An improved feedrate scheduling method for NURBS interpolation in five-axis machining

Five-axis machining plays an important role in manufacturing by dint of its high efficiency and accuracy. While two rotation axes benefit the flexibility of machining, it also brings limitations and challenges. In order to further balance machining precision and efficiency, an improved feedrate scheduling method is presented considering geometric error and kinematic constraints for the Non Uniform Rational B-Spline (NURBS) interpolation in five-axis machining. A simplification method is proposed to calculate the geometric error which describes the deviation between the ideal tool path and the real tool path induced by the non-linear movement. A linear relation between geometric error and feedrate is built to limit the feedrate. The constraints determined by single axis kinematic performance and tangential kinematic performance are also considered. Under these constraints, a constrained feedrate profile is determined. Aiming to get more constant feedrate in the difficult-to-machine areas, this work proposes a scheduling method which combines morphological filtering and S-shape acceleration/deceleration (acc/dec) mode. Simulations and experiments are performed to compare the proposed feedrate scheduling method with two previous feedrate scheduling method and the results prove that the proposed feedrate scheduling method is reliable and effective.     

Analysis on the spatial ecological distribution model of landscape plant community

Because the spatial pattern of plant communities in garden landscapes usually exhibits highly non-random distribution characteristics, the accuracy of the analysis results is not high. For this purpose, a spatial ecological distribution model of landscape landform plant communities is designed. Based on the remote sensing images of landscape plant communities, the study area was obtained. The grassland communities in this area were classified and calculated by geostatistics, and the semi variogram values of typical samples of different vegetation types were obtained. According to the calculation results, the spatial terrain factors of landscape plant community are extracted, and the annual NDVI value is taken as the ecological vitality index of landscape green vegetation, the NDVI level is divided, the spatial ecological distribution model of landscape plant community is constructed, and the evaluation index system is generated, so as to complete the spatial ecological distribution model analysis of landscape plant community. The results show that the method of this study accurately analyzes the area of coniferous and broad-leaved mixed forest, mountain meadow, road, construction land, water area land and slope grade, which is slightly different from the actual value, indicating that the method has certain reliability.     

孪生网络中文语义匹配方法的研究

语义匹配是问答领域的一个核心任务,能够为问答系统和信息检索等领域提供技术支持.目前对于语义匹配这一特殊分类问题,神经网络主要使用交叉熵或者对比代价损失函数,忽略了损失函数的分类限制宽泛,导致其在分类边缘存在误差.为了解决此种问题,本文在已有的孪生神经网络的基础上,引入am-softmax损失函数,提升模型精确度,同时在现有的词向量和字向量作为网络输入的基础,进一步引入Attention机制,使模型进一步获取更多的文本信息.实验结果表明,与之前的深度学习模型相比,模型的性能有进一步提高.     

融合指针网络的新闻文本摘要模型

本文针对实际党建领域中的新闻标题进行自动生成,提出了一种融合指针网络的自动文本摘要模型-Tri-PCN.相比于传统基于编码器-解码器框架的自动文本摘要模型,党建新闻标题生成模型还需要满足(1)从更长的文本序列提取特征;(2)保留关键的党建信息.针对党建新闻比普通文本摘要任务面临更长文本序列问题,论文使用Transformer模型在解码阶段提取多层次全局文本特征.针对党建新闻标题生成过程中需要保留关键的党建信息,论文引入指针生成网络模型的复制机制在新闻标题生成时可以直接从新闻文本中复制关键词信息.实验采用ROUGE值作为评测指标,结果表明本文提出的Tri-PCN模型在党建新闻领域自动文本摘要任务上效果明显优于基准模型,比其他模型具有更好的效果.     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.     

Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.     

模糊逻辑在模拟演练系统智能体设计中的应用

根据模拟演练系统对智能体智能程度的要求为模拟演练系统添加模糊推理机,以增加演练系统的逼真程度及适应性. 本文以模糊逻辑理论为基础,以模拟演练系统为背景,对一个具体的应用实例进行数学建模,阐述了如何运用模糊逻辑来解决问题,并设计了模糊逻辑模块,通过应用实例演示验证了设计的可行性.     

组件技术在嵌入式系统中的应用

研究了提出了通用组件模型不能嵌入式系统中应用的本质原因,对嵌入式组件模型进行分类以及层次化分析. 并对于嵌入式系统的非功能属性进行分析,总结了常见嵌入式组件模型对这些非功能属性的支持情况. 然后介绍了组件库的设计,以及组件模型的未来研究方向.     

融合Cesium和Geoserver的地质数据形变监测可视化方法

随着地质研究与大数据的融合,形成了"多元异构、高容量、低价值密度的"海量地质数据.尤其在城市建设中反映地表和地面沉降情况的地质形变监测数据,具有容量大、时变性、维度复杂的特点.如何通过可视化技术更直观的服务于地质研究分析与问题决策,成为该领域数据可视化研究与应用热点.本文针对这一问题,通过干涉合成孔径雷达(Interferomeric Synthetic Aperture Radar,InSAR)采集的数据,提出一种在 Cesium 和 Geoserver 融合构建的Web三维场景下,展示地域沉降形变监测情况的可视化方法.在形变图层展示效果上,不同于Google Earth单一的渲染效果,设计出一种动态改变形变监测点云数据过渡颜色的方式.在可视化分析交互上与ENVI/SARscape进行对比.实践及应用结果表明,相对于传统方法如二维平面展示以及通过Google Earth导入数据的集成展示,本文方法对形变监测结果有更加直观的展示.同时拥有更加丰富的人机交互方式,为地质专业者提供更良好的辅助决策功能.