Pressure stimulated current in progressive failure process of combined coal-rock under uniaxial compression:Response and mechanism

Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.

Experimental and numerical simulation of loading rate effects on failure and strain energy characteristics of coal-rock composite samples

The deformation and failure of coal and rock is energy-driving results according to thermodynamics.It is important to study the strain energy characteristics of coal-rock composite samples to better understand the deformation and failure mechanism of of coal-rock composite structures.In this research,laboratory tests and numerical simulation of uniaxial compressions of coal-rock composite samples were carried out with five different loading rates.The test results show that strength,deformation,acoustic emission(AE)and energy evolution of coal-rock composite sample all have obvious loading rate effects.The uniaxial compressive strength and elastic modulus increase with the increase of loading rate.And with the increase of loading rate,the AE energy at the peak strength of coal-rock composites increases first,then decreases,and then increases.With the increase of loading rate,the AE cumulative count first decreases and then increases.And the total absorption energy and dissipation energy of coal-rock composite samples show non-linear increasing trends,while release elastic strain energy increases first and then decreases.The laboratory experiments conducted on coal-rock composite samples were simulated numerically using the particle flow code(PFC).With careful selection of suitable material constitutive models for coal and rock,and accurate estimation and calibration of mechanical parameters of coal-rock composite sample,it was possible to obtain a good agreement between the laboratory experimental and numerical results.This research can provide references for understanding failure of underground coalrock composite structure by using energy related measuring methods.

Combination of Loop-Mediated Isothermal Amplification Assay and Nested PCR for Detection of Borrelia burgdorferi sensu lato in Human Serum Samples

A set of universal loop-mediated isothermal amplification （LAMP） primers targeting the flo gene was designed to detect Borrelia burgdorferi sensu lato （B. burgdorferi s.I.） in human samples. The sensitivity of LAMP was 20 copies/reaction, and the assay did not detect false positives among 11 other related bacteria. A positive LAMP result was obtained for 9 of the 24 confirmed cases and for 12 of 94 suspected cases. The positive rate of LAMP was the same as that of nested PCR. The LAMP is a useful diagnostic method that can be developed for rapid detection of B. burgdorferi s.I. in human sera. Combination of the LAMP and nested PCR was more sensitive for detecting B. burgdorferi s.I. in human serum samples.

Combined model of trip mode and destination

This paper analyzes the characteristics of the destination distribution of trips and proposes a stratified sampling strategy for travel mode choice.The stratified sampling strategy can reduce the size of the alternative set;thus,the computation burden of simulation is decreased.Using the stratified sampling strategy,a combined choice model of the trip mode and destination is developed based on the Bayesian theory.Simulations are carried out to verify the proposed model.The results show that the combined choice model of the trip mode and destination can efficiently simulate travelers＇ choice behaviors.Furthermore,the forecasting accuracy of the combined choice model is higher than the one of the gravity model.Therefore,the proposed model is a powerful tool with which to analyze travelers＇ behaviors in selecting the trip mode.

Reasonable location parameters of pick and nozzle in combined cutting system

The drum shearer and high pressure water jet combined cutting system is an effective technology to cut hard coal-rock, but one problem of the technology is the choice of pick and nozzle location parameters. In order to solve the problem, the process and mechanism of combined cutting arc studied and mining seepage catastrophe theory is used to construct the mathematic and physical model of combined cutting hard coal-rock. Based on the model and detailed analysis of combined cutting mechanism, the single pick and nozzle combined cutting test-bed is built to test the main pick and nozzle location parameters of drum shearer and water jet combined cutting system. Test results show that the best vertical distance between the pick-tip and nozzle center point is the sum of cutting thickness and proper target distance in the Y axial direction; the best horizontal distance is the length between pick-tip point and coal-rock surface bursting crack point in the X axial direction. The best incident angle of water jet should be the same as the bursting crack line＇s angle in theory, but considering other important factors comprehensively, it is more reasonable when the incident angle of water jet is 90°.

Application of the Combined Method for Evaluating the Compressive Strength of Concrete on Site

Ultrasonic pulse velocity (UPV) and rebound hammer (RH) tests are often used for assessing the quality of concrete and estimation of its compressive strength. Several parameters influence this property of concrete as the type and size of aggregates, cement content, the implementation of concrete, etc. To account for these factors, both of the two tests are combined and their measurements are calibrated with the results of mechanical tests on cylindrical specimens cast on site and on cores taken from the existing structure in work progress at the new-city Massinissa El-Khroub Constantine in Algeria. In this study;the two tests cited above have been used to determine the concrete quality by applying regression analysis models between compressive strength of in situ concrete on existing structure and the nondestructive tests values, the combined method is used, equations are derived using statistical analysis (simple and multiple regression) to estimate compressive strength of concrete on site and the reliability of the technique for prediction of the strength is discussed for this case study.

基于特征波段选择的芦苇LAI高光谱遥感估测

选取典型芦苇湿地基于芦苇叶片实测高光谱数据和叶面积指数(Leaf Area Index,LAI),在原始光谱的基础上进行了平滑(R)、一阶微分(FD)、倒数(RT)、对数(LT)、倒数一阶微分(RTFD)、对数一阶微分(LTFD)等六种光谱变换,利用竞争性自适应重加权算法(CARS)对不同变换下芦苇LAI特征光谱波段予以筛选,进而用筛选的特征波段采用逐波段组合法(BCI)构建芦苇LAI敏感光谱指数,利用随机森林(RF)、极端梯度提升(XGBoost)以及支持向量机(SVM)回归算法,构建芦苇LAI的高光谱估算模型。结果表明,采用CARS算法筛选不同变换光谱的特征波段构建模型,发现经过FD变换(R^(2)=0.417,RMSE=0.905)的模型效果最优。在CARS基础上使用筛选过后的特征波段构建植被指数进行建模比较,模型效果最好的是XGBoost(R^(2)=0.620,RMSE=0.826)。

Effects of water intrusion and loading rate on mechanical properties of and crack propagation in coal–rock combinations

Tackling the problems of underground water storage in collieries in arid regions requires knowledge of the effect of water intrusion and loading rate on the mechanical properties of and crack development in coal–rock combinations. Fifty-four coal–rock combinations were prepared and split equally into groups containing different moisture contents(dry, natural moisture and saturated) to conduct acoustic emission testing under uniaxial compression with loading rates ranging from 0.1 mm/min to 0.6 mm/min. The results show that the peak stress and strength-softening modulus, elastic modulus, strain-softening modulus, and post-peak modulus partly decrease with increasing moisture content and loading rate. In contrast, peak strain increases with increasing moisture content and fluctuates with rising loading rate. More significantly, the relationship between stiffness and stress, combined with accumulated counts of acoustic emission, can be used to precisely predict all phases of crack propagation. This is helpful in studying the impact of moisture content and loading rate on crack propagation and accurately calculating mechanical properties. We also determined that the stress thresholds of crack closure, crack initiation, and crack damage do not vary with changes of moisture content and loading rate, constituting 15.22%, 32.20%, and 80.98% of peak stress, respectively. These outcomes assist in developing approaches to water storage in coal mines, determining the necessary width of waterproof coal–rock pillars, and methods of supporting water-enriched roadways, while also advances understanding the mechanical properties of coal–rock combinations and laws of crack propagation.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

考虑风-光不确定性的风-光-蓄-火联合调度研究

在当前风电和光伏大规模并网的背景下,要确保能源供应的稳定性和高效性,关键任务是确定合适的可再生能源渗透率,并精确配比可再生能源与储能系统的容量。因此,在考虑风、光不确定性的基础上,构建风-光-蓄-火优化调度模型,使用拉丁超立方抽样法模拟模型中风电和光伏出力场景,再基于Kantorovich距离的同步回代消除算法对生成的风、光出力场景进行缩减,并通过改进的粒子群算法对模型进行求解。以白莲河抽水蓄能电站为例,结合当地的实测风速、光照数据,探究有无抽蓄参与以及不同能源容量配比下的调度结果,得到以下结论:①抽蓄电站的加入使得联合系统收益增加了428.06万元,风、光出力的波动性降低了12.24%,可再生能源的弃电率下降了5.62%,污染物排放也得到了减少。②抽蓄与可再生能源装机容量比例在1∶8.33范围内配置较为合理,在1∶5.21时可再生能源弃电率达到最低。③当可再生能源渗透率为71.09%,抽蓄与可再生能源装机容量比例为1∶6.25时,风-光-蓄-火联合运行边际效益最大。

基于最优样本和最优属性组合的作业车间调度规则挖掘

作业车间调度问题可使用调度规则解决。为挖掘到高效、准确的调度规则,基于训练样本最优和属性组合最优的核心思想,提出一种基于最优样本与最优属性组合的决策树-遗传算法框架(NDTGA)。该框架在构造训练数据时采用成对比较的方式,在构造属性组合时使用属性原值、差值、对比值等多种组合;在遗传算法的每次寻优过程中,调用决策树挖掘全新的调度规则;最终得到最优训练样本和最优属性组合,进而得到最优的调度规则。通过与经典调度规则和其他机器学习算法的对比实验论证了NDTGA框架挖掘所得调度规则的优越性。

管制员个体工作负荷多维量化研究

为提高空管系统高效运行,聚焦管制员个体工作负荷建立量化模型;首先设计试验采集一线16名区域管制员的岗前与岗后各项指标数据,根据测试数据变化,选择出敏感变量,描述个体工作负荷;其次建立包含心理感知负荷、生理反应负荷与脑力工作负荷3个维度的综合评估指标体系,构建管制员个体工作负荷指数模型;然后通过熵权-客观组合法求解个体工作负荷指数最优权重,最终得出管制员个体工作负荷量化模型;最后进一步根据管制员个体工作负荷综合指数进行K-Means聚类分析,结果表明:管制员因个体不同岗后工作负荷存在差异。依据个体工作负荷指数大小,管制员可分为3类,A类管制员数量占总人数50%,岗后个体工作负荷增长最小;B类管制员数量占总人数43.75%,岗后负荷增长居中;C类管制员数量占总人数6.25%,岗后负荷增长最大,与教员对管制员能力的评分结果一致。

动静组合加载下裂隙试样力学特性和破坏模式

目的为了深入了解裂隙岩石在同时受到地应力和动力扰动时的工程灾变机制。方法采用改进的分离式霍普金森压杆系统,对预制裂隙类岩石试样进行了动静组合加载试验。研究了应变率、裂隙倾角和预应力对试样动态力学特性和能耗特征的影响,并对试样的破坏模式进行了分析。结果当应变率从61.82s^(-1)增加到195.57s^(-1),裂隙试样的动态增长因子不断增大,具有显著的应变率效应;动态弹性模量变化规律不明显,应变率效应不显著。随着入射能的增加,裂隙试样的单位耗散能密度不断增加。当裂隙倾角由0°变化到90°,试样的动态增长因子增不断增加,动态弹性模量和单位耗散能密度不断降低。当施加预应力后,30°、45°和60°裂隙试样的动态增长因子增长均有所增加;45°裂隙试样动态弹性模量有所增加,30°和60°裂隙试样的变化规律不明显;45°和60°裂隙试样单位耗散能密度有所降低,30°裂隙试样的变化规律不明显。裂隙试样的破坏模式主要包括张拉破坏、剪切破坏和张拉-剪切复合破坏3种,施加预应力对试样破坏模式的影响不明显。结论0°裂隙对工程岩体的承载能力削弱程度最小,90°裂隙有利于提高破岩效率。预应力在一定程度上会提高工程岩体的承载能力。研究结果可以为预测和评估岩石工程中的风险,制定合理的加固措施提供参考。

基于Style 3D针织运动装虚拟设计与应用

基于Style 3D虚拟服装试衣软件,通过对虚拟试衣技术现状的分析,以针织运动装作为虚拟试衣对象,从四个部分进行展开,包含人体建模,版片的三维排布与缝合,运动装面料仿真与试穿效果展示,以及驳样改款与模块组合。借助于三维面料扫描技术获取面料材质纹理和相关辅料配件,进一步丰富针织运动装的装饰效果。3D快速驳样改款和虚拟展示能有效地优化设计过程,针织运动装模块组合模拟应用适应快时尚节奏下改款搭配的需求,根据现有版型快速驳样改款亦是对传统设计与展示模式的一种突破。

环境因子组合和负样本选取策略对花岗岩区崩岗易发性评价的影响

不同环境因子组合和负样本选取策略对崩岗易发性评价结果存在较多不确定性。为探究其对评价结果的影响,该研究以江西省兴国县花岗岩区为例,利用地理探测器探测17个环境因子的统计量q值,根据累计q值百分比大小依次选择4、7、10和17个环境因子进行组合;利用单随机欠采样、频率比法及改进频率比法等负样本选取策略构建与正样本等量的负样本数据集;采用随机森林模型进行易发性评价,并对评价结果进行对比分析。结果表明:1)3种负样本选取策略下的模型精度随着因子数量的增加先下降再上升,考虑4个环境因子的模型AUC(area under curve)值分别为0.729、0.909和0.909,较最优环境因子组合仅相差0.020~0.038,说明考虑主控环境因子,即可得到较为理想的精度;2)通过频率比法选取的负样本数据集更具合理性;3)研究区内高和极高易发区主要分布在兴国县西南部,而极低易发区主要分布在兴国县北部及东部,这与实际情况较吻合。该研究通过探究不同环境因子组合和负样本选取策略对崩岗易发性评价的影响,可为花岗岩区崩岗的防灾减灾提供科学依据。

Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis

Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously.To estimate the Imprecise Time-variant Failure Probability Function(ITFPF)and derive the imprecise reliability results as a byproduct,Adaptive Combination Augmented Line Sampling(ACALS)is proposed.It consists of three integrated features:Augmented Line Sampling(ALS),adaptive strategy,and the optimal combination.ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t.imprecise parameters.Then,the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously.Finally,the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance(C.o.V.)of the ITFPF estimate.Overall,the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V.Thus,the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications.Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.

面向联合收割机故障领域的命名实体识别研究

联合收割机作为一种机械化设备不可避免地会出现机械故障,为快速地找出并解决机械故障,提出一种面向联合收割机故障领域的命名实体识别模型RP-TEBC(RoBERTa-wwm-ext+PGD+Transformer-Encoder+BiGRU+CRF)。RP-TEBC使用动态编码的RoBERTa-wwm-ext预训练模型作为词嵌入层,利用自适应Transformer编码器层融合双向门控单元(BiGRU)作为上下文编码器,利用条件随机场(CRF)作为解码层,使用维特比算法找出最优的路径输出。同时,RP-TEBC模型在词嵌入层中通过添加一些扰动,生成对抗样本,经过对模型不断的训练优化,可以提高模型整体的鲁棒性和泛化性能。结果表明,在构建的联合收割机故障领域命名实体识别数据集上,相比于基线模型,该模型的准确率、召回率、F1值分别提高1.79%、1.01%、1.46%。

基于EEMD-SE-LSTM 组合模型的开都河日径流模拟研究

为提高开都河日径流模拟的精度和更科学地进行开都河水资源的管理与规划,在集成经验模态分解(EEMD)的基础上进行样本熵(SE)重构来完成长短期记忆网络(LSTM)组合模型的构建。采用集成经验模态分解提取开都河日径流序列中具有物理含义的信息,得到一系列本征模态分量(IMF)及一个趋势项(Res),计算每个分量的样本熵,复杂程度接近的子序列叠加为新序列,建立长短期记忆神经网络模型进行预测,叠加得到最终模拟值。结果表明:EEMD-SE-LSTM组合模型日径流模拟的精度得到提高,其确定系数R2=0.81、纳什效率系数NSE=0.73,均高于LSTM模型的R2=0.73、NSE=0.52和EEMD-LSTM模型的R2=0.64、NSE=0.63;EEMD-SE-LSTM组合模型的日径流模拟准确性更高,其评价指标(R2=0.81、NSE=0.73)高于其他单一模型SVM(R2=0.70、NSE=0.58)。EEMD-SE-LSTM组合模型提高了日径流模拟精度,可以更好地为开都河水资源管理与规划提供科学依据。

双重抽样框下项目无回答插补估计方法研究

抽样调查工作中无回答情形不可避免,双重抽样框下亦如此,因此需要对双重抽样框下抽样调查项目无回答造成的估计量偏差进行纠偏校正。首先通过二重抽样获取辅助变量的信息,使用其构造比率估计量与比率型指数估计量的组合估计量对双重抽样框下抽样调查中项目无回答数据进行插补,得到对应各部分子总体的均值估计,再用Hartley估计量的形式对总体总值进行估计。通过计算估计量偏差、均方误差及最优权重系数,对比相同条件下完全回答时同类型组合估计量均方误差的相对精度损失与使用单一比率型指数估计量的相对精度损失,随机模拟结果显示损失率较低,插补方法有效。选择合适的辅助变量构造比率估计量和比率型指数估计量的组合估计量做插补值,更充分利用辅助变量和已回答研究变量信息,基于提出的组合估计量于抽样调查工作具有一定的应用价值。

基于不确定性估计的离线确定型Actor-Critic

Actor-Critic是一种强化学习方法,通过与环境在线试错交互收集样本来学习策略,是求解序贯感知决策问题的有效手段.但是,这种在线交互的主动学习范式在一些复杂真实环境中收集样本时会带来成本和安全问题离线强化学习作为一种基于数据驱动的强化学习范式,强调从静态样本数据集中学习策略,与环境无探索交互,为机器人、自动驾驶、健康护理等真实世界部署应用提供了可行的解决方案,是近年来的研究热点.目前,离线强化学习方法存在学习策略和行为策略之间的分布偏移挑战,针对这个挑战,通常采用策略约束或值函数正则化来限制访问数据集分布之外(Out-Of-Distribution,OOD)的动作,从而导致学习性能过于保守,阻碍了值函数网络的泛化和学习策略的性能提升.为此,本文利用不确定性估计和OOD采样来平衡值函数学习的泛化性和保守性,提出一种基于不确定性估计的离线确定型Actor-Critic方法(Offline Deterministic Actor-Critic based on UncertaintyEstimation,ODACUE).首先,针对确定型策略,给出一种Q值函数的不确定性估计算子定义,理论证明了该算子学到的Q值函数是最优Q值函数的一种悲观估计.然后,将不确定性估计算子应用于确定型Actor-Critic框架中,通过对不确定性估计算子进行凸组合构造Critic学习的目标函数.最后,D4RL基准数据集任务上的实验结果表明:相较于对比算法,ODACUE在11个不同质量等级数据集任务中的总体性能提升最低达9.56%,最高达64.92%.此外,参数分析和消融实验进一步验证了ODACUE的稳定性和泛化能力.