湿型球磨机槽型衬板工业性试验

通过改变衬板的结构形状来改善衬板的磨损状态，经工业性试验，不仅达到延长衬板使用寿命的目的，而且使磨机处理矿粒的综合性能指标得到有效改善。     

全液压电液模锻锤

就进油打击方式的全液压电液锤和排油打击方式的液气电液锤的特点进行了简单的对比分析。着重介绍了全液压电液模锻锤的原理、结构以及存在的技术问题和制造、使用中应重视的问题。为进一步提高电液锤的可靠性进行了探索。     

Building a Language‐Independent Discourse Parser using Universal Networking Language

Discourse parsing has become an inevitable task to process information in the natural language processing arena. Parsing complex discourse structures beyond the sentence level is a significant challenge. This article proposes a discourse parser that constructs rhetorical structure (RS) trees to identify such complex discourse structures. Unlike previous parsers that construct RS trees using lexical features, syntactic features and cue phrases, the proposed discourse parser constructs RS trees using high‐level semantic features inherited from the Universal Networking Language (UNL). The UNL also adds a language‐independent quality to the parser, because the UNL represents texts in a language‐independent manner. The parser uses a naive Bayes probabilistic classifier to label discourse relations. It has been tested using 500 Tamil‐language documents and the Rhetorical Structure Theory Discourse Treebank, which comprises 21 English‐language documents. The performance of the naive Bayes classifier has been compared with that of the support vector machine (SVM) classifier, which has been used in the earlier approaches to build a discourse parser. It is seen that the naive Bayes probabilistic classifier is better suited for discourse relation labeling when compared with the SVM classifier, in terms of training time, testing time, and accuracy.     

基于条件互信息和概率突跳机制的贝叶斯网络结构学习算法

贝叶斯网络分类器的精确构造是NP难问题,使用K2算法可以有效地缩减搜索空间,提高学习效率。然而K2算法需要初始的节点次序作为输入,这在缺少先验信息的情况下很难确定;另一方面,K2算法采用贪婪的搜索策略,容易陷入局部最优解。提出了一种基于条件互信息和概率突跳机制的贝叶斯网络结构学习算法(CMI-PK2算法),该算法首先利用条件互信息生成有效的节点次序作为K2算法的输入,然后利用概率突跳机制改进K2算法的搜索过程来提高算法的全局寻优能力,学习较为理想的网络结构。在两个基准网络Asia和Alarm上进行了实验验证,结果表明CMI-PK2算法具有更高的分类精度和数据拟合程度。     

Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka

Interpolating climatic variables such as rainfall is challenging due to the highly variable nature of meteorological processes, the effects of terrain and geography, and the difficulty in establishing a representative network of stations. While interpolation models are being adapted to include these effects, often the rainfall data contain significant gaps in coverage. In this paper, we evaluated rainfall data from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. We compared four spatial interpolation techniques: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka.     

Structure synthesis and statics analysis of a foldable stair

This paper proposes a foldable stair that can be deployed for use and folded for conservation and investigates the geometry and statics of the structure. It not only has the moving flexibility of a mechanism but also has the high stiffness and high strength of statically indeterminate structures. The primary linkage is a deployable scissor-like element which forms the staircase after expanding. This paper starts with the analysis of the geometry of planar four- and six-bar mechanisms and processes to synthesize the foldable stair and then investigates its statics. The paper further investigates redundant inner forces of every link based on the second Castigliano theorem, develops a foldable stair model, assembles and manufactures. Working experiments verify the theoretical results and the outstanding advantages over the general fixed stairs.     

Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

Boosted Bayesian network classifiers

The use of Bayesian networks for classification problems has received a significant amount of recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning. In this paper we present boosted Bayesian network classifiers, a framework to combine discriminative data-weighting with generative training of intermediate models. We show that boosted Bayesian network classifiers encompass the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal. We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers. On a large suite of benchmark data-sets, this approach outperforms generative graphical models such as naive Bayes and TAN in classification accuracy. Boosted Bayesian network classifiers have comparable or better performance in comparison to other discriminatively trained graphical models including ELR and BNC. Furthermore, boosted Bayesian networks require significantly less training time than the ELR and BNC algorithms.     

Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.     

Perceptual image preview

Image preview is a convenient way to browse large or multiple images on small displays. However, current signal-level image resampling algorithms may remove many features of interest in the preview image. In this paper, we propose perceptual image preview which retains more perceptual features such that users can inspect features of interest by viewing the preview image only and without zooming in. This technology has two components, structure enhancement and perceptual feature visualization. Structure enhancement enhances the image structure while suppressing subtle details using a gradient modulation method, thus making the succedent perceptual features more apparent. For perceptual feature visualization, features of interest detected in the picture is visualized on the structure enhanced preview image. We demonstrate with two examples of most commonly used image quality features, image blur and noise. The effectiveness of the proposed method is validated by experimental results.