The effect of sub-threshold forces on human performance in multi-modal computer-aided design

In traditional CAD (computer-aided design) systems, the manipulation of points and lines is often difficult because designers manipulate virtual objects through their vision system. Nowadays, designers can explore and manipulate virtual objects in haptic-enabled CAD systems using haptic devices. Haptic devices can present force feedbacks to pull or push the users’ hands into desirable targets. Of course the intent is for the user to experience the same sensations in the virtual realm as they would in the real world. Thus, sub-threshold forces, which cannot be perceived by users, should be incorporated in the control of users’ movements. As a result, our attention is directed to study the effect of sub-threshold forces on the accuracy of movement in a haptic-enabled virtual reality (VR) system. In this study, our goal is to manipulate users’ hands using controlled forces such that users cannot notice the forces. With this in mind, we have constructed a haptic-enabled virtual environment (VE) to carry out a multi-modal Fitts’ type task. In the task, subjects could see the position of the haptic probe in the VE where forces were applied on their hands. Basically, the accuracy of subjects was measured using a performance index when the intensity and direction of forces changed. A psychophysical method was utilized to ensure that the forces were below the force threshold of the human force perception. Results indicate that the accuracy is affected by the intensity and direction of sub-threshold forces even when users are allowed to control their actions through visual feedbacks.     

便携式安全阀在线校验仪的研制

目前,安全阀在线校验系统普遍存在精度低、安全性和便携性差等缺点.提出了以STC12C5A单片机为核心,基于阀瓣微动技术的便携式安全阀自动校验仪的设计方案,并完成了对该压力校准系统的研制.工程应用表明,该仪器具有精度高、功耗低等特点,能实现数据共享,操作灵活,易于携带,在行业中具有很高的推广价值.     

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.     

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.     

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.     

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。     

Efficient data dissemination using locale covers

Location-dependent data are central to many emerging applications, ranging from traffic information services to sensor networks. The standard pull- and push-based data dissemination models become unworkable since the data volumes and number of clients are high.We address this problem using locale covers, a subset of the original set of locations of interest, chosen to include at least one location in a suitably defined neighborhood of any client. Since location-dependent values are highly correlated with location, a query can be answered using a location close to the query point. Typical closeness measures might be Euclidean distance, or a $k$-nearest neighbor criterion.We show that location-dependent queries may be answered satisfactorily using locale covers. Our approach is independent of locations and speeds of clients, and is applicable to mobile clients.We also introduce a nested locale cover scheme that ensures fair access latencies, and allows clients to refine the accuracy of their information over time. We also prove two important results: one regarding the greedy algorithm for sensor covers and the other pertaining to randomized locale covers for $k$-nearest neighbor queries.     

流量仪表等精度传递的实验研究和准确度分析

采用实验研究的方式,讨论了用同等级的流量仪表作为标准表检定其它流量计的可能性与可行性,提出了等精度传递的概念,分析了等精度传递系统中影响传递精度的因素。试验表明,只要仪表具有足够好的重复性,用其作为传递标准组建标准表法流量标准装置,可以使装置的准确度接近上一级流量标准的准确度。     

Geometric Hermite interpolation for space curves

This paper considers the geometric Hermite interpolation for spacial curves by parametric quartic Bézier curve. In additon to position and tangent direction, the curvature vector is prescribed at each knot. We prove that under appropriate assumptions the interpolant exists locally with one degree of freedom. Moreover, we prove the interpolant is 6th order accurate.