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.

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。

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。

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.

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.

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/

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。

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.

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.

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.

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.

基于SCSO-SVM的行业供应链风险检测优化方法

为实现供应链风险等级的高精度检测,基于SVM的参数设置对SVM的性能的影响,提出一种基于沙丘猫群算法(SCSO)优化SVM的供应链风险等级检测方法。首先,通过层次分析法建立供应链风险等级评价指标体系;之后,由于SVM的参数设置会影响到SVM的性能,利用SCSO算法对SVM的参数进行了优化,并给出了一种新的基于SCSO-SVM的供应链风险识别算法。与单独的SVM模型相比,SCSO-SVM的供应链风险检测的准确率分别提高了3.06、7.04个百分点,从而说明SCSO-SVM可以有效提高供应链风险检测的精度。

Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

自动巡航草方格固沙机结构设计分析及试验

针对荒漠化治理对草方格固沙机的需求,提出了一种集底盘、开沟机构、草箱机构、插草机构、覆压沙机构、播种及洒水机构、升降机构与一体的多功能草方格固沙机。分析了“T+一”式草方格种植模式的优势,提出了自动巡航草方格固沙机整体结构方案,并对开沟机构、草箱机构、插草机构、覆压沙机构、播种及洒水机构、升降机构等关键组件的功能和结构进行了详细分析。应用三维建模软件建立虚拟机构模型,对推草和插草机构进行了运动仿真分析,并对整机进行了作业性能试验。结果表明:各机构构件间无干涉,构件运动仿真轨迹符合功能要求,杆件尺寸参数设计合理,机器作业效率高,可有效保证草方格铺设的准确性和有效性,满足机械化固沙的要求,具有良好的通用性和适应性。

用于微塑料分离收集的智能沙滩垃圾处理机

为解决沙滩塑料垃圾处理中的微塑料分离收集问题,实现设备智能化,设计了一种智能沙滩垃圾处理机。这一垃圾处理机利用摄像头成像技术,结合Opencv算法,对塑料垃圾进行快速识别,利用超声传感器确定垃圾位置和距离,以单片机为主控,实现智能垃圾处理。沙子和垃圾混合物运送至垃圾处理机后,通过所设置的振筛机构实现中大型塑料垃圾与微塑料垃圾的分离。以海水为浮选液,利用密度差实现沙子和微塑料垃圾的分离。利用无轴螺旋机构,实现沙水分离。在测试的60min内,塑料垃圾识别正确率为93.6%,收集到微塑料垃圾315g。这一垃圾处理机低碳环保,占地面积小,质量轻,可用于沙滩垃圾清理,也可作为机械创新设计实践教学与培训的工具。

玄武岩机制砂不规则度对混凝土流动和力学性能影响研究

为深入探究机制砂不规则颗粒含量对混凝土流动和力学性能的影响,引入了机制砂不规则度概念。采用室内试验方法,对比研究了各材料配比下玄武岩机制砂不规则度对混凝土流动特性和力学性能的影响。研究结果表明:不同材料配比下,玄武岩机制砂混凝土的坍落度、抗折强度和抗压强度等指标均随机制砂不规则度的增加而不断降低,且高强度混凝土配比方案比低强度混凝土配比方案的降低幅度更大;当机制砂不规则颗粒含量超过30%时,不规则度对任何材料配比的混凝土力学性能和流动性能均有较大的影响;机制砂不规则度对混凝土抗折强度的影响大于对抗压强度的影响。

矿物掺合料对全机制砂灌浆料的影响研究

试验采用普通硅酸盐水泥与快硬硫铝酸盐水泥的复合胶凝体系,选用石灰石机制砂作为细集料,制备全机制砂灌浆料。选用硅灰、粉煤灰与石粉作为矿物掺合料,研究矿物掺合料对全机制砂灌浆料流动度、抗压强度与竖向膨胀率的影响,并利用SEM电镜对全机制砂灌浆料进行微观机理分析。研究结果表明:矿物掺合料复掺有利于提高灌浆料的流动性能与力学性能,改善灌浆料的界面过渡区,通过正交试验优化机制砂灌浆料配方,得到的最优矿物掺合料掺量为硅灰5%、粉煤灰6%、矿粉8%。

机制砂混凝土的力学性能与声发射特征研究

基于数字图像处理技术,测定了机制砂的形状参数。结合数值建模方法,重构了不同细观特征的机制砂混凝土模型,并通过离散元分析软件,定量化地研究了机制砂混凝土的力学与声发射规律。研究结论如下:(1)单轴压缩下不同细观特征机制砂混凝土呈现出同样的应力应变规律,可分为弹性阶段,屈服阶段和软化阶段。(2)机制砂圆润度越高,机制砂混凝土峰值应力越大,同时塑性承载能力越强,其整体力学性能越好。(3)机制砂混凝土模型声发射和能量演化可分为平静期,缓增期和活跃期3个时期;声发射事件带预示着试样内宏观裂纹的扩展路径;试样内部声发射事件数密集或高能量释放区域更容易产生张开型裂隙。