基于XGBoost的低渗油田储层粒度预测

针对低渗油田储层粒度预测问题,本文提出利用机器学习中的极致剃度提升树(extreme gradient boosting,XGBoost)来对低渗油田储层粒度进行预测的方案.首先,根据问题构建合适的XGBoost模型,然后根据已有的岩心储层粒度特征值与其余测井信息的关系,选取适用于粒度预测的测井曲线建立样本库,最后利用...     

焊缝跟踪视觉传感器中图像多类型特征选择与提取

针对在焊接机器人焊缝跟踪传感器中不同类型工件焊缝接头的结构光图像特征,提出一种图像特征选择与提取方法.首先,对图像进行自适应分割,提取出激光条纹中心线.再根据三种典型焊缝接头类型:预留坡口对接类,无坡口直接对接类和左、右搭接类型焊缝的结构光图像的特点,分别选择激光条纹曲线的沟槽中心、条纹曲线的转折点和间断点作为焊缝特征点,分别进行提取.对不同类型焊缝结构光图像的处理实验结果,验证了本文方法的有效性.     

A novel system for off-line 3D seam extraction and path planning based on point cloud segmentation for arc welding robot

Due to ever increasing precision and automation demands in robotic welding, the automatic and robust 3D seam extraction has become a research hot-spot of the welding robots. At present, most of the research work about seam extraction is aimed at butt joints. Nevertheless,too little work has been devoted to fillet joints and lap joints. Consequently,to achieve robust 3D seam extraction of different weld seams, a novel seam extraction system is proposed according to the 3D structures of welding work pieces. Firstly, a fringe projection system based on Digital Light Processing(DLP) projector is designed to measure the appearance of welding work pieces. Secondly, fusion of the shape information of welding work piece, a 3D seam extraction algorithm is proposed based on point cloud segmentation. Finally, according to the space structure of weld seams, the 3D seam path model and pose estimation are solved based on the established mathematical model of weld seams. Experiments show that the proposed algorithm could well solve different weld seams, such as fillet joints, butt joints and lap joints. Meanwhile, it could well overcome the influence of the materials of welding work pieces, scratch and rust.     

聚乙烯管焊接接头相控阵超声检测的优化设计

在管道工程应用中,由于良好的耐腐蚀性和韧性,聚乙烯(PE)管与其他材料相比具有显着优势,已广泛地应用在核电、油气输送领域;PE管焊接过程中,焊接参数、人员操作和环境等因素会影响聚乙烯管道的焊接情况,可能致使焊接接头内部形成缺陷,影响管道安全运行,而相控阵超声检测技术能够实现聚乙烯管道焊接接头内部结构的无损检测及评估;因此开展PE管焊接接头的相控阵超声实际检测工作,研究了聚乙烯材料的声衰减规律及声频散效应,优化了焊接接头的超声相控阵检测工艺,基于CIVA仿真软件实现了聚乙烯管道相控阵超声检测的声场模拟和缺陷仿真,通过研究检测参数及声学参量变化对检测结果的影响,获得了PE管焊接接头的最优检测工艺;通过加工带有典型缺陷的聚乙烯热熔对接接头试样,对前期基于仿真结果设计的检测工艺以及针对焊接接头相控阵检测研发的楔形耦合组件进行可靠性测试和工艺试验;研究结果为PE管接头缺陷的超声相控阵检测提供了工艺设计的理论支持和可靠的研究方法。     

电子鼻传感器阵列优化与谷物霉变程度的检测

研制一套适合对谷物霉变进行检测的电子鼻系统,对6个霉变程度的稻谷进行了检测.主成份(PCA)分析结果显示6个霉变程度的稻谷是可以区分的,对前三个主成分的载荷因子进行分析去掉冗余传感器,PCA分析结果显示去掉冗余传感器并不影响分类结果.最后用BP神经网络对所得的数据进行模式识别,30组测试样本对霉变菌落总数的预测结果平均相对误差为1.010 30,最大相对误差为3.942 57.实验结果表明系统对稻谷霉变程度的检测具有很高的分析精度.     

铈掺杂WO_3纳米材料气敏特性研究

以金属W粉为原料采用溶胶—凝胶法制得纳米级WO3粉体,探讨了不同CeO2添加量对气敏特性的影响。CeO2掺杂WO3材料对挥发性有机化合物(VOCs)气体灵敏度有显著提高,而器件的工作温度有所降低。FE—SEM测试结果说明:CeO2掺杂对晶界的移动形成某种"钉扎"效应,使晶粒减小,比表面积增大。复阻抗谱分析认为,Ce4+主要存在于晶界,使晶界电阻增大,晶界电容减小,提高了WO3材料的气敏特性。     

Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.     

Detection of fillet weld joints using an adaptive line growing algorithm for robotic arc welding

One of the main challenges for robotic welding in low to medium volume manufacturing or repair work is the time taken to programme the robot path for a new job. It is often cheaper and more efficient to weld the parts manually. There are many papers published on the detection of butt welds, however there is no mature method for the identification of fillet welds which are more common. This paper presents a novel method that can autonomously identify fillet weld joints regardless of the base material, surface finish and surface imperfections such as scratches, mill scale and rust. The new method introduces an adaptive line growing algorithm for robust identification of weld joints regardless of the shape of the seam. The proposed method is validated through experiments using an industrial welding robot in a workshop environment. The results show that this method can detect realistic fillet weld joints for industrial arc welding applications.     

Retrieval of snow grain size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The snow grains are modeled as fractal rather than spherical particles in order to account for their irregular shape. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived grain size shows a good sensitivity to snow melting and snow precipitation events. Pre-processing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust discrimination of clouds over bright snow and ice. Because in-situ grain size measurements over Greenland were not available at the time of this work, the validation was performed using data of Aoki et al. (Aoki, T., Hori, M., Motoyoshi, H., Tanikawa, T., Hachikubo, A., Sugiura, K., et al. (2007). ADEOS-II/GLI snow/ice products — Part II: Validation results using GLI and MODIS data. Remote Sensing of Environment, 111, 274-290) collected at Barrow (Alaska, USA), and Saroma, Abashiri and Nakashibetsu (Japan) in 2001-2005. The retrievals correlate well with measurements in the range of radii ~ 0.1-1 mm, although retrieved optical diameter may be about a factor of 1.5 lower than the physical measured diameter. As part of validation analysis for Greenland, the derived grain size from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM/I radiometer which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS grain size was compared with predictions of the snow model CROCUS driven by measurements of the automatic weather stations of the Greenland Climate Network. We found that the MODIS value is on average a factor of two smaller than CROCUS grain size. This result agrees with the direct validation analysis indicating that the snow reflectance model may need a “calibration” factor of ~ 1.5 for the retrieved grain size to match the physical snow grain size. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of snow grain size for four permanent sites, the paper presents maps of this important parameter over the Greenland ice sheet for the March-September period of 2004.     

Parametric investigation of the buckling performance of metal-plate-connected joints

A comprehensive analytic study has been conducted to investigate the instability problems of metal-plate-connected (MPC) joints in light frame trusses. The primary objective in this study is to determine the governing factors that constitute the buckling of the metal connectors and their effects on the structural response of joints.The numeric data presented in this paper has emerged from a broad base that was founded on over 350 advanced computer simulations, and was supported by available experimental results obtained by others. This basic-to-applied research includes practical engineering parameters such as size of gaps, shear lengths, gauge o connectors, size of unbraced areas, failure modes, and progressive disintegration of joints. Square-end members have been emphasized though the results cover the custom-made fitted joints.The results indicate that chord shears cause and dominate the buckling of MPC joints, and the shear length has a more pronounced effect than the size of gaps. Further, large gauges and small unbraced areas improve the buckling response. Several practical recommendations have been suggested throughout the paper. The study reveals that the engineering of single web-to-chord MPC joints should not be interpreted for multi-area joints, even where one web is in tension and the other in compression. Finally, the results obtained from this study favorably agree with experimental data by others, and the classic buckling theories for other structural components.     

Snow-cover characteristics using Hyperion data for the Himalayan region

The present study demonstrates the potential of hyperspectral imaging Hyperion data for the mapping of snow grain size and snow mixed objects in the Himalayan region. The spectral signatures collected using a field spectroradiometer for different types of snow grain size and vegetation-/soil-mixed snow were used for the identification/comparison of collected image spectra. Snow grain size was estimated using the spectral angle mapper (SAM) method and compared/validated with the grain sizes obtained from grain index and asymptotic radiative transfer (ART) theory methods. The overall matching area was 81% among different snow grain size classes using SAM and grain index methods. A good match was observed between the class-wise (i.e. fine, medium, and coarse) grain sizes and quantitatively obtained grain sizes using the ART theory; however, the grain diameters obtained from the ART method were small, which may be due to the difference between equivalent grain and effective optical grain size. The obtained grain size was also supported by the field snow conditions of the region. The spectra of mixed snow cover were collected from Hyperion images and compared with the spectra collected from snow mixed objects during field experiments. The vegetation-mixed and contamination/patchy (soil-mixed) snow-cover areas were identified in Hyperion scenes and the results supported using high-resolution images and snow conditions of the region. This study is of importance in the mapping of snow-cover characteristics, which can provide valuable input for climatology, hydrology, and mountain hazard applications.     

Numerical–experimental crack growth analysis in AA2024-T3 FSWed butt joints

This paper deals with a numerical and experimental investigation on the influence of residual stresses on fatigue crack growth in AA2024-T3 friction stir welded butt joints. The computational approach is based on the sequential usage of the Finite Element Method (FEM) and the Dual Boundary Element Method (DBEM). Linear elastic FE simulations are performed to evaluate the process induced residual stresses, by means of the contour method. The computed stress field is transferred to a DBEM environment and superimposed to the stress field produced by a remote fatigue traction load applied on a friction stir welded cracked specimen; the crack propagation is then simulated according to a two-parameter growth model. Numerical results have been compared with experimental data showing good agreement and evidencing the predictive capability of the proposed method. The obtained results highlight the influence of the residual stress distribution on crack growth.     

Optimization of laser butt welding parameters based on the orthogonal array with fuzzy logic and desirability approach

This paper presents the study carried out on 3.5 kW cooled slab laser welding of 904 L super austenitic stainless steel. The joints had butts welded with different shielding gases like argon, helium and nitrogen at a constant flow rate. Super austenitic stainless steel (SASS) normally contains high amount of Mo, Cr, Ni, N and Mn. The mechanical properties are controlled to obtain good welded joints. The quality of the joint is evaluated by studying the features of weld bead geometry such as bead width (BW) and depth of penetration (DOP). In this paper, the tensile strength and bead profiles (BW and DOP) of laser welded butt joints made of AISI 904 L SASS are investigated. Taguchi approach is used as statistical design of experiment (DOE) technique for optimizing the selected welding parameters. Fuzzy logic and desirability approach are applied to optimize the input parameters considering multiple output variables simultaneously. Confirmation experiment has also been conducted for both the analyses to validate the optimized parameters.     

基于涡流阵列检测技术的奥氏体不锈钢压力管道缺陷判别方法研究

奥氏体不锈钢压力管道的检测由于现有检测手段的限制,难以保证其检测效果。本文针对工程应用中出现的问题,研究基于涡流阵列检测技术（ECA）的奥氏体不锈钢压力管道对接接头缺陷判别方法。根据提离信号、应力腐蚀开裂、圆形缺陷、条形缺陷的阻抗特征和C扫成像特征,通过缺陷C扫图谱形状及表面尺寸、阻抗图的阻抗幅值和阻抗相位,实现奥氏体不锈钢管道对接接头的表面开口缺陷和近表面缺陷的有效检出,并将所提方法应用于实际现场检测中。对比传统渗透检测方法,本文提出的方法有更好的缺陷检出率及缺陷判别率,试验结果验证了本文方法的可行性及有效性。     

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia.

The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is − 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient − 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29).     

一种基于雪粒径演化过程的积雪亮温模拟新方法

本研究采用HUT模型、DMRT模型和MEMLS模型模拟积雪雪粒子与不同波段（18.7 GHz和36.5 GHz）微波相互作用（吸收和消光）,并用于辐射传输模型。而雪粒径的获取一直是一个难点,本研究由Jordan91雪粒径演化模型演化得到雪粒径,并将其作为辐射传输模型的输入参数,基于像元内实测数据进行混合像元18.7和36.5 GHz水平极化亮温模拟。结果表明：采用HUT模型、DMRT模型和MEMLS模型的消光系数在18.7 GHz时模拟亮温的偏差分别为-3.6、-1.8和-0.7 K,在36.5 GHz时分别为4.0、10.4和14.4 K。对于18.7 GHz水平极化和36.5 GHz水平极化,基于有效雪粒径的亮温模拟与基于雪粒径演化过程的亮温模拟精度呈现出很好的线性关系。因此,基于雪粒径演化过程的方法是一种合适的获取辐射传输模型中雪粒径参数的方法。     

A New Method of Simulating Bright Temperature of Snow Cover based on Snow Grain Size Evolution Process

This research used HUT model, DMRT model and MEMLS model to simulate interactions(absorption and extinction) between snow grainsfor different wave bands (18.7 GHz and 36.5 GHz) of microwave which were used for radiative transfer model. Obtaining the snow grain size is always a difficulty. So this research used Jordan91 snow grain size evolution model to evolve snow grain size which was regarded as input parameter of radiative transfer model, and used measured data to simulate spaceborne brightness temperature for 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization in a mixed pixel. The results showed that the bias of simulation brightness temperature using extinction coefficient of HUT model, DMRT model and MEMLS model for 18.7 GHz horizontal polarization were -3.6 K、-1.8 K and -0.7 K respectively, and for 36.5 GHz horizontal polarization were 4.0 K、10.4 K and 14.4 K respectively. For 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization, the bright temperature simulation based on effective snow grain size shows a good linear relationship with the brightness temperature simulation basedon snow grain size evolution process. Therefore, the method based on the snow grain size evolution process is a suitable method for obtaining the snow grain size parameter in the radiative transfer model.     

灰色神经网络在粮食产量预测中的应用

研究粮食准确预测优化问题,粮食产量受到多种因素影响,同时具有复杂的非线性和随机性特点,传统单一模型难准确对其变化规律进行准确描述,预测精度较低。为提高粮食产量预测精度,提出一种将灰色理论和BP神经网络相结合的粮食产量预测模型。首先采用灰色GM(1,1)预测模型动态预测粮食产量变化趋势,然后运用BP神经网络对灰色GM(1,1)模型预测结果进行修正,以提高粮食产量预测精度。采用1978-2008年我国粮食产量数据对预测模型性能进行仿真测试,仿真结果表明,组合预测模型提高了粮食产量的预测精度,更能描述粮食产量变化规律,为粮食产量准确预测提供了一种有效研究方法。     

ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.