基于激光扫描技术的三维模型重建

通过分析三维激光扫描系统获取的点云数据,得到了利用点云数据构建三维模型的技术、方法和流程。介绍了利用地面三维激光扫描仪获取点云数据的过程以及结合RiSCAN PRO软件和Geomagic Studio软件进行建模的方法。对原始测量的点云数据进行处理(去除噪声,平滑,对多站点数据做拼接配准,提取目标建筑物等)得到正确和完整的目标建筑物的表面信息,然后构建三角网建立它的三维表面模型,最后通过所拍的照片进行纹理映射得到真实的三维模型。实验结果表明,利用上述方法可以有效地处理三维激光扫描获取的点云数据,实现对建筑物快速三维可视化建模。     

SiC增强铝基复合材料

<正> 前言一般来说,增强复合材料可分为三类:①用长的、连续或者完整的高强度材料纤维增强;②用晶须增强(晶须的直径比纤维小得多,纤维直径为几百微米,而晶须直径约为一微米左右;晶须实质上是不连续的,即具有有限长径比的真正无缺陷的高强度材料);③用高强度材料的颗粒或等轴粒子增强。第三种情况与沉淀强化金属合金有很多     

以固态核径迹探测器方法为基础的铀矿勘探钻孔测井技术

根据不同介质中氡迁移的不同机制,本文作者研究了一种新的铀矿勘探钻孔测井技术。这种新技术参照了其他钻孔测井方法,其优点是:有可能找到钻孔所需深度的答案.在空气和水中,氡迁移的2种形式一直被用来测定氡分布。对这2种研究的形式开发了计算机程序,以期从理论上对氡径迹密度(作为钻孔深度的一个函数)进行计算。本文将理论上与实际上所获得的数据进行了比较.     

Control release of bactericidal ion by an electronically driven system

There is a dramatic proliferation of research related to electronically generated metallic bactericidal ions. Unfortunately, there are no literature reviews or discussions concerning metallic-nanoparticle suspension as a drug reservoir for iontophoretic applications. Heavy metals, especially silver, are frequently used to treat infection before the development of systemic antimicrobial agents. For medical applications, the conversion of colloidal silver into its ionic form is required; however, it does not directly use silver salts to provide the silver ions, due to the counter-ion (e.g., NO3-, SO4-) content of silver salts, which may cause severe problems to the body as the silver ion is consumed. The goal of this research is to develop an electronic dissociation system (EDS), which can provide a relatively safe bactericidal ion (Ag+) solution from the silver nanoparticles that has a controllable electric field. In this study, an ionic selective electrode (ISE) was used to observe and identify the details of the system activity throughout the course of the experiment. Both qualitative and quantitative data analyses were performed. The experimental data indicated that EDS can control the parameters of ion-releasing profiles, including the area under curve (AUC, dosage), rate of profile rise and fall, total dissociation time, peak time, and peak level concentration by a constant voltage (CV) mode or constant current (CC) mode. However, the CC mode was proved to be more controllable (an increase of 200 microA, equal to an increase of 1 ppm/hour), as the premeditated driving force is more precise, and relies on the current not voltage. This technology will be used to develop a chemical residue-free administration of control-released medical devices for iontophoretic applications.     

Computer-assisted assessment of depression and function in older primary care patients

We wanted to test the psychometric reliability and validity of self-reported information on psychological and functional status gathered by computer in a sample of primary care outpatients. Persons aged 65 years and older visiting a primary care medical practice in Baltimore (n=240) were approached. Complete baseline data were obtained for 54 patients and 34 patients completed 1-week retest follow-up. Standard instruments were administered by computer and also given as paper and pencil tests. Test-retest reliability estimates were calculated and comparisons across mode of administration were made. Separately, an interviewer administered a questionnaire to gauge patient attitudes and feelings after using the computer. Most participants (72%) reported no previous computer use. Nevertheless, inter-method reliability of the GDS15 at baseline (0.719, n=47), intra-method reliability of the computer in time (0.797, n=31), inter-method reliability of the CESDR20 at baseline (0.740, n=53), and the correlation between the CESDR20 computer version at baseline and follow-up (0.849, n=34) were all excellent. The inter-method reliability of the CESDR20 at follow-up (0.615, n=37) was lower but still acceptable. Although 28% were anxious prior to using the computer testing system, that percent decreased to 19% while using the system. The efficiency and reliability in comparison to the paper instruments were good or better. Even though most participants had not ever used a computer prior to participating in the study, they had generally favorable attitudes toward the use of computers, and also reported having favorable experience with the computer testing system.     

Formation polarity dependent improved resistive switching memory characteristics using nanoscale (1.3 nm) core-shell IrOx nano-dots

ABSTRACT: Improved resistive switching memory characteristics by controlling the formation polarity in an IrOx/Al2O3/IrOx-ND/Al2O3/WOx/W structure have been investigated. High density of 1 × 1013/cm2 and small size of 1.3 nm in diameter of the IrOx nano-dots (NDs) have been observed by high-resolution transmission electron microscopy. The IrOx-NDs, Al2O3, and WOx layers are confirmed by X-ray photo-electron spectroscopy. Capacitance-voltage hysteresis characteristics show higher charge-trapping density in the IrOx-ND memory as compared to the pure Al2O3 devices. This suggests that the IrOx-ND device has more defect sites than that of the pure Al2O3 devices. Stable resistive switching characteristics under positive formation polarity on the IrOx electrode are observed, and the conducting filament is controlled by oxygen ion migration toward the Al2O3/IrOx top electrode interface. The switching mechanism is explained schematically based on our resistive switching parameters. The resistive switching random access memory (ReRAM) devices under positive formation polarity have an applicable resistance ratio of > 10 after extrapolation of 10 years data retention at 85°C and a long read endurance of 105 cycles. A large memory size of > 60 Tbit/sq in. can be realized in future for ReRAM device application. This study is not only important for improving the resistive switching memory performance but also help design other nanoscale high-density nonvolatile memory in future.     

Machine learning for molecular thermodynamics

Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes. It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems. Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions. This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples. The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data. The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials. The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations. The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering. We will also discuss the perspective of the broad applications of machine learning in chemical engineering.     

High-resolution optical chirped pulse gating

We describe a system for achieving high-resolution range gating using optically chirped pulses. The technique converts signals from the time domain into signals in the frequency domain through a nonlinear, sum-frequency generation process. The technique is based on similar methods used in microwave radar. We draw analogies between our method and conventional and time-lens imaging processes, and present experimental results demonstrating the method.