基于C8051的光缆性能试验机的设计

设计了一种基于C8051F340微处理器的新型光缆性能试验机的控制系统。针对传统试验机的不足,简化了设计方案、提高了采集数据的精度、改进了控制台的设计和数据转递的方式、增加了语音告警和提示功能。该方案能广泛应用于数据采集、工业监控和网络传输等领域。对硬件和软件设计中的关键技术进行了说明。     

Dominance-based rough set approach and knowledge reductions in incomplete ordered information system

Many methods based on the rough set to deal with incomplete information systems have been proposed in recent years. However, they are only suitable for the incomplete systems with regular attributes whose domains are not preference-ordered. This paper thus attempts to present research focusing on a complex incomplete information system—the incomplete ordered information system. In such incomplete information systems, all attributes are considered as criterions. A criterion indicates an attribute with preference-ordered domain. To conduct classification analysis in the incomplete ordered information system, the concept of similarity dominance relation is first proposed. Two types of knowledge reductions are then formed for preserving two different notions of similarity dominance relations. With introduction of the approximate distribution reduct into the incomplete ordered decision system, the judgment theorems and discernibility matrixes associated with four novel approximate distribution reducts are obtained. A numerical example is employed to substantiate the conceptual arguments.     

A model for describing and composing direction relations between overlapping and contained regions

Current models of direction relations are not designed to describe direction information inside the Minimum Bounding Rectangle (MBR) of a reference region. Thus, the direction relations between overlapping and contained regions cannot be effectively described and derived. To resolve this problem, a new model of direction relations, namely interior-boundary direction relations, is proposed in this study to describe direction concepts relative to the interior or boundary of a region, such as “east part of a region”, “west border of a region”, “a line goes through east part of a region”, and etc. By combining the interior and exterior direction relations, three types of compositions of direction relations are investigated: (1) composing two interior direction relations which can be used to derive the interior or exterior direction relations between two regions with the same parent region; (2) composing an interior direction relation with an exterior direction relation; and (3) composing an interior with an exterior direction relation. The results indicate that the new interior-boundary direction relations and its compositions with exterior direction relations are powerful in describing and deriving direction relations between overlapped and contained regions.     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

Semi-parametric nonlinear regression and transformation using functional networks

Functional networks are used to solve some nonlinear regression problems. One particular problem is how to find the optimal transformations of the response and/or the explanatory variables and obtain the best possible functional relation between the response and predictor variables. After a brief introduction to functional networks, two specific transformation models based on functional networks are proposed. Unlike in neural networks, where the selection of the network topology is arbitrary, the selection of the initial topology of a functional network is problem driven. This important feature of functional networks is illustrated for each of the two proposed models. An equivalent, but simpler network may be obtained from the initial topology using functional equations. The resultant model is then checked for uniqueness of representation. When the functions specified by the transformations are unknown in form, families of linear independent functions are used as approximations. Two different parametric criteria are used for learning these functions: the constrained least squares and the maximum canonical correlation. Model selection criteria are used to avoid the problem of overfitting. Finally, performance of the proposed method are assessed and compared to other methods using a simulation study as well as several real-life data.     

Multilayer hybrid visualizations to support 3D GIS

In this paper, we present a unique hybrid visualization system for spatial data. Although some existing 3D GIS systems offer 2D views they are typically isolated from the 3D view in that they are presented in a separate window. Our system is a novel hybrid 2D/3D approach that seamlessly integrates 2D and 3D views of the same data. In our interface, multiple layers of information are continuously transformed between the 2D and 3D modes under the control of the user, directly over a base terrain. In this way, our prototype system is able to depict 2D and 3D views within the same window. This has advantages, since 2D and 3D visualizations can each be easier to interpret in different contexts.In this work we develop this concept of a hybrid visualization by presenting a comprehensive set of capabilities within our distinctive system. These include new facilities such as: hybrid landmark, 3D point, and chart layers, the grouping of multiple hybrid layers, layer painting, the merging of layer controls and consistent zooming functionality.     

基于最大互信息指标的对偶控制研究

针对参数未知但恒定的随机系统, 研究了基于最大互信息指标的对偶控制. 运用了Kalman滤波器估计随机系统未知参数的方法; 研究了最大互信息指标所具有的对偶特性, 即跟踪理想的目标以及探测未知参数的不确定性; 采用了两级优化算法获得次优对偶控制律. 算例验证了此算法的有效性和可行性.     

Technology-assisted learning: a longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning

Abstract   A field experiment compares the effectiveness and satisfaction associated with technology-assisted learning with that of face-to-face learning. The empirical evidence suggests that technology-assisted learning effectiveness depends on the target knowledge category. Building on Kolb's experiential learning model, we show that technology-assisted learning improves students' acquisition of knowledge that demands abstract conceptualization and reflective observation but adversely affects their ability to obtain knowledge that requires concrete experience. Technology-assisted learning better supports vocabulary learning than face-to-face learning but is comparatively less effective in developing listening comprehension skills. In addition, according to empirical tests, perceived ease of learning and learning community support significantly predict both perceived learning effectiveness and learning satisfaction. Overall, the results support our hypotheses and research model and suggest instructors should consider the target knowledge when considering technology-assisted learning options or designing a Web-based course. In addition, a supportive learning community can make technology-assisted learning easier for students and increase their learning satisfaction.     

Scanning Depth of Route Panorama Based on Stationary Blur

This work achieves an efficient acquisition of scenes and their depths along long streets. A camera is mounted on a vehicle moving along a straight or a mildly curved path and a sampling line properly set in the camera frame scans the 1D images over scenes continuously to form a 2D route panorama. This paper proposes a method to estimate the depth from the camera path by analyzing a phenomenon called stationary blur in the route panorama. This temporal blur is a perspective effect in parallel projection yielded from the sampling slit with a physical width. We analyze the behavior of the stationary blur with respect to the scene depth, vehicle path, and camera properties. Based on that, we develop an adaptive filter to evaluate the degree of the blur for depth estimation, which avoids error-prone feature matching or tracking in capturing complex street scenes and facilitates real time sensing. The method also uses much less data than the structure from motion approach so that it can extend the sensing area significantly. The resulting route panorama with depth information is useful for urban visualization, monitoring, navigation, and modeling.     

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.