了探究。研究结果表明:疲劳敏感性指标变化规律可有效表征特长隧道内驾驶疲劳演变过程,而GS-M-SVMs模型分类检测准确率达到90.75%,对疲劳程度的分类识别效果较好,并且累积行驶时间和隧道长度显著影响驾驶人的疲劳程度,其模型回归系数分别为2.634和0.395,表明累积行驶时间是驾驶人在特长隧道路段中疲劳程度加重的最主要因素,隧道照度和隧道线形等因素并无显著影响。

基于GS-SVR的架空输电线路工程投资估算预测研究

传统的投资估算编制模式存在过度依赖定额的现象,随着大量工程造价数据的积累,利用其实现投资估算,以弥补传统定额计价模式的不足,能够对建设项目工程造价起到总体控制作用。文章以架空输电线路工程为例,基于支持向量回归机(Support Vector Regression,SVR)研究架空输电线路工程投资估算问题。结果表明:通过选取影响架空输电线路工程投资估算的主要指标,构建基于SVR的架空输电线路工程投资估算模型,并利用改进的网格搜索法(Grid Search,GS)优化模型参数,得到基于GS-SVR的投资估算预测模型;与传统的线性回归和SVR模型相比,GS-SVR模型表现出更为良好的性能。

浅析震源位置准确度及其影响因素

地震定位是地震学研究的基础,然而地震定位和地震学研究之间存在“供给”矛盾.不同研究对地震位置准确度级别的要求不尽相同,震源机制和壳幔结构研究要求震源位置的准确度为千米级别,工业生产活动和诱发地震研究要求震源位置的准确度为百米级别.然而,地震监测台网给出的地震位置准确度仅为数千米.诸多地震定位方法从不同方面对地震定位过程进行优化和改进,但它们的侧重点不尽相同.总体而言,已有的定位方法对地震位置的准确度关注程度尚显不足.在大量的地震定位实践中,前人获得了用于优化地震位置准确度的若干经验法则,这些经验法则不但存在地区差异,而且还有一定的适用条件,经验法则仍需要被进一步地优化和修正.本文简要分析地震定位准确度的多方面影响因素,有针对性地开展研究,在地震定位算法和控制观测数据质量方面获得一定的研究进展;在地震定位耦合关系方面补充了定位速度模型、发震位置和发震时刻三者之间的制约关系;在地震定位流程方面提出了使用逐步消元定位的建议.

Grid A^(*):面向野外空地协同应急处置的快速路径规划

在野外应急救援活动中,灾害现场或事故区域通常缺乏地面交通工具可直达的现成道路,但该区域地表环境仍可满足部分越野车辆的通行。在空地协同系统中,无人机可提供行进路径周边环境的影像,地面终端可快速提取影像中地表类型以及地形起伏等特征信息,通过分析计算便可为车辆提供通往救援目标点的导航路径。本文针对这一应用需求,对现有A^(*)算法进行了改进,主要有3个方面的创新:其一,针对户外地表环境的应用特点,提出一种综合地表类型与地表高程信息的通行性代价函数;其二,针对无人机影像分辨率与实际车辆通行路径之间的尺度关系,提出一种基于格网单元的路径快速搜索算法;其三,在顾及格网单元内部地表类型连通分布特点的基础上,选择格网边缘特征点用于通行性路径规划,在提高算法搜索效率的同时,兼顾了格网单元内部的地形信息,从而使算法在优化计算的同时,能充分利用到无人机影像的细节信息。实验表明,算法搜索得到的可通行路径具有较高的可靠性,从路径三维可视化结果来看,符合越野车辆通行的需要。此外,同等情况下,本算法的运行时间降至传统A^(*)算法的15%,提高了野外应急救援应用的时效性。

基于聚类和GBDT的镀锌钢卷力学性能预测

热镀锌钢卷力学性能影响因素之间关系复杂,限制了模型精度的提升。采用k-means算法利用化学成分属性对镀锌钢卷数据集进行聚类,将数据聚成三种模式簇实现样本的优选。利用梯度提升树算法,开展各模式数据集与不划分模式的全数据集下的力学性能建模研究,最后结合网格搜索与交叉验证方法进行模型参数优化。研究结果表明,分模式下模型MAE误差相比于全数据集建模平均减小0.85 MPa。参数优化后,各模式下MAE误差平均减少5.19 MPa,RMSE误差平均减少3.63 MPa,提高了预测模型精度。

基于Grid-GSA算法的植保无人机路径规划方法

为了提高植保无人机的作业效率,研究了一种路径规划方法。运用栅格法构建环境模型,根据实际的作业区域规模、形状等环境信息和无人机航向,为相应栅格赋予概率,无人机优先选择概率高的栅格行进。基于上述机制实现了在形状不规则的作业区域内进行往复回转式全覆盖路径规划;以每次植保作业距离为变量,根据仿真算法得出返航点数量与位置来确定寻优模型中的变量维数范围,以往返飞行、电池更换与药剂装填等非植保作业耗费时间最短为目标函数,通过采用引力搜索算法,实现对返航点数量与位置的寻优;为无人机设置必要的路径纠偏与光顺机制,使无人机能够按既定路线与速度飞行。对提出的路径规划方法进行了实例检验,结果显示,相比于简单规划与未规划的情况,运用Grid-GSA规划方法得出的结果中往返飞行距离总和分别减少了14%与68%,非植保作业时间分别减少了21%与36%,其它各项指标也均有不同程度的提高。在验证测试试验中,实际的往返距离总和减少了322 m,实际路径与规划路径存在较小偏差。验证了路径规划方法具有合理性、可行性以及一定的实用性。