本体映射过程中概念相似度计算方法的改进

针对本体映射中概念相似度计算中存在的一些不足之处,提出了一种改进的方法。首先根据本体概念树的特点以及数据挖掘的思想,提出了一种改进的侯选映射集设置的方法,减少了相似度计算的工作量。进而根据本体和本体中概念的特点,综合概念名称、概念实例、概念的属性、结构、关系等因素。设计了一种改进的相似度的计算方法。改善了相似度计算中存在的片面性和不完善性问题,提高了本体映射的查全率和查准率。初步实验表明该算法在计算复杂度、查全率和查准率上都要优于Glue方法。     

灰度梯度映射函数改进的Otsu阈值法

针对目标与背景灰度分布不均匀的图像,基于集中于目标的图像阈值法思想,引入图像的灰度直方图信息,得到更为细致的阈值化准则。考虑图像的边缘信息,引入灰度梯度映射函数,提出了基于梯度的集中于目标的Otsu阈值法。大量经典图像阈值化结果表明,该方法在目标提取的完整性和边缘保留的清晰性方面,均表现出了更佳的效果。     

一种XML的树匹配改进方法

基于XML树的匹配已被广泛应用于数据挖掘、自然语言自处理、图像检索等领域。通过分析现有的基于XML树的匹配度计算方法,发现存在对计算的前期要求(如权值分割)太过严格、匹配度结果存在误差等问题,影响了匹配的精度和效率。基于XML的内容约束和结构约束,综合结点相似度和层次相似度,提出一种结构相似度计算公式,改进了匹配计算结果的准确度,并通过实验验证了公式的有效性。     

A super-resolution mapping method using local indicator variograms

Super-resolution mapping (SRM) is a recently developed research task in the field of remotely sensed information processing. It provides the ability to obtain land-cover maps at a finer scale using relatively low-resolution images. Existing algorithms based on indicator geostatistics and downscaling cokriging offer an SRM approach using spatial structure models derived from real data. In this article, a novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model. In the SLIV method, indicator variograms extracted from target-resolution classification are produced from a representative local area as opposed to using the entire image. This simplifies the application of the method since limited target-resolution reference data are required. Our investigation on three diverse case studies shows that the local window (approximately 2% of the entire study area) selection process offers comparable accuracy results to those using globally derived spatial structures, indicating our methodology to be a promising practice. Furthermore, comparison of the proposed method with random realizations indicates an improvement of 7–12% in terms of overall accuracy and 15–18% in terms of the kappa coefficient. The evaluation focused on a 270–30 m pixel size reconstruction as a potential popular application, for example moving from Moderate Resolution Imaging Spectroradiometer (MODIS) to Landsat-type resolutions.     

一种改进的指数核时频表示*

针对指数分布只适用于模糊函数自分量分布沿ξ和τ轴附近的信号问题,通过将指数核函数进行坐标旋转,从而实现让自模糊函数项尽可能地全部通过指数核函数,而互模糊函数项尽可能地远离该指数核函数。解决了指数分布在分析模糊域中不沿ξ和τ轴附近分布的信号时时频分布效果差的问题,并以Chirp信号为例进行了仿真,其结果证明该方法算法简单、易于实现,为抑制指数核的交叉项问题提供了另一种思路。     

一种基于空间分割的无源RFID室内定位方法*

为了提高室内定位系统的性能,提出了一种利用无源RFID技术定位目标物体的算法。本算法使用双层对称分布的参考标签和空间连续分割的方法提高了室内定位的精度。实际测试结果表明,本算法可较好地实现目标的三维空间定位,与传统LANDMARC定位系统相比,单个平面定位精度提升了8倍,适合于室内人员、物资等定位应用。     

改进的谱聚类图像分割方法

图像分割作为图像识别的一个重要处理步骤,但存在效果不理想或者计算复杂度过高的问题。提出一种新的灰度图像二值化的方法。该方法将Ncut作为谱聚类的量度,在计算该值时使用基于图像灰度级的权重矩阵,而非普通基于图像像素的权重矩阵。这样,计算复杂度和空间复杂度都明显降低。通过对实际场景中文本图像的实验,数据表明此方法在时间和系统开销方面比传统基于阈值的分割方法具有更优的性能。     

改进的使用非负矩阵分解的隐私保护分类方法

针对现有的基于非负矩阵分解的隐私保护数据挖掘方法中,不区分样本的重要性的不同,对所有样本都进行同样强度扰动的问题进行改进。提出了一种结合样本选择的基于非负矩阵分解的隐私保护分类方法。该方法使用样本选择将原始样本区分为重要的和不重要的两类。在对数据进行扰动时,使用现有的基于非负矩阵分解的方法对所有样本进行扰动。随后利用非负矩阵分解的聚类性质,对不重要的样本进行附加扰动。实验表明,该方法在保持数据可用性的同时,可以对隐私信息提供更好的保护。     

一种改进的基于Pi演算的Web服务自动组装方法

提出了一种改进的Web服务自动组装的方法,以提高Web服务组装的自动化。组装Web服务时,先用UML对复合Web服务进行建模,从UML模型得到复合服务的Pi演算描述,通过Pi演算的形式化验证,为这些逻辑子服务自动找到匹配的已实现的原子服务,最后生成可执行的复合Web服务。     

一种改进的RBF全局优化方法

径向基函数（Radial Basis Functions）由于具有良好的近似效果和运算简单的特点,被应用于全局优化中,成为解决黑箱函数全局优化问题的有效方法。然而现有的基于RBF的全局优化算法存在迭代过程中RBF模型重构效率低下,以及采样方法不合理导致函数估值次数过多等问题。在此提出几个改进思路：采用基于矩阵分块的增量RBF方法以减少模型重构时间提高效率;采用增量LHD采样方法以确保具有更好的空间填充性;采用算法重启策略以降低估值次数。通过实验验证改进方法的优势。     

Improved sensitivity analysis using a complex variable semi-analytical method

The semi-analytical method (SAM) is a computationally efficient and easy to implement approach often used for the sensitivity analysis of finite element models. However, it is known to exhibit serious inaccuracy for shape sensitivity analysis for structures modeled by beam, frame, plate, or shell elements. In the present paper, we use a semi-analytical approach based on complex variables (SACVM) to compute the sensitivity of finite element models composed of beam and plate elements. The SACVM combines the complex variable method (CVM) with the semi-analytical method (SAM) to obtain the response sensitivity accurately and efficiently. The current approach maintains the computational efficiency of the semi-analytical method but with higher accuracy. In addition, the current approach is insensitive to the choice of step size, a feature that simplifies its use in practical problems. The method is applicable to any structural elements including beam, frame, plate, or shell elements and only requires minor modifications to existing finite element codes.     

改进的小波高阶统计量的JPEG图像隐写分析

提出了一种针对JPEG图像隐写的盲检测改进方法。直接把JPEG图像的DCT系数分解到小波域,计算出各个子带小波系数的一系列高阶统计特征。对得到小波高阶统计特征进行校准,以使统计特征能更好地反映信息嵌入以后的变化。校准后的统计特征作为图像最后的特征。应用基于核技巧的支持向量机SVM作为分类器,判断图像是否含有隐藏信息。针对两种典型的JPEG类嵌入方法Outguess和JP Hide & Seek进行了实验,结果表明该方法有效地实现了信息隐藏的盲检测分析。     

Improved understorey bamboo cover mapping using a novel hybrid neural network and expert system

The giant panda is an obligate bamboo grazer. Therefore, the availability and abundance of understorey bamboo determines the quantity and quality of panda habitat. However, there is little or no information about the spatial distribution or abundance of bamboo underneath the forest canopy, due to the limitations of traditional remote sensing classification techniques. In this paper, a new method combines an artificial neural network and a GIS expert system in order to map understorey bamboo in the Qinling Mountains of south‐western China. Results from leaf‐off ASTER imagery, using a neural network and an expert system, were evaluated for their suitability to quantify understorey bamboo. Three density classes of understorey bamboo were mapped, first using a neural network (overall accuracy 64.7%, Kappa 0.45) and then using an expert system (overall accuracy 62.1%, Kappa 0.43). However, when using the results of the neural network classification as input into the expert system, a significantly improved mapping accuracy was achieved with an overall accuracy of 73.8% and Kappa of 0.60 (average z‐value = 3.35, p = 0.001). Our study suggests that combining a neural network with an expert system makes it possible to successfully map the cover of understorey species such as bamboo in complex forested landscapes (e.g. coniferous‐dominated and dense canopy forests), and with higher accuracy than when using either a neural network or an expert system.     

Improved occupancy grid mapping in specular environment

This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. In this paper the approach is experimented for the office environment and the model is used for grid mapping. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments.     

A simple method for Are growth mapping using AVHRR channel 3 data

AHVRR Channel 3 data have been used widely for forest fire detection and mapping. However, little attention has been paid to the use of these data for daily fire growth monitoring. A simple method for fire growth mapping using channel 3 data is presented. An 18000 hectare forest fire affecting the Mediterranean coast of Spain is used as a case study. Discrimination of burned area was performed on every image after multitemporal registration. A thermal threshold was established to mask out fire pixels in both diurnal and nocturnal images. GIS overlay techniques were used lo obtain a synthesis map of the daily evolution of the fire. This product can generate valuable input for fire behaviour programmes to improve our understanding of the factors affecting fire spread and fire severity.     

采用帧差法和相关滤波改进的TLD跟踪算法

针对TLD (tracking-learning-detection)算法实时性和鲁棒性差的问题,提出一种改进的FD-CFTLD (foreground detection-correlation filter TLD)目标跟踪算法。以TLD算法为基本框架,在检测模块采用帧差法进行前景检测,减小检测区域,提高检测速度;在跟踪模块采用核相关滤波(kernelized correlation filter,KCF)算法,并采用新的更新策略,使用检测模块修正后的跟踪结果更新跟踪器中的滤波器模型,提高跟踪的鲁棒性和精确度。实验结果表明,FD-CFTLD算法的成功率和精确度优于TLD算法,在应对光照变化、尺度变化和遮挡等场景时表现出良好的鲁棒性和实时性。     

Improved parameter estimates for non-linear dynamical models using a bootstrap method

It is known that the least-squares (LS) class of algorithms produce unbiased estimates providing certain assumptions are met. There are many practical problems, however, where the required assumptions are violated. Typical examples include non-linear dynamical system identification problems, where the input and output observations are affected by measurement uncertainty and possibly correlated noise. This will result in biased LS estimates and the identified model will exhibit poor generalisation properties. Model estimation for this type of error-in-variables problem is investigated in this study, and a new identification scheme based on a bootstrap algorithm is proposed to improve the model estimates for non-linear dynamical system identification.     

Reinforcement learning using Voronoi space division

Reinforcement learning is considered an important tool for robotic learning in unknown/uncertain environments. In this article, we suggest that Voronoi space division creates a new Voronoi region which permits an arbitrary point in the plane, say a Voronoi Q-value element (VQE), and constructs a new method for space division using a Voronoi diagram in order to realize multidimensional reinforcement learning. This article shows some results for four-dimensional spaces, and the essential characteristics of VQEs in a continuous state and action are also described. The advantages of learning with a variety of VQEs are enhanced learning speed and reliability for this task.     

Sparse polynomial division using a heap

In 1974, Johnson showed how to multiply and divide sparse polynomials using a binary heap. This paper introduces a new algorithm that uses a heap to divide with the same complexity as multiplication. It is a fraction-free method that also reduces the number of integer operations for divisions of polynomials with integer coefficients over the rationals. Heap-based algorithms use very little memory and do not generate garbage. They can run in the CPU cache and achieve high performance. We compare our C implementation of sparse polynomial multiplication and division with integer coefficients to the routines of the Magma, Maple, Pari, Singular and Trip computer algebra systems.