Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

基于粗糙集理论与PCA-APSO-SVM的沥青路面使用性能预测

针对传统沥青路面使用性能预测精度较低的问题,建立了基于粗糙集理论(rough set,RS)与主成分分析法(principal compoent analysis,PCA)-自适应粒子群算法(adaptive particle swarm optimization,APSO)-支持向量机(support vector machine,SVM)的沥青路面使用性能预测模型。基于沥青路面的时序指标与影响因素指标,建立了11个初始预测指标(包括前3年的路面使用性能、当量轴次、路龄、养护性质、坑槽率、修补率、年降水量、平均气温、日照时数);通过RS属性约减筛选出9个核心指标;利用PCA提取4个主成分,得到了基于4个主成分的数据集;将APSO引入到SVM中,对数据集进行训练,并优化了SVM模型参数;建立了路面使用性能的PCA-APSO-SVM预测模型,并以G6京藏高速甘肃境内某段道路为例,对路面使用性能进行预测。研究结果表明:PCA-APSO-SVM模型预测精度较PCA-PSO-SVM、APSO-SVM、PSO-SVM有较大提高,预测结果与实际情况更加符合,能为路面养护决策提供相关参考。

基于RS-PCA-SVM的建筑项目安全预测模型

为了减少建筑项目安全事故的发生,文章提出一种基于RS-PCA-SVM建筑项目安全组合预测模型,采用粗糙集理论(rough set,RS)对数据进行属性约简,剔除交叉和冗余信息,降低输入变量维数和计算复杂度,减少训练时间;利用主成分分析(principal component analysis,PCA)法进行降维处理,除去贡献率较低的主成分,将剩余主成分作为支持向量机(support vector machine,SVM)的输入变量,并选择自适应权重粒子群优化算法(particle swarm optimization,PSO)优化SVM的参数,避免参数选择的盲目性。结果表明:该模型的平均预测准确率为93.78%,相比传统方法预测精度高、计算速度快。

基于PCA-BP神经网络的巷道通风摩擦阻力系数预测模型

根据实测巷道通风摩擦阻力系数数据的特点,建立了主成分分析PCA-BP神经网络预测模型。采用PCA法对影响巷道通风摩擦阻力系数的支护类型、断面形状、巷道宽、巷道高、支护部分周边长、巷道断面积和巷道长度7个因素进行降维。将降维后因素的贡献率进行排序筛选,得到3个主成分指标(F_(1)、F_(2)和F_(3)),作为BP神经网络输入层的神经元。利用实测数据对PCA-BP神经网络模型进行训练和测试,并将测试结果与支持向量机回归(SVM)模型和BP神经网络模型的测试结果进行对比,结果显示:全因素的BP神经网络预测模型和SVM预测模型的平均精度分别为92.9420%、93.0235%,而PCA-BP预测模型的平均精度达到了96.4325%。PCA-BP神经网络模型不但简化了网络结构,更提高了网络的泛化能力,使预测误差更小、精度更高,为更准确地获得巷道通风摩擦阻力系数提供了一种有效的方法。

基于PCA-EWM两级特征融合和NGO-GRU的梁桥损伤诊断

为了提高损伤识别中单一指标对损伤的灵敏度和抗噪能力,基于模态应变能理论,提出联合主成分分析(principal component analysis,PCA)和熵权融合(entropy weight method,EWM)的两级特征融合方法,并使用北方苍鹰优化算法(northern goshawk optimization,NGO)结合门控循环单元(gated recurrent unit,GRU)进行桥梁损伤程度预测。首先,基于传统的模态应变能理论,构造出对角模态应变能比,由此衍生出对角模态应变能比变化率,对角模态应变能比耗散率,标准化对角模态应变能比差指标。其次,使用主成分分析实现指标内特征提取,熵权法融合指标间的特征,从而构造出加权决策指标(weighted decision index,WDI)。将单个模态应变能衍生指标输入到NGO-GRU混合神经网络中,损伤程度为输出,从而建立指标值与损伤程度之间的关系,进而实现损伤量化。通过三跨连续梁桥数值模型对所提出的方法进行验证,结果表明:加权决策指标具有良好的损伤定位能力和抗噪性,混合神经网络具有较高的损伤预测精度,预测准确率为91.14%。

基于PCA-PSO-ELM模型预测地震死亡人数研究

筛选42个历史地震震例,对地震震级、震源深度、震中烈度、抗震设防烈度、震中烈度与抗震设防烈度之差(ΔL)、人口密度以及发震时刻7个影响指标进行主成分分析(principal components analysis,PCA),构建粒子群优化(particle swarm optimization,PSO)极限学习机(extreme learning machine,ELM)地震死亡人数预测模型。将37个震例数据进行预处理和训练,并使用5个震例数据来检验模型的预测精度。实验结果表明,该PCA-PSO-ELM组合模型的平均误差率为10.87%,相比于PCA-ELM模型和ELM模型,其平均误差率分别降低8.70个百分点和18.38个百分点。因此,采用PCA-PSO-ELM组合模型预测地震死亡人数具有一定的可行性。

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

Study on Complete Analysis of LRE Test Samples Based on PCA

Incomplete data samples have a serious impact on the effectiveness of data mining.Aiming at the LRE historical test samples,based on correlation analysis of condition parameter,this paper introduced principle component analysis(PCA)and proposed a complete analysis method based on PCA for incomplete samples.At first,the covariance matrix of complete data set was calculated;Then,according to corresponding eigenvalues which were in descending,a principle matrix composed of eigen-vectors of covariance matrix was made;Finally,the vacant data was estimated based on the principle matrix and the known data.Compared with traditional method validated the method proposed in this paper has a better effect on complete test samples.An application example shows that the method suggested in this paper can update the value in use of historical test data.

基于PCA+KNN和kernal-PCA+KNN算法的废旧纺织物鉴别

该研究采集了15类废旧纺织物的4 998张近红外谱图,以7∶3的比例分为训练集和验证集,并分别采用主成分分析(PCA)与核主成分分析(kernal-PCA)两种不同降维方法对数据进行降维,并选用余弦相似度(cosine)核作为kernal-PCA的最佳核函数,最后分别将PCA和kernal-PCA降维处理后的数据进行k-近邻算法(KNN)训练。结果表明,kernal-PCA+KNN的模型准确率(95.17%)优于PCA+KNN模型的准确率(92.34%)。研究表明,kernal-PCA+KNN算法可以实现15类废旧纺织物识别准确率的提升,为废旧纺织物在线近红外自动分拣提供有力的技术支撑。

基于PCA-GWO-GRU的锂离子电池剩余使用寿命预测

为了提高GRU神经网络模型预测锂离子电池剩余使用寿命时的准确性,提出基于PCA-GWO优化的GRU模型,并应用于锂离子电池剩余寿命预测。结果表明,与传统GRU模型相比,经PCA-GWO算法优化的GRU模型具有更高的预测精度。预测起始点为原始数据90%时,预测精度达到最大,对应的均方根误差RMSE为0.0049、平均绝对误差MAE为0.0036、决定系数R^(2)为0.9863。

基于PCA-LDA-SVM算法的茶小绿叶蝉识别

为提高茶小绿叶蝉病虫害的识别效率和精度,提出一种基于PCA-LDA-SVM的茶小绿叶蝉病虫害识别方法。首先,对采集的茶叶图像进行预处理,得到缩放后的图像;然后,利用主成分分析(PCA)对预处理后的图像提取全局特征,降低特征数据的维度,从而减少后续的计算时间;再利用线性判别分析(LDA)寻找特征数据的最优投影空间,使类内散布距离最小,类间散布距离最大,进一步提高识别的准确率和精确度;最后,利用支持向量机(SVM)分类器进行分类识别。试验结果表明,PCA-LDA-SVM模型识别准确率达96%,精确度达100%,召回率达92%,整体识别性能优于SVM,BP,KNN,PCA-SVM模型,具备一定的理论价值和参考意义。

基于PCA和EEMD的柔性直流配电网故障选线算法

柔性直流故障选线技术的发展对直流配电网有着至关重要的作用。本文针对现有柔性直流故障存在的可利用的故障信息较少等问题,提出了一种新算法,该算法有效利用了集合经验模态分解EEMD(ensemble empirical mode decomposition)算法、主成分分析PCA(principal component analysis)和相关系数各自的优势。首先,提取暂态电流样本信号,采用EEMD得到以正交基函数表示的数据矩阵;接着,基于PCA进行该矩阵元素特征向量到主成分的转换,将样本信号投影到主元空间实现坐标变换,从而得到对样本数据的聚类和识别结果;最后,基于相关系数进行故障线路判别。本文算法的EEMD揭露了原始历史数据的内在变化规律,PCA能够有效选择故障有效特征。大量实验表明,该新算法准确有效,与现有其他方法相比,在故障信息不明显、不同过渡电阻方面具有优势。

基于改进PCA-BP神经网络模型的海宁市需水预测

需水预测是地区水资源规划中的重要部分,对于实现水资源合理有序开发,保障社会经济的可持续发展有重要的指导意义。采用改进PCA-BP神经网络模型对影响需水量的9个影响因子进行降维处理,并分别以海宁市2001~2014、2015~2020年数据作为训练样本和检验样本完成模型训练,其中,综合灰色预测模型GM(1,1)对降维后的影响因子独立预测,从而预测海宁市规划年需水量,并与传统定额法的需水预测结果进行对比分析。结果表明,人口、GDP、居民生活用水量、城镇公共用水量为影响海宁市需水量的主要因子;通过构建改进PCA-BP神经网络模型得到的2025、2030、2035年需水结果,比传统定额法更为真实、合理,进一步证实了预测模型的合理性,可为海宁市未来水资源规划提供指导。

VARIABILITY OF DAILY PRECIPITATION IN CHINA(1980-1993): PCA AND WAVELET ANALYSIS OF OBSERVATION AND ECMWF REANALYSIS DATA

In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its variability and evaluate the validation of reanalyzed precipitation. The results showed that northward movement of the summer rain belt was a wavelike propagation, which was always accompanied by rainfall breaks and could be treated as one event under time scale of about 1 month only. The first 4 EOFs accounted for 28% and 35% of total variance from observation and reanalysis, respectively, and were roughly consistent with each other. The first and third EOFs for observation mainly represented interweekly, interseasonal and interannual variations and contained some summer intraseasonal fluctuations also. The second and fourth ones mainly represented some rather strong summer intraseasonal fluctuations for a paticular year and contained interweekly, interseasonal and interannual variations also. Although there is still room for improvement, the ECMWF reanalysis is the best available dataset with global coverage and daily variability.

基于PCA-GA-RF的矿井突水水源快速识别模型

矿井突水已成为影响矿山安全生产的主要危害之一,快速准确识别突水水源类型是矿井突水灾害治理的关键步骤。提出了1种基于PCA-GA-RF的矿井突水水源识别模型;基于安徽省颍上县谢桥煤矿的88组水样实测数据,遵循分层随机抽样的原则,按照7∶3的比例将其分为62组训练样本和26组预测样本,经PCA提取4个主成分,构建PCA-GA-RF模型,并与PCA-RF、PCA-ABC-RF和PCA-FA-RF模型对比。结果表明:PCA-GA-RF模型判别结果准确率为96.153 8%,与其他模型相比准确率、精确率、召回率和F1值(精确召回率)最高,具有优越性。

基于PCA和ICA模式融合的非高斯特征检测识别

针对无人船(Unmanned surface vehicle,USV)航行位姿观测数据的非高斯性/高斯性判别问题,提出一种基于主成分分析(Principal component analysis,PCA)和独立成分分析(Independent component analysis,ICA)模式融合的非高斯特征检测识别方法.首先,采用基于标准化加权平均和信息熵的数据预处理方法.其次,引入混合加权核函数并使用灰狼优化(Grey wolf optimization,GWO)算法进行参数优化,以提高PCA方法的准确性.同时,该算法采用一种新的非线性控制因子策略,提高全局和局部搜索能力.最后,建立了一种基于ICA和PCA联合的相关性分析方法来实现多维数据的降维,在降维数据的基础上综合T型多维偏度峰度检验法和KS(Kolmogorov-Smirnov)检验法进行非高斯性/高斯性特征检测识别.该方法考虑了非线性非高斯的噪声对降维结果精确度的影响,有效降低了多维数据非高斯检测的复杂度,同时也为后续在实际USV位姿估计等应用中提供了保障.实验表明,该方法具有较高的准确性和稳定性,可为USV航行位姿观测数据处理提供支持.

PCA优化CEEMD的DSQ水管倾斜仪信号随机噪声压制方法

提出一种基于主成分分析(PCA)优化完备集合经验模态分解(CEEMD)的DSQ水管倾斜仪信号随机噪声压制方法CEEMD-PCA。该方法融合了相关系数、分布熵、MSE、R^(2)、SSE、RMSE、MAE、MAPE等8个IMF分量质量评价指标,借助PCA实施指标值矩阵的降维压缩,将其转化为一个能代表全部不同类型指标特点的新参数,并构建IMF分量质量综合评价函数,根据分数排名结果完成原始含噪信号的线性重构。仿真信号和实测信号去噪实验结果皆表明,CEEMD-PCA模型优于卡尔曼滤波、70阶低通FIR滤波等经典模型,能提高原始信号的信噪比,精准完成信号重构,更好地保留有效成分。

Equipment damage measurement method of wartime based on FCE-PCA-RF

As the“engine”of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation(FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85%of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.