Sensor adaptive fault tolerant control for non-linear processes

Adaptive fault tolerant control of non-linear processes is an open problem. In this paper, on the basis of a strong tracking filter (STF), an approach to sensor adaptive fault tolerant generic model control for non-linear processes is proposed. When the process runs normally, Adaptive Generic Model Control (AGMC) based on parameter estimation is used to control non-linear time-varying processes. A sensor fault model is set up by introducing a bias vector into the output equation of the process. The bias vector is estimated on-line based on the STF during every control period. With the estimated sensor bias vector and the time-varying parameters, a fault detection mechanism is developed to supervise sensors. When a sensor fault is detected, AGMC will be switched to a state estimation and soft-sensor-based GMC. This strategy constitutes a sensor-adaptive fault tolerant generic model control for non-linear processes. Experimental results on a three-tank system demonstrate the effectiveness of the proposed approach.     

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data

In this article, Landsat TM images acquired during the same season from both 1984 and 1997 were analysed for urban built-up land change detection in Beijing, China, where great changes have taken place during the recent decades. To reduce the spectral confusion between urban 'built-up' and rural 'non built-up' land cover categories, we propose a new structural method based on road density combined with spectral bands for change detection. The road density represents one type of structural information while the multiple Landsat TM bands represent spectral information. Road density maps for both dates were produced using a gradient direction profile analysis (GDPA) algorithm and then integrated with spectral bands. Results from the spectral-structural postclassification comparison (SSPCC) and spectral-structural image differencing (SSID) methods were evaluated and compared with spectral-only change detection methods. The proposed SSPCC method greatly reduced spectral confusion and increased the accuracy of land cover classification compared with spectral classification, which in turn improved the change detection results. This article also shows that the SSID change detection result complemented spectral band differencing by detecting areas with greater structural changes, some of which were missed, by spectral band differencing.     

Mapping snow cover in the pan-Arctic zone,using multi-year (1998-2001) images from optical VEGETATION sensor

Timely information on spatial distribution and temporal dynamics of snow cover in the pan-Arctic zone is needed, as snow cover plays an important role in climate, hydrology and ecological processes. Here we report estimates of snow cover in the pan-Arctic zone (north of 45° N) at 1-km spatial resolution and at a 10-day temporal interval over the period of April 1998 to December 2001, using 10-day composite images of VEGETATION sensor onboard Système Pour l'Observation de la Terre (SPOT)-4 satellite. The results show that snow covered area (SCA) in North America (north of 45° N) increased from 1998 to 2001, while SCA in Eurasia (north of 45° N) decreased from 1998 to 2000 but increased in 2001. There were large spatial and temporal variations of snow cover in the pan-Arctic zone during 1998-2001.     

Bayesian Multi‐net Classifier for classification of remote sensing data

The purpose of this paper is to investigate the applicability of Bayesian Multi‐net Classifier (BMC) to classify remote sensing data. BMC is based on Bayesian Network (BN), which is a graphical model encoding probabilistic relationships among variables of interest. Different from the BNC that has a mere network, a BMC has as many local Bayesian Networks as the predefined classes, which means that the probabilistic relationships among the features can be different for different classes. Classification is done by computing the probability of the class, given the particular instance of the features, and then predicting the class with the highest posterior probability. This method was validated using a Landsat ETM+ image of Beijing acquired on 1 May 2003. Based on the confusion matrix, overall accuracy, Kappa statistic, total normalized probability of misclassification (TNPM), and McNemar's test, classification results of BMC were compared with those of MLC and BNC in the case study. The comparison results show that BMC performs slightly better than MLC and similar to BNC. The local Bayesian Networks of BMC can also lead to a better understanding of the dependencies between bands for different classes.     

A microwave vegetation water index from passive microwave radiometer data

The vegetation water content (VWC) index has been widely used in agriculture, forestry and hydrology studies. It is also useful in retrieving soil moisture from microwave remote sensing observations. Space‐borne and airborne microwave radiometers have widespread utility in soil moisture and vegetation condition retrieval. To simplify the original retrieval algorithm, this paper developed a theoretical microwave vegetation water index (MVWI) from microwave radiometer data, which contains only VWC and a vegetation structure parameter. Based on the MVWI, an efficient VWC retrieval algorithm was developed.     

Assembly operation process planning by mapping a virtual assembly simulation to real operation

Virtual assembly has been widely used in product development. However, virtual operation and actual operation are different in time and space, the simulation of interactive virtual assembly cannot support the assembly operation's process planning directly. In this paper, the solution for assembly operation's process planning is developed based on interactive virtual assembly. According to the solution, interactive assembly operation is used to obtain the actions of operation sequence. The actions are mapped into the data of a real operation action to obtain real operation actions. Then assembly operation cards can be obtained. To support the assembly operation actions obtained through virtual assembly simulation, a product assembly model is proposed. An operation semantic model is used to replace the geometric constraint model of assembly, which contains several ordered geometric constraints and some engineering restriction conditions. To test the solution and the models, one process planning example of an automobile engine is introduced. The results verified the feasibility and the effectiveness of the methods.     

Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China

In this paper, drought status of northwestern China is evaluated using the Terra–Moderate Resolution Imaging Spectroradiometer (MODIS) data with a newly developed method called perpendicular drought index (PDI), which is defined as a line segment that is parallel with the soil line and perpendicular to the normal line of soil line intersecting the coordinate origin in the two‐dimensional scatter plot of red against near infrared (NIR) wavelength reflectance. To validate the PDI in macroscale applications, quantitative evaluation of drought conditions in Ningxia, Northwestern China is carried out by comparing the PDI with one of the well‐known drought indexes, namely, temperature‐vegetation index (TVX). Linear regression between ground‐measured soil moisture data and the PDI and the TVX was made. Results show that satellite based PDI and TVX has significant correlation with 0–20 cm averaged soil moisture obtained over the meteorological observing stations across the whole study area. The highest correlation of R ² = 0.48 for the PDI and R ² = 0.40 for the TVX is obtained when compared with average soil moisture from 0 to 20 cm soil depth. According to the drought critical values defined by soil hydrologic parameters including soil moisture, wilting coefficient and field moisture capacity, the PDI based drought guidelines are established, and then the drought status in the study area is evaluated using the PDI. It is evident from the results showing the spatial distribution of drought in northwestern China that the PDI is highly accordant with field drought status.     

Boundary feedback exponential stabilization of a Timoshenko beam with both ends free

In the present paper we consider the boundary feedback stabilization of a Timoshenko beam with both ends free. We propose boundary feedback control law that makes the closed loop system dissipative. Using asymptotic analysis techniques, we give explicit asymptotic formula of eigenvalues of the closed loop system, and prove the Riesz basis property of eigenvectors and generalized eigenvectors. By a detailed analysis of spectrum of the closed loop system, we show that the closed system is exponentially stable.     

Optimal bilinear observers for bilinear state-space models by interaction matrices

This paper formulates optimal bilinear observers for bilinear state-space models. Observers in bilinear form, as opposed to other nonlinear forms, are required to develop an extension of observer/Kalman filter identification for simultaneous identification of a bilinear state-space model and an associated bilinear observer from noisy input–output measurements. The paper establishes the relationship between the bilinear observer gains and the interaction matrices which are used to convert the original bilinear state-space model to a form that simplifies the identification of such a model. Techniques to find the interaction matrices are developed. In the absence of noises, these matrices produce the gains of the fastest converging observer. In the presence of noises, they minimise the state estimation error in the same manner as a standard steady-state Kalman filter. Numerical examples illustrate both the theoretical and computational aspects of the proposed algorithms.