Application Research of Gravel and Machine-Made Sand along the KKH-2 Project in Pakistan on Asphalt Pavement

According to the characteristics of stone along the KKH-2 project in Pakistan, the applicability of gravel and machine-made sand for road engineering was studied. Through investigation, the types of stone along the project were relatively simple, and the stone materials used for road construction were mainly limestone, sandstone and pebbles, and the reserves?were?abundant. The experiment research and analyses comparisons of the parameters and road performance characteristics of natural gravel materials were carried out, and the design parameters and road performance indicators of natural grit in the current code were supplemented and adjusted to make it more suitable for Pakistan to use natural gravel materials for road construction. Thesis combines the project,?proposing that mechanism sand and natural sand mixed concrete?is?not inferior?tonatural sand mixed concrete in terms of technical performance, and the overall cost is lower than that of natural sand mixed concrete. The research results are of great significance for saving engineering construction costs, ensuring road performance and prolonging service life.

Estimation of sand liquefaction based on support vector machines

The origin and influence factors of sand liquefaction were analyzed, and the relation between liquefaction and its influence factors was founded. A model based on support vector machines (SVM) was established whose input parameters were selected as following influence factors of sand liquefaction: magnitude (M), the value of SPT, effective pressure of superstratum, the content of clay and the average of grain diameter. Sand was divided into two classes: liquefaction and non-liquefaction, and the class label was treated as output parameter of the model. Then the model was used to estimate sand samples, 20 support vectors and 17 borderline support vectors were gotten, then the parameters were optimized, 14 support vectors and 6 borderline support vectors were gotten, and the prediction precision reaches 100%. In order to verify the generalization of the SVM method, two other practical samples' data from two cities, Tangshan of Hebei province and Sanshui of Guangdong province, were dealt with by another more intricate model for polytomies, which also considered some influence factors of sand liquefaction as the input parameters and divided sand into four liquefaction grades: serious liquefaction, medium liquefaction, slight liquefaction and non-liquefaction as the output parameters. The simulation results show that the latter model has a very high precision, and using SVM model to estimate sand liquefaction is completely feasible.

Research on A Master - slave Multi - microcomputers Control System for Hollow Spindle Fancy Yarn Spinning Machine

In this paper, a successfully studied and developed master - slave muld - microcomputers control system based on PC - BUS for hollow spindle fancy yarn spinning machine, mainly Its overall scheme, software and hardware construction, is introduced. Spinning experiments show that the system achieves satisfactory result. This system can solve the diftkultles of mechatronical fusion between domestic hollow splndk fancy yarn spuming muchine and its microcomputer control technology.

DK7732-1 Electric Spark CNC Wire-Cuffing Machine

The machine tool is one of the new products developed and produced by the Shanghai No.8 Machine Tool Plant. It adopts a lift adjustable wiretrame and molybdenum filament tensioning mechanism with large cutting thickness and high machining precision. It is equipped with an advanced IBM-PC 386 microcomputer-controlled system, with strong performance and CRT display. Man/

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Experimental analysis of sand particles' lift-off and incident velocities in wind-blown sand flux

The probability distributions of sand particles＇ lift-off and incident velocities in a wind-blown sand flux play very important roles in the simulation of the wind-blown sand movement. In this paper, the vertical and the horizontal speeds of sand particles located at 1.0 mm above a sand-bed in a wind-blown sand flux are observed with the aid of Phase Doppler Anemometry （PDA） in a wind tunnel. Based on the experimental data, the probability distributions of not only the vertical lift-off speed but also the lift-off velocity as well as its horizontal component and the incident velocity as well as its vertical and horizontal components can be obtained by the equal distance histogram method. It is found, according to the results of the X^2-test for these probability distributions, that the probability density functions （pdf＇s） of the sand particles＇ lift-off and incident velocities as well as their vertical com- ponents are described by the Gamma density function with different peak values and shapes and the downwind incident and lift-off horizontal speeds, respectively, can be described by the lognormal and the Gamma density functions, These pdf＇s depend on not only the sand particle diameter but also the wind speed.

基于SCSO-SVM算法的光伏组件故障识别

光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine,SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine,PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine,GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine,SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine,GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine,WOA-SVM)算法。首先,六种SVM混合算法都克服了SVM诊断结果易受参数初始值影响的缺点,识别精度相较传统SVM算法都有所提升,但是识别时间都增加。其次,7种算法中SCSO-SVM识别效果最好,克服了SVM易受参数初始值的影响,相较SVM识别精度提高了约9.4594%;是因为更能有效找到SVM惩罚因子和核函数参数。然后,对于同一种算法而言,算法的识别精度是随输入特征减少而降低的,是因为输入特征越少,越不能有效表征光伏组件在不同故障类型下的输出属性。但算法的识别时间却不是随输入特征减少而减短。所以选取合适的输入特征才能兼顾算法的故障识别准确率和效率。最后,发现七种算法的识别效果依赖于数据集的影响。原因可能是各个算法参数选择过多导致泛化性有差异,且依赖参数初始值选择。

基于机器学习的砂土邓肯-张模型参数预测

为了给砂土邓肯-张模型参数的确定提供一种不做三轴试验条件下的获取途径,以大量的砂土三轴试验数据为基础,利用机器学习算法(支持向量机),用平均粒径、不均匀系数、曲率系数、相对密实度、干密度等较容易测得的基本物理参数作为输入值,以邓肯-张本构模型参数作为输出值,建立砂土本构参数的预测模型。从输入参数与输出参数的相关性看,输入参数中的干密度对输出参数影响最大;从不同核函数对支持向量机(SVM)预测效果的影响看,RBF核函数预测效果最好;在此基础上,预测邓肯-张本构模型参数。利用建立的参数预测模型,只需进行简单的室内物理性质试验获得基本物理性质参数,即可推定用于工程数值计算的邓肯-张模型参数,提高工程分析的效率和准确性,也可以用于判断室内三轴试验结果的正确性等。

Verifying the accuracy of interlocking tables for railway signalling systems using abstract state machines

Railway transportation system is a critical sector where design methods and techniques are defined by international standards in order to reduce possible risks to an acceptable minimum level. CENELEC 50128 strongly recommends the utilization of finite state machines during system modelling stage and formal proof methods during the verifi- cation and testing stages of control algorithms. Due to the high importance of interlocking table at the design state of a sig- nalization system, the modelling and verification of inter- locking tables are examined in this work. For this purpose, abstract state machines are used as a modelling tool. The developed models have been performed in a generalized structure such that the model control can be done automatically for the interlocking systems. In this study, NuSMV is used at the verification state. Also, the consistency of the developed models has been supervised through fault injection. The developed models and software components are applied on a real railway station operated by Metro Istanbul Co.

Self-aggregating behavior of poly(4-vinyl pyridine)and the potential in mitigating sand production based onπ-πstacking interaction

Unconsolidated sandstone reservoirs are most susceptible to sand production that leads to a dramatic oil production decline.In this study,the poly(4-vinyl pyridine)(P_(4)VP)incorporated with self-aggregating behavior was proposed for sand migration control.The P_(4)VP could aggregate sand grains spontaneously throughπ-πstacking interactions to withstand the drag forces sufficiently.The influential factors on the self-aggregating behavior of the P_(4)VP were evaluated by adhesion force test.The adsorption as well as desorption behavior of P_(4)VP on sand grains was characterized by scanning electron microscopy and adhesion force test at different pH conditions.The result indicated that the pH altered the forms of surface silanol groups on sand grains,which in turn affected the adsorption process of P_(4)VP.The spontaneous dimerization of P_(4)VP molecules resulting from theπ-πstacking interaction was demonstrated by reduced density gradient analysis,which contributed to the self-aggregating behavior and the thermally reversible characteristic of the P_(4)VP.Dynamic sand stabilization test revealed that the P_(4)VP showed wide pH and temperature ranges of application.The production of sands can be mitigated effectively at 20-130℃ within the pH range of 4-8.

一种基于KPCA-SCSO-SVM的装甲车发动机状态评估方法

润滑油在发动机各部件间流动时,不仅发挥其应有的功能,同时也承载了丰富的关于发动机运行状况的信息,能够有效地反映发动机状态.以某型装甲车底盘发动机为对象,提出一种对润滑油信息进行分析以实现发动机状态评估的方法.该方法基于核主成分分析(KPCA)和沙猫群优化(SCSO)算法优化的支持向量机(SVM),使用KPCA对收集的油液数据进行降维处理,得到的降维数据作为SVM的输入.随后,应用SCSO算法优化SVM的关键参数,建立状态评估模型.通过实际数据的实验验证及与其他几种状态评估模型的比较,结果显示该方法准确率达到了97.35%,能有效评估发动机状态,从而为发动机的维护提供重要参考.

玄武岩机制砂混凝土应力-应变关系研究

以云南某隧道废弃玄武岩生产的机制砂为骨料,制备标准棱柱体和立方体试件分别进行单轴抗压试验,分析其受力变形及破坏特性,得到其单轴受压状态下的抗压强度和应力-应变全过程曲线,并结合过镇海模型建立应力-应变关系和模量计算方法,通过开展数值模拟研究验证该应力-应变关系的准确性。结果表明:由于机制砂棱角较为明显,故断裂面或裂缝均出现在骨料和水泥砂浆交界处,破坏时混凝土试件两侧边缘位移较大,出现剥落现象,即呈现X型破坏;与常规河砂混凝土相比,受压破坏时玄武岩机制砂混凝土裂缝较多;随着玄武岩机制砂混凝土强度的增大,其脆性也逐渐增大;玄武岩机制砂混凝土立方体抗压强度试验和轴心抗压强度换算系数建议取0.76;结合过镇海模型建立的玄武岩机制砂混凝土应力-应变关系计算方法能够较好地反映其受力变形特性;拟合值和数值模拟结果较为接近,误差较小,计算结果较为合理。

严重生物降解原油GC-MS特征及油源对比

图牧吉油砂位于松辽盆地边缘,搞清它的油源对于该地区的油气勘探有着十分重要的意义.气相色谱-质谱分析表明,该样品受到了严重的生物降解,部分用于对比的甾、萜烷生标标志物受到了降解,使与之有关的参数值发生了变化,给油源对比带来了困难.……

粒子群算法优化RBF-SVM沙尘暴预报模型参数

为提高沙尘暴的预报准确率,针对目前已出现的RBF-SVM沙尘暴预报模型中的参数优化进行研究.利用基本粒子群优化算法(SPSO算法)中粒子速度及其位置与RBF-SVM模型中参数对相对应,对沙尘暴进行预报,为解决SPSO算法易陷入局部解的缺陷,提出了惯性权值自适应调节的改进粒子群算法(WPSO算法),并对沙尘暴RBF-SVM模型参数进行了优化.仿真结果表明,无论是SPSO算法,还是WPSO算法,在优化RBF-SVM沙尘暴预报模型参数方面都表现出了良好的性能,分别比已有的SVM方法的预报准确率提高了22.3%和45.3%.

基于聚类-二叉树支持向量机的砂土液化预测模型

建立在统计学习理论基础之上的支持向量机(SVM),是一种基于结构风险最小的小样本机器学习方法。经典的支持向量机主要针对二分类问题,而工程实践中遇到的往往是多分类问题。根据影响砂土液化的主要因素,采用聚类分析中的类距离思想,建立了基于聚类-二叉树的多类支持向量机的砂土液化判别模型。该模型可以通过有限样本的学习,建立砂土液化与各影响因素之间的非线性关系。研究结果表明,基于聚类-二叉树支持向量机的层次结构合理,分类精度高,泛化性好,可对砂土液化等级进行较准确判别。

基于RS-PCA-GA-SVM的砂土液化预测方法研究

砂土液化是一种危害性比较大的自然灾害,对砂土液化进行判定预测在地质灾害防治领域中有重要的研究意义。通过粗糙集理论(Rough Set,RS)对影响砂土液化的6个初始评价指标(包括震级、土深、震中距、地下水位、标贯击数和地震持续时间)进行属性约简,去掉冗余或干扰信息,得到基于4个核心预测指标的数据集。通过主成分分析法(Principal Component Analysis,PCA)从核心评价指标中提取出主成分,采用支持向量机(Support Vector Machine,SVM)对数据集进行训练,用遗传算法(Genetic Algorithm,GA)优化参数,建立砂土液化的RS-PCA-GA-SVM预测模型。并结合砂土液化实际数据将预测结果与基于Levenberg-Marquardt算法改进的BP神经网络模型(LM-BP)的预测结果做比较。实例计算表明:基于RS-PCA-GA-SVM模型得到的砂土液化预测结果精度较LM-BP神经网络有很大的提高,判别结果与实际情况比较吻合,可在实际工程中应用。

PK-3型板用机制砂混凝土性能试验研究与应用

试验研究了不同机制砂取代率对PK-3型板用混凝土工作性、抗压强度、劈裂抗拉强度和弹性模量的影响,并对实测弹性模量进行了拟合分析。结果表明:机制砂部分或全取代河砂,改善了混凝土的工作性,提高了混凝土抗压强度、劈裂抗拉强度和弹性模量;通过回归分析拟合出的弹性模量函数表达式与实测数据拟合度较好;采用机制砂混凝土生产的PK-3型板施工方便、和易性较好,且强度满足要求,脱模后无开裂现象。

ZDH-30型振动电机式振动混砂机的参数选择与计算

介绍了ZDH-30型振动电机式振动混砂机的结构方案、主要参数的选择与计算方法及机器的主要技术参数。

Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma

Background and aims:Hepatocellular carcinoma(HCC),which is prevalent worldwide and has a high mortality rate,needs to be effectively diagnosed.We aimed to evaluate the significance of plasma microRNA-15a/16-1(miR-15a/16)as a biomarker of hepatitis B virus-related HCC(HBV-HCC)using the machine learning model.This study was the first large-scale investigation of these two miRNAs in HCC plasma samples.Methods:Using quantitative polymerase chain reaction,we measured the plasma miR-15a/16 levels in a total of 766 participants,including 74 healthy controls,335 with chronic hepatitis B(CHB),47 with compensated liver cirrhosis,and 310 with HBV-HCC.The diagnostic performance of miR-15a/16 was examined using a machine learning model and compared with that of alpha-fetoprotein(AFP).Lastly,to validate the diagnostic efficiency of miR-15a/16,we performed pseudotemporal sorting of the samples to simulate progression from CHB to HCC.Results:Plasma miR-15a/16 was significantly decreased in HCC than in all control groups(P<0.05 for all).In the training cohort,the area under the receiver operating characteristic curve(AUC),sensitivity,and average precision(AP)for the detection of HCC were higher for miR-15a(AUC=0.80,67.3%,AP=0.80)and miR-16(AUC=0.83,79.0%,AP=0.83)than for AFP(AUC=0.74,61.7%,AP=0.72).Combining miR-15a/16 with AFP increased the AUC to 0.86(sensitivity 85.9%)and the AP to 0.85 and was significantly superior to the other markers in this study(P<0.05 for all),as further demonstrated by the detection error tradeoff curves.Moreover,miR-15a/16 impressively showed potent diagnostic power in early-stage,small-tumor,and AFP-negative HCC.A validation cohort confirmed these results.Lastly,the simulated follow-up of patients further validated the diagnostic efficiency of miR-15a/16.Conclusions:We developed and validated a plasma miR-15a/16-based machine learning model,which exhibited better diagnostic performance for the early diagnosis of HCC compared to that of AFP.

基于LS-SVM的渤海油田防砂设计优化方法研究

渤海油田储层以疏松砂岩为主,常用的经验出砂预测和防砂设计方法存在考虑因素单一和针对性较差等问题,部分井按照原有设计方法施工后,在生产后期发生了出砂现象,急需在设计阶段就充分考虑单井个性因素的综合影响。为此,利用最小二乘支持向量机(LS-SVM)计算模型,基于渤海油田已经投产生产井的出砂情况,构建了综合考虑防砂方式、工具厂商及含水体积分数变化等多个因素的出砂预测模型和精细化防砂设计优化方法。以渤海A油田为例,结合该油田已经投产的28口生产井的实际出砂情况,建立了该油田的单井出砂预测模型,并利用5口井的实际数据验证了该模型的准确性。基于此模型对渤海X井的防砂设计进行了局部优化,降低了该井生产后期的出砂风险。LS-SVM出砂预测模型的计算精度较高,可用于渤海油田单口生产井的出砂预测与防砂设计优化,有助于打造精细化、个性化的防砂设计方案。