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Inertial sensors such as gyroscopes and accelerometers are normally considered as the most important sensors in a navigation system. Especially in the underwater or under-ice applications, the accuracy of the entire navigation system has to mainly rely on the precision of the inertial sensors due to inapplicability of global positioning systems. For MEMS-based inertial sensors, fabrication variation and environmental disturbance are among the major error sources. To address these challenges, in this paper we propose an optimization methodology by using parametric analysis on a reference design for improving sensor reliability and resolution. Apart from studying the resolution improvement by deploying an alternative sensing scheme, the effects of changing location, shape, and size of critical cantilevers have been thoroughly explored. By using this method, we have derived an improved mechanical structure for tuning-fork gyroscopes. Our numerical analyses show that the bandwidth of the proposed structure, which is the most important stability measure in the vibratory gyroscopes with slightly mismatched resonant frequencies, is over 1.7 times more immune to fabrication imperfection than the other structural alternatives. The drive and sense resonant amplitude robustness against fabrication imperfection is also improved in the proposed structure. In addition, this structure is able to provide at least 2.3 times larger sense-mode capacitance response to external rotation compared to the previously published designs. More important, it is observed that there is always non-negligible room for improving performance of gyroscopes if our proposed structural optimization methodology is integrated into the conventional MEMS-based inertial sensor design flow. 相似文献
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Use of a dark object concept and support vector machines to automate forest cover change analysis 总被引:5,自引:0,他引:5
Chengquan Huang Kuan Song Sunghee Kim Paul Davis Jeffrey G. Masek 《Remote sensing of environment》2008,112(3):970-985
An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas. 相似文献
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A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS 总被引:14,自引:0,他引:14
Thomas Hilker Michael A. Wulder Julia Linke Jeffrey G. Masek Joanne C. White 《Remote sensing of environment》2009,113(8):1613-1627
Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques. 相似文献
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Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina 总被引:1,自引:0,他引:1
Katelyn A. Dolan George C. Hurtt Jeffrey Q. Chambers Ralph O. Dubayah Jeffrey G. Masek 《Remote sensing of environment》2011,115(1):86-96
In 2005, hurricane Katrina resulted in a large disturbance to U.S. forests. Recent estimates of damage from hurricane Katrina have relied primarily on optical remote sensing and field data. This paper is the first large-scale study to use satellite-based lidar data to quantify changes in forest structure from that event. GLAS data for the years prior to and following hurricane Katrina were compared to wind speed, forest cover, and damage data to assess the adequacy of sensor sampling, and to estimate changes in Mean Canopy Height (MCH) over all areas that experienced tropical force winds and greater. Statistically significant decreases in MCH post-Katrina were found to increase with wind intensity: Tropical Storm ?MCH = − 0.5 m, Category 1 ?MCH = − 2 m, and Category 2 ?MCH = − 4 m. A strong relationship was also found between changes in non-photosynthetic vegetation (?NPV), a metric previously shown to be related to storm damage, and post-storm MCH. The season of data acquisition was shown to influence calculations of MCH and MCH loss, but did not preclude the detection of major large-scale patterns of damage. Results from this study show promise for using space-borne lidar for large-scale assessments of forest disturbance, and highlight the need for future data on vegetation structure from space. 相似文献
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Abhijit Gosavi Susan L. Murray Christopher M. Masek 《Engineering Management Journal; EMJ》2013,25(3):134-144
AbstractGiven that healthcare costs in the U.S. continue to climb, engineering managers can help address this situation by identifying designs for cost savings. Using discrete-event simulation, the layout of four different healthcare clinic scenarios considered in a redesign program named Patient Aligned Care Teams were studied. Model 1 is an existing layout in many clinics and Model 2 is staff-centered, while Models 3 and 4 provide additional space including common hallway(s). Our main conclusion is that Model 4 reduces patient travel distances and average queueing waits, while also providing the best privacy in accordance with HIPAA and the most security for the staff. 相似文献