茶藨属植物果实提取物及其制备工艺及其应用

本发明涉及一种茶藨属植物果实提取物及其制备工艺和应用；它是将青藏高原的茶藨属植物的果实干燥后粉碎，再用醚类溶剂浸泡提取，萃取得果实油脂；然后将果实油脂加醚并加KOH－CH3－OH溶液摇动后静置10－20分钟；将静置的溶液加水静置分层，取上层清液得提取物；本发明富含不饱和脂肪酸，含量在92％以上；其提取工艺简单、提取率高达98.5％；本发明的提取物可用于降血脂、抑制血小板凝聚、治疗高血脂和血栓病。

基于遗传退火算法的装配线设计多目标优化方法

针对混装配线设计这一有约束的多目标优化问题,建立了数学模型。将基于Pareto的解的分级方法与Lp-范数形式的非线性机制相组合,构建了基于遗传退火算法多目标优化方法。重点阐述了个体编码、染色体检修、多目标处理机制等关键技术。设计了算法流程图,并开发了优化程序。该方法克服了加权和方法的不足,用模拟退火改善了遗传算法全局寻优性能。计算实例表明,随着迭代次数的增加,每代的非受控点逐渐收敛于Pareto最优边界,是一种混装线设计多目标优化的新方法。

Model 1027连续测氡仪测量室内氡浓度若干问题的探讨

本文探讨Model 1027 连续测氡仪测量室内氡浓度的方法。用两台仪器对一房间内氡浓度在不同情况下进行近一个月的测量后,得出以下结论:仪器操作简便,测量结果稳定;仪器处于DISABLE(数字显示关闭) 状态所测氡浓度高于ENABLE(数字显示开启) 状态测量氡浓度;测量结果表达方式有两类,即氡浓度总平均值和美国环保局推荐平均值,多点比较测量应采用同一类氡浓度;仪器连续测量时间不得少于24h ,最好为48h 或更长;本次所测房间氡浓度平均值为9215 Bq/ m3 ,符合国家标准。

氮同位素示踪贵州红枫湖河流季节性氮污染

利用氮同位素技术对贵州红枫湖各输入、输出河流氮污染状况和季节性变化规律进行了研究,并通过对输入河流和输出河流的氮对比,探讨红枫湖的氮负荷变化。农业输入河流季节氮污染变化较小,以低硝酸盐、低铵盐含量为特征,其氮同位素组成较小,位于农业源范围之内( < + 10 ‰) 。工业污染河流氮污染呈干季和雨季变化:干季(冬春季) 以高硝酸盐、高铵盐含量和高氮同位素组成( > + 10 ‰) 为特征,雨季(夏季) 则相似于农业输入河流。因而利用氮同位素组成可以对不同类型河流氮污染源进行可靠识别。

准噶尔盆地构造动力学过程

准噶尔盆地被古生代缝合线构造包围，周缘山系向盆地方向逆冲推覆，显示了盆地处于长期受压的构造环境；火山喷发由海相转变为大陆相，火山活动由强到弱，表明二叠纪以后，上地幔物质一直处于收缩状态，盆地整体持续下沉接受沉积，地壳增厚。二叠纪进入盆地演化阶段之后，经历了晚海西、印支、燕山和喜马拉雅四期构造应力场的作用，发生了南北向拉张变形、南北向和北西向碰撞挤压变形、南北向和北西向张压交替变形以及南北向压扭冲断、走滑和重力滑覆变形，形成复杂多样构造类型。

Redução de perdas pós-colheita em batata para consumo.

2004

MB 03: clone de batata resistente à murcha bacteriana.

2004

Structural Saliency: The Detection of Globally Salient Structures Using a Locally Connected Network

Certain salient structures in images attract our immediate attention without requiring a systematic scan. We present a method for computing saliency by a simple iterative scheme, using a uniform network of locally connected processing elements. The network uses an optimization approach to produce a "saliency map," a representation of the image emphasizing salient locations. The main properties of the network are: (i) the computations are simple and local, (ii) globally salient structures emerge with a small number of iterations, and (iii) as a by-product of the computations, contours are smoothed and gaps are filled in.

Analysis of Differential and Matching Methods for Optical Flow

Several algorithms for optical flow are studied theoretically and experimentally. Differential and matching methods are examined; these two methods have differing domains of application- differential methods are best when displacements in the image are small (<2 pixels) while matching methods work well for moderate displacements but do not handle sub-pixel motions. Both types of optical flow algorithm can use either local or global constraints, such as spatial smoothness. Local matching and differential techniques and global differential techniques will be examined. Most algorithms for optical flow utilize weak assumptions on the local variation of the flow and on the variation of image brightness. Strengthening these assumptions improves the flow computation. The computational consequence of this is a need for larger spatial and temporal support. Global differential approaches can be extended to local (patchwise) differential methods and local differential methods using higher derivatives. Using larger support is valid when constraint on the local shape of the flow are satisfied. We show that a simple constraint on the local shape of the optical flow, that there is slow spatial variation in the image plane, is often satisfied. We show how local differential methods imply the constraints for related methods using higher derivatives. Experiments show the behavior of these optical flow methods on velocity fields which so not obey the assumptions. Implementation of these methods highlights the importance of numerical differentiation. Numerical approximation of derivatives require care, in two respects: first, it is important that the temporal and spatial derivatives be matched, because of the significant scale differences in space and time, and, second, the derivative estimates improve with larger support.

Estimating the Illuminant Color from the Shading of a Smooth Surface

\0\05{\0\0\0\0\0\0\0\0 a uniform wall illuminated by a spot light often gives a strong impression of the illuminant color. How can it be possible to know if it is a white wall illuminated by yellow light or a yellow wall illuminated by white light? If the wall is a Lambertian reflector, it would not be possible to tell the difference. However, in the real world, some amount of specular reflection is almost always present. In this memo, it is shown that the computation is possible in most practical cases.

Message-Driven Processor Architecture: Verson 11

The Message-Driven Processor is a node of a large-scale multiprocessor being developed by the Concurrent VLSI Architecture Group. It is intended to support fine-grained, message passing, parallel computation. It contains several novel architectural features, such as a low-latency network interface, extensive type-checking hardware, and on-chip memory that can be used as an associative lookup table. This document is a programmer's guide to the MDP. It describes the processor's register architecture, instruction set, and the data types supported by the processor. It also details the MDP's message sending and exception handling facilities.

An Operating Environment for the Jellybean Machine

The Jellybean Machine is a scalable MIMD concurrent processor consisting of special purpose RISC processors loosely coupled into a low latency network. I have developed an operating system to provide the supportive environment required to efficiently coordinate the collective power of the distributed processing elements. The system services are developed in detail, and may be of interest to other designers of fine grain, distributed memory processing networks.

Parallel Networks for Machine Vision

The amount of computation required to solve many early vision problems is prodigious, and so it has long been thought that systems that operate in a reasonable amount of time will only become feasible when parallel systems become available. Such systems now exist in digital form, but most are large and expensive. These machines constitute an invaluable test-bed for the development of new algorithms, but they can probably not be scaled down rapidly in both physical size and cost, despite continued advances in semiconductor technology and machine architecture. Simple analog networks can perform interesting computations, as has been known for a long time. We have reached the point where it is feasible to experiment with implementation of these ideas in VLSI form, particularly if we focus on networks composed of locally interconnected passive elements, linear amplifiers, and simple nonlinear components. While there have been excursions into the development of ideas in this area since the very beginnings of work on machine vision, much work remains to be done. Progress will depend on careful attention to matching of the capabilities of simple networks to the needs of early vision. Note that this is not at all intended to be anything like a review of the field, but merely a collection of some ideas that seem to be interesting.

An Optimal Scale for Edge Detection

Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter $\\ lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\\lambda $ is derived from regularization analysis for the case of small values of $\\lambda$. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.

A Robot that Walks: Emergent Behaviors from a Carefully Evolved Network

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical problems have been ignored.