Granular Neural Networks With Evolutionary Interval Learning

To deal with different membership functions of the same linguistic term, a new interval reasoning method using new granular sets is proposed based on Yin Yang methodology. To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a granular neural network (GNN) with a new high-speed evolutionary interval learning is designed. Simulation results in nonlinear function approximation and bioinformatics have shown that the GNN with the evolutionary interval learning is able to extract interval-valued granular rules effectively and efficiently from training data by using the new evolutionary interval learning algorithm.     

Video Annotation Based on Kernel Linear Neighborhood Propagation

The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, a novel graph-based semi-supervised learning method named kernel linear neighborhood propagation (KLNP) is proposed and applied to video annotation. This approach combines the consistency assumption, which is the basic assumption in semi-supervised learning, and the local linear embedding (LLE) method in a nonlinear kernel-mapped space. KLNP improves a recently proposed method linear neighborhood propagation (LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach outperforms other popular graph-based semi-supervised learning methods for video semantic annotation.     

K(Knowledge)-net: building up and its dynamics

The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence?     

Convergent Smoothing and Segmentation of Noisy Range Data in Multiscale Space

With few exceptions, most of the existing noise reduction and data segmentation algorithms are only suited to image data. Therefore, an adaptive smoothing algorithm, with model-based masks, within a scale space framework is proposed for range data in this paper. This algorithm smoothes range data that conform to predefined, geometric models, while leaving other data points unaffected. The convergence of the algorithm in yielding dominant features is shown based on its compliance with the anisotropic diffusion concept. The weights of the smoothing masks are adaptively calculated according to the Mahalanobis distances between range data and model-based predictions. These behave as the diffusion coefficient in the anisotropic diffusion equation, thus satisfying the requirements of the causality criterion that no new features are introduced from fine to coarse scales. The computational complexity of this algorithm is examined and compared to that of the well-known RANSAC feature extraction algorithm. Unlike RANSAC, it has the advantage that the computational complexity is less affected by increasing the order of the model, and is independent of the number of model outliers. The proposed algorithm can be used to smooth range data in multiscale space by increasing the number of smoothing iterations. Robust, robot-occlusion-invariant features are then easily extracted from the smoothed data by least squares fitting algorithms.     

有机薄膜器件的镀膜自动控制系统设计

设计了用于有机薄膜器件的镀膜自动控制系统;整个自动控制系统以单片机为核心,可分为3个功能模块:人机交互、强弱电转换、样品处理。人机交互由单片机来实现;强弱电转换由固体继电器来完成;样品处理包括镀膜速率自动控制和样品保护两个部分。控制系统体现了弱电控制强电的设计思路,系统采用了单片机、继电器、电磁阀和可调变阻器等器件以及程控可调电阻电路。所设计的控制系统能克服目前有机薄膜器件镀膜系统的一些弊端,实现镀膜和镀膜速率的自动控制,并且能够避免有机半导体材料在真空腔内的相互污染。     

基于ARM的人体脂肪测量仪的设计

为解决传统的人体脂肪测量仪只测量人体的全身阻抗,不能反映人体各个部位的脂肪分布情况的问题,提出了人体阻抗分布模型和分段阻抗计算公式,进而测量人体不同部位的脂肪含量的方法,并设计完成了一种基于ARM处理器的人体脂肪测量仪;该仪器以ARM处理器S3C2410为核心,外扩激励信号源、信号采集和处理、放大电路及整流滤波等电路,在嵌入式linux操作系统下实现了对人体脂肪的测量和分析;实验结果表明,该仪器比传统的人体脂肪测量仪检测精度高,能反映人体脂肪分布状况。     

基于CCD的电缆料杂质检测的研究

为了提高现阶段XLPE电缆料杂质检测的精度与速度,运用TCD132D芯片,建立了杂质检测系统;运用虚拟示波器DSO2902对系统采集的数据进行分析和处理,从而有效地识别杂质颗粒的存在与否并能够准确地计算出存在杂质颗粒的尺寸、位置等;结果表明,该系统能够准确地测出XLPE电缆料的杂质颗粒的尺寸,并能够确定该杂质颗粒所处的准确位置与杂质颗粒的个数等特性,其分辨力可达到20μm,误差小于10%,杂质的检出率达100%。     

Ad hoc网络中基于节能的DSR路由算法改进

在Ad hoc中,DBF、DSDV、AODV,DSR等都是基于最小跳路由协议.从能量的角度来看,最短路由并不一定是最佳的路由.为此在DSR按需驱动路由基础上提出了一种新的基于动态优先因子的路由策略EDSR.在这种协议中,综合考虑能耗低,链路容量大的路由,从而较好地节省网络能量.     

用ID3算法对高校计算机教学中学生成绩的分类研究

介绍判定树归纳分类ID3算法,利用ID3算法,对有上机课的课程成绩分类,为学生和老师提供参考信息.     

网络封包截获技术研究

深入研究Windows server 2003 DDK 的passthru中间层驱动程序,并对该驱动程序进行扩展,使之能对底层的网络封包实现截获和过滤.