Experimental and modeling approaches of MR behaviors under large step strains

Rheological properties of MR fluids under large step strain shear are presented in this paper. The experiments were carried out using a rheometer with parallel-plate geometry. Under the large step strain shear, MR fluids behave as nonlinear viscoelastic properties, where the stress relaxation modulus, G(t, γ), shows a decreasing trend with step strain. The experimental results indicate that G(t, γ) obeys time-strain separability. Thus, a mathematical form based on finite exponential serials is proposed to predict MR behavior. In this model, G(t, γ) is represented as the product of a linear stress relaxation, G(t), and the damping function, h(γ), i.e. G(t, γ)=G(t) h(γ). G(t) is simply represented as a three-parameter exponential serial and h(γ) has a sigmoidal form with two parameters. The parameters are identified by adopting an efficient optimization method proposed by Stango et al. The comparison between the experimental results and the model-predicted values indicates that this mathematical model can accurately predict MR behavior.     

MTJ MRAM的特性分析与设计

对 MTJ(磁隧道结 )的 GMR(巨磁阻 )效应进行了分析。 MTJ的结构、形态和工作条件会对 GMR效应产生不同的影响。提出了一种 4× 1位 MTJMRAM(磁存储器 )的电路结构 ,每个 MRAM的存储单元由一个MTJ和一个 MOSFET构成 ,用 MTJ两磁极磁化方向的相对取向表示所存储的数据 ,数字线和位线电流产生磁场的共同作用可完成 MRAM数据的写入。     

用CT技术确定砂岩的孔隙度

周渤然等．用ＣＴ技术确定砂岩的孔隙度．测井技术，１９９４，１８（３）：１７８～１８４本文首先提出用计算机层析（ＣＴ）技术确定砂岩孔隙度的原理和方法。通过对四块人工岩样和六十块砂岩岩样的测定，其结果表明该方法是可靠的。使用ＣＴ技术可定量、定性地解释某些岩样的内部特征，重矿物分布及裂缝变化情况。实践表明，采用图象减法技术则有助于消除光束硬化现象造成的误差。     

Effect of fiber types on relevant properties of porous asphalt

The research was conducted to evaluate the effects of cellulose and polyester fibers on the properties of porous asphalt mixes, using the tests of draindown, abrasion, volumetric properties, rutting, and moisture damage. Images of scanning electron microscopy and X-ray computerized tomography were adopted to identify the microstructure of the fiber and inner stone skeleton of porous asphalt. The influence of rutting parameter （G＊/sin δ） of asphalt modified by different fibers on the rutting resistance of the mixes was investigated. Based upon MOHR-COULOMB theory, the cohesion and the angle of internal friction of the mixes were derived from both indirect tension and unconfined compression strength. The experimental results indicate that fibers mainly stabilize asphalt binder and thicken asphalt film around aggregates. Furthermore, they result in the improved mechanical strength of porous asphalt mixes at high temperature slightly. From comparison analysis, cellulose fibers appear to perform better than polyester fibers in porous asphalt mixes.     

工业CT技术在无损检测炸药部件中的应用综述

综述了美国能源部的有关单位将工业ＣＴ技术用于高能炸药无损检测的研究状况，劳伦斯利弗莫尔国家实验室（ＬＬＮＬ）洛斯阿拉莫斯国家实验室（ＬＡＮＬ）和潘台克斯工厂（ＰａｎｔｅｘＰｌａｎｔ）在这方面做的大量工作，包括ＣＴ机的建造，实验方法的制定和图像的重建等。测试样品有ＰＢＸ－９５０２，ＰＢＸ－９５０１和ＬＸ－１０－２炸药的药饼和半球，对ＣＴ技术在无损检测高能炸药方面的实用性和优越性作了评价。     

虚拟肝脏的规则和非规则手术切除技术

为了提高肝癌手术治疗计划的可靠性,在利用腹部CT图像数据进行肝脏三维重建的基础上,实现了任意角度对虚拟肝脏进行平面切割和切割面结构的显示;通过构造可以调节大小、位置的球体模型,实现了球体切除;通过在肝脏三维模型表面上勾画不规则封闭区域,实现了任意曲面肝脏切除;并依据肝内血管结构进行肝段划分,实现了解剖性肝段切除.临床实验结果表明,文中所述的技术能够满足虚拟手术的需求,有利于医生制定安全可靠的术前计划,提高手术成功率.     

Segmentation of abdominal organs from CT using a multi-level,hierarchical neural network strategy

Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision. Recent studies on multiple feature–classifier combinations show that hierarchical systems outperform composite feature–single classifier models. In this study, how hierarchical formations can translate to improved accuracy, when large size feature spaces are involved, is explored for the problem of abdominal organ segmentation. As a result, a semi-automatic, slice-by-slice segmentation method is developed using a novel multi-level and hierarchical neural network (MHNN). MHNN is designed to collect complementary information about organs at each level of the hierarchy via different feature–classifier combinations. Moreover, each level of MHNN receives residual data from the previous level. The residual data is constructed to preserve zero false positive error until the last level of the hierarchy, where only most challenging samples remain. The algorithm mimics analysis behaviour of a radiologist by using the slice-by-slice iteration, which is supported with adjacent slice similarity features. This enables adaptive determination of system parameters and turns into the advantage of online training, which is done in parallel to the segmentation process. Proposed design can perform robust and accurate segmentation of abdominal organs as validated by using diverse data sets with various challenges.     

基于CT扫描的致密砂岩脆性破坏裂纹扩展规律研究

岩石在荷载作用下产生宏观破坏,其破裂面细观形态变化可以直接地反映出结构破坏特征。目前,常采用扫描电镜法、透镜法和CT扫描技术对岩石细观结构进行表征。运用CT扫描技术对典型脆性岩样单轴压缩破坏后破裂面及全断面进行扫描,获取全断面的细观形态,分析和总结了典型脆性岩样破坏细观裂纹扩展的发展规律:每一条独立裂纹都以一恒定曲率半径系数K随层位向试样内部扩展,直至裂纹曲率半径趋近于无穷。此时若无其他因素影响,其曲率半径则不会再随层位继续扩展;并通过分析定义出岩石脆性破坏状态时裂纹的聚集域和非聚集域,同时根据能量守恒定律得出了衍生裂纹及新次生裂纹产生的判据。