Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.     

固定双桨驱动的无人水面艇自主直线路径跟踪系统

针对直流电机驱动固定双桨的无人水面艇,介绍了一种自主直线路径跟踪系统,该系统由岸基监控系统和艇载控制系统组成,具有自主航行和遥控航行两种工作模式,可在自主航行出现危险时切换到遥控模式,保证航行安全。岸基监控系统通过数传电台与艇载控制系统通信,向艇载控制系统发送控制命令,接收并显示其传回的状态信息;艇载控制系统以工控机为主控单元,进行数据采集与解算,与岸基监控系统通信,并为直线路径跟踪控制算法提供程序接口;GPS双天线高精度测向定位系统为直线路径跟踪控制算法提供位置和航向信息,直线路径跟踪控制算法根据距离偏差和航向偏差计算出左右两侧电机电压,进而控制无人水面艇航行。实验分别采用了PID、模糊控制和模糊PID三种控制方法,系统实际水上实验表明,在风力2~3级,晴到多云天气条件下,无人水面艇对目标路径的最大跟踪误差小于0.6 m。     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

Point-augmented biquadratic C1 subdivision surfaces

Shape artifacts, especially for convex input polyhedra, make Doo and Sabin’s generalization of bi-quadratic (bi-2) subdivision surfaces unattractive for general design. Rather than tuning the eigenstructure of the subdivision matrix, we improve shape by adding a point and enriching the refinement rules. Adding a guiding point can also yield a polar bi-2 subdivision algorithm. Both the augmented and the polar bi-2 subdivision are complemented by a new Primal Bi-2 Subdivision scheme. All surfaces are $C^1$ and can be combined.     

基于小波包变换与Wiener滤波的SMT焊点图像去噪技术

针对目前SMT(surface mount technology)焊点图像去噪效果不理想的问题,提出了一种基于小波包变换与wiener滤波的SMT焊点图像去噪新方法.利用小波包对图像进行分解,可以同时对SMT焊点图像的低频和高频部分进行多层分解,有利于保留图像信息,减少噪声对图像的影响.通过对图像的小波包系数的分析,对小波包树高频系数进行Wiener滤波,保留低频系数;然后进行小波包反变换,重构得到SMT焊点去噪后图像.实验表明,提出的方法不仅可以有效地去除SMT焊点图像的噪声,而且能很好地保留原图像的边缘信息,与传统方法相比,去噪性能和去噪声效果有一定的提高.     

DTC变频技术在污水处理中的应用

变频器以其客观的节能效果而被广泛推广使用,特别是ABB变频器的DTC控制技术更是交流传动中革命性的电机控制方式,DTC在零速时产生满转矩,特别适用于污水处理行业中卡鲁塞尔。氧化沟中表面曝气设备的控制。     

New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

This paper discusses the lessons learned from analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-Surface Temperature/Emissivity (LST) products in the current (V4) and previous versions, and presents eight new refinements for V5 product generation executive code (PGE16) and the test results with real Terra and Aqua MODIS data. The major refinements include considering surface elevation when using the MODIS cloudmask product, removal of temporal averaging in the 1 km daily level-3 LST product, removal of cloud-contaminated LSTs in level-3 LST products, and the refinements for the day/night LST algorithm. These refinements significantly improved the spatial coverage of LSTs, especially in highland regions, and the accuracy and stability of the MODIS LST products. Comparisons between V5 LSTs and in-situ values in 47 clear-sky cases (in the LST range from − 10 °C to 58 °C and atmospheric column water vapor range from 0.4 to 3.5 cm) indicate that the accuracy of the MODIS LST product is better than 1 K in most cases (39 out of 47) and the root of mean squares of differences is less than 0.7 K for all 47 cases or 0.5 K for all but the 8 cases apparently with heavy aerosol loadings. Emissivities retrieved by the day/night algorithm are well compared to the surface emissivity spectra measured by a sun-shadow method in two field campaigns. The time series of V5 MODIS LST product over two sites (Lake Tahoe in California and Namco lake in Tibet) in 2003 are evaluated, showing that the quantity and quality of MODIS LST products depend on clear-sky conditions because of the inherent limitation of the thermal infrared remote sensing.     

Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin

Spatially distributed estimates of evaporative fraction and actual evapotranspiration are pursued using a simple remote sensing technique based on a remotely sensed vegetation index (NDVI) and diurnal changes in land surface temperature. The technique, known as the triangle method, is improved by utilizing the high temporal resolution of the geostationary MSG-SEVIRI sensor. With 15 min acquisition intervals, the MSG-SEVIRI data allow for a precise estimation of the morning rise in land surface temperature which is a strong proxy for total daytime sensible heat fluxes. Combining the diurnal change in surface temperature, dT_s with an interpretation of the triangular shaped dT_s − NDVI space allows for a direct estimation of evaporative fraction. The mean daytime energy available for evapotranspiration (R_n − G) is estimated using several remote sensors and limited ancillary data. Finally regional estimates of actual evapotranspiration are made by combining evaporative fraction and available energy estimates. The estimated evaporative fraction (EF) and actual evapotranspiration (ET) for the Senegal River basin have been validated against field observations for the rainy season 2005. The validation results showed low biases and RMSE and R² of 0.13 [−] and 0.63 for EF and RMSE of 41.45 W m^− 2 and R² of 0.66 for ET.     

Real-Time GPU Silhouette Refinement using Adaptively Blended Bézier Patches

We present an algorithm for detecting and extracting the silhouette edges of a triangle mesh in real time using Graphical Processing Units (GPUs). We also propose a tessellation strategy for visualizing the mesh with smooth silhouettes through a continuous blend between Bézier patches with varying level of detail. Furthermore, we show how our techniques can be integrated with displacement and normal mapping. We give details on our GPU implementation and provide a performance analysis with respect to mesh size.