Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.     

Linear mixture model applied to Amazonian vegetation classification

Many research projects require accurate delineation of different secondary succession (SS) stages over large regions/subregions of the Amazon basin. However, the complexity of vegetation stand structure, abundant vegetation species, and the smooth transition between different SS stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier (MLC). Most of the time, classification distinguishes only between forest/non-forest. It has been difficult to accurately distinguish stages of SS. In this paper, a linear mixture model (LMM) approach is applied to classify successional and mature forests using Thematic Mapper (TM) imagery in the Rondônia region of the Brazilian Amazon. Three endmembers (i.e., shade, soil, and green vegetation or GV) were identified based on the image itself and a constrained least-squares solution was used to unmix the image. This study indicates that the LMM approach is a promising method for distinguishing successional and mature forests in the Amazon basin using TM data. It improved vegetation classification accuracy over that of the MLC. Initial, intermediate, and advanced successional and mature forests were classified with overall accuracy of 78.2% using a threshold method on the ratio of shade to GV fractions, a 7.4% increase over the MLC. The GV and shade fractions are sensitive to the change of vegetation stand structures and better capture biophysical structure information.     

Density measurements on levitated liquid metal droplets

Electromagnetic levitation is a useful tool for measuring thermophysical properties of high-temperature melts such as liquid metals. Due to its noncontact nature, the undercooled regime is also accessible. Density and thermal expansion of a levitated drop can be derived from volume measurements, if its mass is known. Assuming cylindrical symmetry, the volume of a drop can be determined from its cross section. Using photography, such measurements on liquid metals have been performed in the past. Here we present an improvement of this method, which replaces the photographic camera with a CCD videocamera and subsequent digital image processing. This reduces the time effect required to obtain the results and allows one to average over disturbing surface oscillations. The specific problems of digital image processing, namely resolution and edge detection, are addressed and results on nickel are presented.Paper presented at the Fourth International Workshop on Subsecond Thermophysics, June 27–29, 1991, Köln, Germany.     

扫描近场光学显微镜及其在生物微结构探测上的应用

扫描近场光学显微镜（ＳＮＯＭ）可探测生物体亚波长水平的光学特性，以接近分子水平的分辨率在生物微结构成象、探测和修饰等多方面有巨大的潜力。本文对ＳＮＯＭ的技术方法及其近期的的生物学应用进行了介绍。     

“空中B超”——遥感影像用于考古

结合全球卫星定位系统GPS,遥感成像技术能提供大量对考古研究极为有价值的田野影像信息。结合不同年代拍摄的航空照片,考古学家从这些遥感影像中获得大量信息,并得以在近些年来取得很多举世瞩目的成果。本文列举了一系列典型事例,也披露了我国一些古迹的演变情况。     

基于公共存储器进行数据交换的高速数字图象处理系统

本文介绍了一种基于公共存储器进行数据交换的高速数字图象处理系统．给出了公共存储器的设计，描述了处理器对公共存储器的进程同步控制．     

高灵敏度荧光显微镜及其在细胞光敏效应研究中的应用

研制了一种可探测极微弱细胞荧光图象的高灵敏度荧光显微镜，在仪器中使用了一个高质量的亮度增益为５０，０００的象增强器对细胞的荧光图象进行光能量放大，因而这种荧光显微镜可探测到普通荧光显微镜不能观察的微弱荧光图象。由于仪器中光源的输出强度很小，避免了长时间观察对活细胞的光损伤，并可观察到荧光效率低的荧光物质的分布图象。探测系统中用计算机采集图象数据，同时对数据进行必要处理，以提高图象的质量。这种高灵敏度的荧光显微镜可用于分析细胞中荧光物质的分布，以及细胞动态生理过程的研究。使用此仪器已直接观察到光敏竹红菌甲素在Ｈｅｌａ细胞（人体宫颈癌细胞）中的分布，细胞受光照射后的光损伤现象及加入三种细胞保护剂后获得不同程度保护的细胞状态。     

同心球所夹空间电场的电像法求解

采用电像法分析研究了二同心导体球所夹空间的静电场分布。     

Technical Note: Selecting a Classification Method by Cross-Validation

If we lack relevant problem-specific knowledge, cross-validation methods may be used to select a classification method empirically. We examine this idea here to show in what senses cross-validation does and does not solve the selection problem. As illustrated empirically, cross-validation may lead to higher average performance than application of any single classification strategy, and it also cuts the risk of poor performance. On the other hand, cross-validation is no more or less a form of bias than simpler strategies, and applying it appropriately ultimately depends in the same way on prior knowledge. In fact, cross-validation may be seen as a way of applying partial information about the applicability of alternative classification strategies.     

形态学边缘检测的新算法及其应用

提出了一种基于数学形态学的边缘检测的所算法，该算法具有简便和灵活可编程控制等优点，比原来主算法更国完善，在实际应用中有着很好的前景，并给出了实验结果。