Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight

In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.

Separate-Type Estimators for Estimating Population Ratio in Post-Stratified Sampling Using Variable Transformation

The study proposes, along the line of [1], six separate-type estimators for estimating the population ratio of two variables in post-stratified sampling, using variable transformation. Properties of the proposed estimators were obtained up to first order approximations, both for achieved sample configurations (conditional argument) and over repeated samples of fixed size n (unconditional argument). Efficiency conditions, under which the proposed separate-type estimators would perform better than the associated customary separate-type estimators in terms of having smaller mean squared errors, were obtained. Furthermore, conditions under which some of the proposed separate-type estimators would perform better than other proposed separate-type estimators were also obtained. The optimum estimators among the proposed separate-type estimators were obtained and an empirical illustration confirmed the theoretical results.

A Class of Estimators for Population Ratio in Simple Random Sampling Using Variable Transformation

This paper is an extension and generalization of the study carried out by [1] on the estimation of the population ratio (R) of the population means of two variables (y and x) under Simple Random Sampling (SRS) scheme, using a variable transformation of the auxiliary variable, x. All the six estimators proposed by [1] are easily identified as special cases of the proposed class of estimators. Asymptotic properties of the proposed class of estimators are derived theoretically and subsequently verified using empirical illustrations. Some of the proposed estimators are found to have relatively large gains in efficiency over the customary ratio estimator, ?for the given data set.

Mixture Ratio Estimators Using Multi-Auxiliary Variables and Attributes for Two-Phase Sampling

In this paper, we have proposed three classes of mixture ratio estimators for estimating population mean by using information on auxiliary variables and attributes simultaneously in two-phase sampling under full, partial and no information cases and analyzed the properties of the estimators. A simulated study was carried out to compare the performance of the proposed estimators with the existing estimators of finite population mean. It has been found that the mixture ratio estimator in full information case using multiple auxiliary variables and attributes is more efficient than mean per unit, ratio estimator using one auxiliary variable and one attribute, ratio estimator using multiple auxiliary variable and multiple auxiliary attributes and mixture ratio estimators in both partial and no information case in two-phase sampling. A mixture ratio estimator in partial information case is more efficient than mixture ratio estimators in no information case.

Speech Separation Methodology for Hearing Aid

In the design of hearing aids(HA),the real-time speech-enhancement is done.The digital hearing aids should provide high signal-to-noise ratio,gain improvement and should eliminate feedback.In generic hearing aids the perfor-mance towards different frequencies varies and non uniform.Existing noise can-cellation and speech separation methods drops the voice magnitude under the noise environment.The performance of the HA for frequency response is non uni-form.Existing noise suppression methods reduce the required signal strength also.So,the performance of uniform sub band analysis is poor when hearing aid is con-cern.In this paper,a speech separation method using Non-negative Matrix Fac-torization(NMF)algorithm is proposed for wavelet decomposition.The Proposed non-uniformfilter-bank was validated by parameters like band power,Signal-to-noise ratio(SNR),Mean Square Error(MSE),Signal to Noise and Dis-tortion Ratio(SINAD),Spurious-free dynamic range(SFDR),error and time.The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union-Telecommunication standard ITU-T P.862.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

Generalized Ratio-Cum-Product Estimators for Two-Phase Sampling Using Multi-Auxiliary Variables

In this paper, we have proposed estimators of finite population mean using generalized Ratio- cum-product estimator for two-Phase sampling using multi-auxiliary variables under full, partial and no information cases and investigated their finite sample properties. An empirical study is given to compare the performance of the proposed estimators with the existing estimators that utilize auxiliary variable(s) for finite population mean. It has been found that the generalized Ra-tio-cum-product estimator in full information case using multiple auxiliary variables is more efficient than mean per unit, ratio and product estimator using one auxiliary variable, ratio and product estimator using multiple auxiliary variable and ratio-cum-product estimators in both partial and no information case in two phase sampling. A generalized Ratio-cum-product estimator in partial information case is more efficient than Generalized Ratio-cum-product estimator in No information case.

基于盲源分离的工业谐波源负荷分类识别方法

针对传统谐波源辨识方法无法实现工业用户谐波源具体类型的非侵入式识别的问题,该文提出一种基于盲源分离的工业谐波源负荷分类识别方法。该方法仅依据工业用户进线处电压、电流数据即可实现谐波源负荷具体类型的非侵入式识别。首先,从负荷等值阻抗模型入手,建立工业用户多负荷等值阻抗并联电路模型;其次,采用集合经验模态分解与奇异值分解相结合的方法确定构成用户进线处监测点综合等值阻抗信号的源阻抗信号数目;然后,采用快速独立分量分析实现将谐波源负荷等值阻抗信号从综合负荷等值阻抗信号中分离;最后,将分离出谐波源负荷等值阻抗信号频率特征与典型谐波源负荷进行匹配,进而实现分类识别。仿真与实测实验结果均表明,所提方法能够准确识别工业用户所含多种谐波源负荷的具体类型,具有较好的可行性和实用性。

高气液比井下气液旋流分离器结构设计与性能分析

针对井下高气液比工况气液高效分离难度大的问题,提出了一种旋流-重力耦合式井下气液分离器结构。结合实验研究、数值模拟及实验设计方法,对气液分离器结构参数进行显著性分析和优化设计,建立显著性结构参数与气液分离效率间的数学关系模型,确立了结构参数的最佳匹配方案。分析了入口流量、压力及气液比对气液分离器性能的影响规律。结果表明,重力沉降与旋流分离的结合设计可以实现高气液比条件下的液相沉积,进而实现气液两相的高效分离。分离器的分离效率随着进液量升高而升高,增幅逐渐趋于平稳。随着气液比增大,分离效率呈现先增加后降低的趋势。得出该气液分离器的最佳进液量为42m^(3)/d,最佳入口压力2MPa,最佳气液比为700∶1,在最佳工况下分离效率为97%,模拟结果与实验结果呈现出较好的一致性。研究结果将为高气液比条件下气液分离装备研发提供新的思路和参考。

榆树林油田CCUS采油工程方案优化设计与实践

大庆榆树林油田扶杨油层属低孔、特低渗透储层,针对以往开发过程中存在注水困难、压裂有效期短、水驱开发难以有效动用的问题,开展CO_(2)驱油技术现场试验。从经济性、技术适应性、配套工艺成熟性等角度对采油工程方案进行综合评价,优选出单、双管分层注入工艺、高气液比举升工艺、注采两端个性化防腐工艺及物理化学组合解冻堵工艺等CCUS采油工程技术。结果表明:试验区投产初期及目前生产情况均达到了油藏预测指标;采出井平均泵效及检泵周期与外围油田平均水平相持平,注气井与采出井腐蚀速率小于行业指标;实现CO_(2)有效埋存108.9×10^(4) t,比水驱预测采出程度提高采收率4.39百分点,取得较好的驱油开发效果。研究成果为CCUS示范区的高效建设提供了技术支撑,开辟了大庆油田外围难采储量有效动用的新途径。

基于二阶统计特性的方向向量估计算法的DOA估计

为了减小天线阵流形误差对波达方向(DOA)估计结果的影响,以及克服基于传统盲源分离算法的DOA估计算法不能应用于少通道测向设备的不足,提出一种基于2阶统计特性的方向向量估计算法的DOA估计算法。首先,根据确定性最大似然(DML)估计算法谱函数的特征,构造关于协方差矩阵的酉约束下的优化问题;然后,通过优化该问题获得各个单信号的实际方向向量;最后,将各个单信号的实际方向向量输入到空间谱算法中实现DOA估计。由于将多信号的DOA估计转化为多个单信号的DOA估计,因此在天线阵列流形存在误差时,所提算法比传统的DOA方法具有更好的DOA估计性能。由于所提算法仅需使用协方差矩阵,因此所提算法可应用于少通道测向设备。由仿真实验结果可知,在阵列流形存在误差以及测向设备为少通道测向设备时,与传统DOA方法相比,所提算法的DOA估计的准确度、抗扰度以及分辨率更高。

新型螺旋式气液旋流分离器数值模拟研究

为研究气液旋流分离器的气液分离性能,解决油气产出液中潜在高含气导致泵气锁的问题。基于旋流分离理论,设计了一种新型螺旋气液分离器。在不同气液比条件下对其内部流场进行研究。研究结果表明:随着分流比的增加,旋流器的内壁面和底流口附近的含气体积分数显著降低,同时旋流器溢流压力损失逐渐增大,而底流压力损失逐渐减小,溢流压力损失增加的速率与底流压力损失减小的速率基本相同;分流比的增加使气液分离器的除气效率逐渐提高,底流含气体积分数逐渐减小。综合分析气液比分别为1∶1、1∶2、1∶5以及1∶8的4种工况,得出了其最佳溢流分流比分别为58%、39%、22%以及15%。研究结果可为高含气油井的井下气液分离装置设计提供依据,同时可为现场应用及操作提供指导。

基于波噪比的高速铁路钢轨波磨快速检测方法

为实现高速铁路钢轨波磨里程覆盖式、高频次、快速测量,提出基于波噪比的钢轨波磨快速检测方法。采用便携式添乘仪检测高速列车车体振动和车内噪声数据,提出基于车体纵向加速度进行数值积分来计算列车速度和里程,采用曲线地段车体摇头角速度里程与台账里程的偏差值修正速度积分误差。利用提取的里程修正后车厢噪声数据与钢轨波磨对应的400~700 Hz频带成分,计算频带能量占噪声总能量的比值,并获取波噪比超限时的钢轨波磨波长和里程。结合高速列车实测数据分析,研究结果表明:速度修正后列车定位里程最大误差为87 m,对波磨比大于0.3的线路区段进行钢轨波磨波形测量和轴箱加速度振动能量比分析,钢轨波磨波长范围为53~57 mm,实测波长为53 mm,验证了该方法的正确性,为高速铁路钢轨波磨的快速测量提供技术支撑。

动态变化混叠模型下盲源分离中的源数估计

在进行欠定盲分离时,特别是对于源信号数目及混合矩阵动态变化的情况,常规的欠定盲分离及源数估计方法不能对源信号数目的变化时刻做出判断,因此很难实现动态变化的源信号数目实时和准确的估计。针对这个问题,提出了一种动态变化混叠模型下欠定盲源分离中的源数估计方法。首先,建立动态变化混叠情形下盲源分离的数学模型及动态标识矩阵。其次,基于构建的动态标识矩阵统计和判断动态源信号数目的变化情况。最后,通过分段时间内多维观测矢量采样点聚类区间局部峰值统计,实现动态变化混叠模型下盲源分离中的源信号数目的有效估计。仿真结果表明,该方法能有效实现动态变化混叠模型下欠定盲源分离中的源数估计,并且信号估计效果良好。

联合比估计法在敏感性问题调查中的应用

文章基于定性特征敏感性随机化模型——分层抽样下的Warner模型,运用联合比估计法对敏感性问题调查比例的估计量及估计误差进行了理论推导。通过效率比较得出:当各层样本量较大、比估计有效时,联合比估计法的精度优于分层估计法的精度。经调查实例验证,上述结论正确。

氮掺杂的碳基吸附剂高效分离甲烷/氮气

采用变压吸附(PSA)高效分离甲烷和氮气是实现甲烷富集和回收的重要手段之一,其核心是高性能吸附剂的制备。本研究以三聚氰胺(M)为氮源,采用水热法合成了五种不同三聚氰胺和间苯二酚(R)质量比的间苯二酚树脂基吸附材料MRF-AC_(n)。运用容量法测试303K,0~0.7MPa下甲烷和氮气的吸附性能,低温(77 K)氮吸脱附法对材料的孔结构进行表征,分别用Langmuir Freundlich(LF)模型和准二阶动力学(PSO)模型拟合甲烷和氮气的吸附等温线数据和动力学数据。结果表明,氮元素的引入调节了吸附材料的孔结构,不同程度增加了吸附材料的比表面积和微孔孔容,增加了吸附材料的极性,从而改变了甲烷和氮气的吸附量,其中样品MRF-AC_(2)表现出更好的甲烷和氮气吸附和分离性能,吸附量分别为45.20和22.23mL·g^(-1)分离比最高达到10以上。

溶析结晶用于盐湖卤水提钾和镁锂分离研究

采用溶析结晶法快速分离盐湖卤水中氯化钾并降低卤水中的镁锂比,解决青海盐湖盐田摊晒存在的生产周期长、需要修建大面积盐田等难题。以有机溶剂为溶析剂,考察了不同种类溶析剂加入量、溶析温度、溶析时间等对盐湖氯化钾收率及镁锂比的影响。实验结果表明,以无水乙醇为溶析剂与盐湖卤水体积比为2∶1,溶析温度为-15℃,时间为90 min时,氯化钾和氯化钠的收率分别达到93.15%和99.84%。溶析结晶后的母液经过过滤,在70~80℃,以0.5℃/min的降温速率进行动态降温,析出氯化镁晶体,镁锂质量比由原卤中的388.44降低至266.63,最终达到快速分离氯化钾的目的及为后续对镁锂进一步分离提供更有利的条件。

基于参数估计和Kalman滤波的单通道盲源分离算法

针对存在频谱混叠通信信号的单通道盲源分离(single channel blind source separation,SCBSS)问题,提出一种基于参数估计和Kalman滤波的SCBSS算法。首先,针对根多重信号分类(root multiple signal classification,Root-MUSIC)算法在相近载频估计方面的局限性,提出一种自适应的Root-MUSIC算法,对接收到的盲混合信号的源信号数目和载频进行估计;其次,将Kalman滤波的思想引入到SCBSS算法中,根据估计得到的源信号参数构造信号模型,将其作为Kalman滤波系统的观测向量,执行“时间更新”和“测量更新”两个过程,得到源信号的最佳估计,实现单通道盲源分离。仿真结果表明,所提算法能够有效地从存在频谱混叠的单路接收信号中准确地分离出多路源信号,比传统的算法分离精度高,运算速度快。

蛋白尿与估算肾小球滤过率联合对糖尿病患者新发心血管疾病的影响

目的 观察估算肾小球滤过率(estimated glomerular filtration rate, eGFR)和尿微量白蛋白与肌酐比值(urine albumin-to-creatinine ratio, uACR)评估肾功能的一致性,探讨二者联合对糖尿病患者新发心脑血管疾病(cardia-cerebrovascular disease, CVD)(包括心力衰竭、心肌梗死、脑卒中)的影响。方法 选择参加开滦第5次或第6次健康体检且进行尿微量白蛋白及尿肌酐、血肌酐检测的8 791例2型糖尿病(type 2 diabetes mellitus, T2DM)患者为研究对象。依据基线uACR和eGFR水平分组:正常组(uACR<3 mg/mmol且eGFR≥90 mL·min^(-1)·1.73 m^(-2))、单纯eGFR下降组(uACR<3 mg/mmol且eGFR<90 mL·min^(-1)·1.73 m^(-2))、单纯uACR升高组(uACR≥3 mg/mmol且eGFR≥90 mL·min^(-1)·1.73 m^(-2))和uACR升高合并eGFR下降组(uACR≥3 mg/mmol且eGFR<90 mL·min^(-1)·1.73 m^(-2))。观察eGFR和uACR评估的肾功能是否一致。采用多因素Cox回归模型分析uACR和eGFR联合分组对CVD发病风险的影响。结果 (1)研究对象基线年龄为(60.97±9.99)岁,男性占79.05%,中位uACR为1.68(0.81,4.60)mg/mmol,平均eGFR为(92.14±16.52) mL·min^(-1)·1.73 m^(-2),uACR升高与eGFR下降不一致者占43.59%。(2)中位随访时间为3.83年,共发生CVD事件694例(7.89%),4组总CVD事件的发病密度分别为12.96/1 000人年、19.04/1 000人年、25.65/1 000人年和46.87/1 000人年。(3)与正常组相比,单纯eGFR下降组、单纯uACR升高组和uACR升高合并eGFR下降组新发总CVD事件的风险分别升高1.06(95%CI 0.73~1.55)倍、1.99(95%CI 1.41~2.80)倍和3.00(95%CI 2.12~4.25)倍。结论 eGFR下降和uACR升高所评估的肾功能不一致现象较为常见,二者对CVD发病的影响存在联合作用。

海南石碌铁矿石氢基矿相转化——磁选试验研究

海南石碌铁矿石铁品位为52.45%,铁主要以赤(褐)铁矿形式存在,分布率达86.18%,少量以磁铁矿形式存在。为高效回收利用该矿石,采用氢基矿相转化(HMPT)—磁选工艺流程开展了系统的试验研究,并对适宜条件下的产品进行了化学成分、XRD和VSM分析。结果表明:试样在给矿粒度-0.074mm占89.41%、HMPT温度525℃、HMPT时间10min、总气体流量500m L/min、H_(2)浓度20%、HMPT产品磨矿细度-0.045mm占69.68%的适宜条件下,经磁选获得铁品位65.26%、铁回收率95.39%的铁精矿。试验所获铁精矿较原重磁联合工艺铁精矿品位提高了2.7个百分点,铁回收率增加了30个百分点。研究不仅为海南石碌铁矿氢基矿相转化—高效分选工业应用提供技术支撑,还为其他复杂难选铁矿资源的绿色高效分选提供了参考依据。