红外化学遥感数字信号处理算法的研究进展

介绍了几种为远距离红外蒸汽信号监测开发的分类器设计，频域数字滤波，时域数字滤波等数字信号处理技术。对线性分类器，分段线性分类器和神经网络分类器等在红外化学遥感系统中的应用做了初步的探讨。对背景扣除技术，频域数字滤波技术，数字滤波的切趾方法等做了简要的描述。阐述了时域滤波技术的基本原理及实际应用。展望了红外化学遥感数字信号处理算法的研究前景。     

压电类智能层合结构的力学和计算模型综述

综述了压电类智能层合结构的力学和计算模型。基于大量国内外有关压电类智能层合结构的文献 ,根据各自采用的运动学假设、场变量的近似、压电层的表达法以及曲率和温度的影响 ,对压电层合结构的力学和计算模型进行了分类 ,阐述了各种理论的特点及一般形式。     

湿敏半导体材料电导率计算方法的研究

结合修正了的耗尽层理论和多孔湿敏半导体的特点，本文提出了一个简单的物理模型作为ｎ型湿敏半导体特征的一级近似。描述了多孔湿敏半导体的导电机理，并给出了湿度与宏观电导率的关系。     

Electronic Sensing of Aromatic Volatiles for Quality Sorting of Blueberries

An electronic sniffer using semi-conductor gas sensors that nondestructively measured aromatic volatile gas emissions from fruit was developed to assess blueberry quality. The sniffer detected soft and damaged fruit in packaged containers at a 5% level of damage and distinguished four of five fruit ripeness classes: (1) mature-green and green-pink; (2) blue-pink; (3) blue; and (4) ripe fruit. Sniffer response increased as fruit ripened, as did total concentration of aromatic volatiles. Sniffer response correlated with berry firmness, pH, titratable acidity, and color, and detected differences among 10 cultivars, as did impact response analysis. The electronic sniffer is rapid, nondestructive and may be used to sort and quality check for presence of unripe or damaged fruit in closed packs of fresh berries.     

软测量的工程化设计

将软测量应用到工业生产之前必须解决其工程化设计的问题.为此,在软测量技术理论研究和应用经验的基础上,对软测量工程化设计中应解决的关键问题和解决方法进行了研究.软测量的工程化设计包括机理分析、辅助变量选择、数据采集和预处理、建立软测量模型、设计校正模块等步骤.其关键技术在于软测量过程的数据处理、模型的建立和校正.并以杭州龙山化工厂碳酸化塔中C02浓度软测量的工程化设计为例,对工程化设计中的一些关键问题处理进行介绍.实践表明,软测量的工程化设计在实际工业生产中具有很好的应用性和指导性,但也有待更进一步的研究.     

Influence of residual milk-clotting enzyme and proteolysis on melting properties of soft cheese

In this work, we assessed the influence of coagulant residual activity and primary proteolysis on Cremoso Argentino cheese melting properties. For that purpose, we made Cremoso soft cheeses using different amounts of coagulant, and also obtained samples in which milk-clotting enzyme was inactivated. Primary proteolysis correlated with residual activity of coagulant in early stages of cheese ripening; however, it was similar in all cheeses after 30 days. The hydrolysis of caseins did not significantly affect the melting ability of the cheeses, expressed as the area increase after heating samples under standardized conditions. Samples with similar proximate composition showed some changes in meltability; those seemed related to pH evolution during ripening.     

Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

湖泊藻类叶绿素-α和悬浮物浓度的高光谱定量遥感模型研究

本文利用地物光谱仪对太湖水体进行了光谱测量和同步采样分析，对获得的数据尝试用光谱分离法进行分析，从中分离出蓝藻和悬浮物的特征波峰，建立波峰高度与同步水质采样得到的叶绿素-α浓度和悬浮物浓度的对应关系，得出其遥感定量反演算法，并实际应用在OMIS成像光谱仪图像上．     

基于组织协同进化分类算法的遥感图像目标识别

针对遥感图像目标识别问题,提出了一种基于组织协同进化分类算法的识别方法。它没有复杂的运算,训练和识别速度都很快。对实测遥感图像的实验表明,本文方法性能稳定,优于文献[3]和[4]中基于支撑矢量机的方法,识别率均达到了95％以上,且训练时间非常短,不到1秒钟。     

Knowledge-based diagnosis of drill conditions

One major bottleneck in the automation of the drilling process by robots in the aerospace industry is drill condition monitoring. This paper describes a system approach to solve this problem through the advancement of new machine design, sensor instrumentation, metal-cutting research, and intelligent software development. All drill failures can be detected and distinguished: chisel edge wear, flank wear, crater wear, margin wear, corner wear, breakage, asymmetry, lip height difference, and chipping at lips. However, in the real manufacturing environment, different workpiece materials, drill size, drill geometry, drill material, cutting speed, feed rate, etc. will change the criteria for judging the drill condition. The knowledge base used for diagnosing the drill failures requires a huge data bank and prior exhaustive testing. A self-learning scheme is therefore introduced to the machine in order to acquire the threshold history needed for automatic diagnosis by using the same new tool under the same drilling conditions.