Surface Preparation of Tantalum for Encapsulation and Adhesive Bonding

A number of methods of surface preparation of tantalum for encapsulation in silicone rubber and for structural adhesive bonding were explored. The only ones which could be generally useful were boiling for 24 hours in distilled water (28% improvement) or boiling for 4 hours in 20% sodium hydroxide solution followed by boiling for 2 hours in dilute hydrochloric acid (34% improvement). An alternative, which could sometimes be used, was heating in air for at least 2 hours at 100°C.     

真空粉末绝热低温贮罐制造的部分工艺方法

在真空粉末绝热低温贮罐的制造过程中、通过吸附器分子筛的活化、夹层真空系统的处理和充装珠光砂等工艺方法的改进,可较大地缩短产品的制造周期并提高产品的质量。     

提高MG2×150/700-WD型采煤机截割部轴承寿命周期的设计改进

通过改进结构及轴承类型,提高MG2×150/700-WD型交流电牵引采煤机截割部轴承使用周期。     

Adaptive Safety Motion Control for Underactuated Hovercraft Using Improved Integral Barrier Lyapunov Function

International Journal of Control, Automation and Systems - In this paper, we investigate the problem of safety motion control for an underactuated hovercraft from subject to safety constraint on...     

Deformation transition of intact coal induced by gas injection

Gas migration in coal bed is a multiple-physical process, of which not only includes gas desorption/diffusion through coal matrix and gas Darcy flow through the cleat system, but also results in deform...     

Effect of Al in Ti－microalloyed Mn－Ni－Mo welding wire on microstructure and toughness of deposited metal

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An interactive lung field segmentation scheme with automated capability

Feasible clinical application of any automated segmenting methodology demands more than just accuracy. Amendment to the automated delineation is necessary when the algorithm fails, however, integrated solution to such a scenario is largely absent in literature. Hence in this survey we devised an architecture that does both the automated and interactive lung field localizations using a single segmenting engine—random walker algorithm—so that intuitive amendment is only necessary when the automated generated delineation is unsatisfactory. The algorithm proceeds by first extracting 18 intensity profiles running horizontally, each of them equally spacing apart, and in each intensity profile three extreme points denoting the two lungs and the esophagus are determined through profile matching. This done, the algorithm removes profiles that do not intersect with the lung, and the rest of the extreme points are plugged into random walker algorithm to perform segmentation. The achieved accuracy in localization by the above was 0.8875 in terms of overlap measure (the maximum value for this parameter is 1) over 341 images. In the case where unsatisfactory delineation prompts amendment necessary, the user can interactively segment the lung by just a shift on some of the previous-determined points to the desired locations, and random walker algorithm is run again with the amended input. By such a fusion, the benefits of both the automated and interactive segmentation are shared in a single architecture.