开环数控系统控制精度分析

分析了开环数控系统误差的来源,并提出了改善和提高精度的方法.     

锥面包络螺旋面上影象法成象点的分布规律及与测量精度的关系

建立了锥面包络螺旋面上影象法成象点的数学模型,证明了锥面包络螺旋面上的影象法成象点具有极高精度的共面性,定量分析了由于成象点不能同时共处于瞄准平面上而引入的原理误差及其表现形式,并且指出螺旋面上影象法成象点所具有的共面性在消除上述原理误差的研究中的重要意义。     

精确延时爆破条件下的边坡振动响应特性

精确延时条件下的爆破振动响应特性是当前工程爆破领域的重点研究内容。基于岩质边坡爆破振动响应的理论分析,结合溪洛渡水电站边坡开挖爆破振动的数值模拟,研究了精确延时条件下的岩质边坡爆破振动响应特性。结果表明,在边坡的爆源近区,质点峰值振动速度及第一主应力的变化随延时时间的不同具有明显的低峰值,而在爆源远区,则难以确定明确的变化规律。在爆源的近区,合理的延时时间可以降低甚至避免振动效应叠加,该延时时间的最小值约为爆破振动的主周期;爆源的远区,虽然振动峰值降低,但是难以避免爆破振动叠加效应。     

应用GPS授时时角拍星法标校测角精度

在用拍星法标校高精度测角设备过程中,针对通常采用天文授时存在的授时难、理论值误差大等问题,提出了应用 GPS 授时时角拍星法进行拍星标校。授时精度的提高和新时角算法的运用,大大提高了拍星标校的理论值精度,标校精度可达 5″以内,并在高精度测角设备上得以应用。     

弹道导弹的雷达探测系统仿真

该文根据弹道理论建立了雷则弹道导弹的仿真模型,并对雷达跟踪的导弹弹道数据进行了处理,预报了它的落点范围,并与理论落点进行比较,给出了落点精度。仿真结果表明：雷达对导弹的探测数据经过一数据处理,所得到的落点比较精确。     

Stochastic computer network with multiple terminals under total accuracy rate

From the viewpoint of service level agreements,data transmission accuracy is one of the critical performances for assessing Internet by service providers and enterprise customers.The stochastic computer network(SCN),in which each edge has several capacities and the accuracy rate,has multiple terminals.This paper is aimed mainly to evaluate the system reliability for an SCN,where system reliability is the probability that the demand can be fulfilled under the total accuracy rate.A minimal capacity vector allows the system to transmit demand to each terminal under the total accuracy rate.This study proposes an efficient algorithm to find all minimal capacity vectors by minimal paths.The system reliability can then be computed in terms of all minimal capacity vectors by the recursive sum of disjoint products(RSDP) algorithm.     

A dynamic over-sampling procedure based on sensitivity for multi-class problems

Classification with imbalanced datasets supposes a new challenge for researches in the framework of machine learning. This problem appears when the number of patterns that represents one of the classes of the dataset (usually the concept of interest) is much lower than in the remaining classes. Thus, the learning model must be adapted to this situation, which is very common in real applications. In this paper, a dynamic over-sampling procedure is proposed for improving the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a memetic algorithm (MA) that optimizes radial basis functions neural networks (RBFNNs). To handle class imbalance, the training data are resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to balance in part the size of the classes. Then, the MA is run and the data are over-sampled in different generations of the evolution, generating new patterns of the minimum sensitivity class (the class with the worst accuracy for the best RBFNN of the population). The methodology proposed is tested using 13 imbalanced benchmark classification datasets from well-known machine learning problems and one complex problem of microbial growth. It is compared to other neural network methods specifically designed for handling imbalanced data. These methods include different over-sampling procedures in the preprocessing stage, a threshold-moving method where the output threshold is moved toward inexpensive classes and ensembles approaches combining the models obtained with these techniques. The results show that our proposal is able to improve the sensitivity in the generalization set and obtains both a high accuracy level and a good classification level for each class.     

Satellite image-based maps: Scientific inference or pretty pictures?

The scientific method has been characterized as having two distinct components, Discovery and Justification. Discovery emphasizes ideas and creativity, focuses on conceiving hypotheses and constructing models, and is generally regarded as lacking a formal logic. Justification begins with the hypotheses and models and ends with a valid scientific inference. Unlike Discovery, Justification has a formal logic whose rules must be rigorously followed to produce valid scientific inferences. In particular, when inferences are based on sample data, the rules of the logic of Justification require assessments of bias and precision. Thus, satellite image-based maps that lack such assessments for parameters of populations depicted by the maps may be of little utility for scientific inference; essentially, they may be just pretty pictures. Probability- and model-based approaches are explained, illustrated, and compared for producing inferences for population parameters using a map depicting three land cover classes: non-forest, coniferous forest, and deciduous forest. The maps were constructed using forest inventory data and Landsat imagery. Although a multinomial logistic regression model was used to classify the imagery, the methods for assessing bias and precision can be used with any classification method. For probability-based approaches, the difference estimator was used, and for model-based inference, a bootstrap approach was used.     

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft, as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches.     

Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions

Regularly updated land cover information at continental or national scales is a requirement for various land management applications as well as biogeochemical and climate modeling exercises. However, monitoring or updating of map products with sufficient spatial detail is currently not widely practiced due to inadequate time-series coverage for most regions of the Earth. Classifications of coarser spatial resolution data can be automatically generated on an annual or finer time scale. However, discrete land cover classifications of such data cannot sufficiently quantify land surface heterogeneity or change. This study presents a methodology for continuous and discrete land cover mapping using moderate spatial resolution time series data sets. The method automatically selects sample data from higher spatial resolution maps and generates multiple decision trees. The leaves of decision trees are interpreted considering the sample distribution of all classes yielding class membership maps, which can be used as estimates for the diversity of classes in a coarse resolution cell. Results are demonstrated for the heterogeneous, small-patch landscape of Germany and the bio-climatically varying landscape of South Africa. Results have overall classification accuracies of 80%. A sensitivity analysis of individual modules of the classification process indicates the importance of appropriately chosen features, sample data balanced among classes, and an appropriate method to combine individual classifications. The comparison of classification results over several years not only indicates the method's consistency, but also its potential to detect land cover changes.