Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non-Euclidean algorithms which are computationally more demanding.     

A self‐applicable online partial evaluator for recursive flowchart languages

This paper describes a self‐applicable online partial evaluator for a flowchart language with recursive calls. Self‐application of the partial evaluator yields generating extensions that are as efficient as those reported in the literature for offline partial evaluation. This result is remarkable because it has been assumed that online partial evaluation techniques unavoidably lead to inefficient and overgeneralized generating extensions. The purpose of this paper is not to determine which kind of partial evaluation is better, but to show how the problem can be solved by recursive polyvariant specialization. The design of the self‐applicable online partial evaluator is based on a number of known techniques, but by combining them in a new way this result can be produced. The partial evaluator, its techniques, and its implementation are presented in full. Self‐application according to all three Futamura projections is demonstrated. The complete bootstrap of a compiler generator from a partial evaluator is also reported. Copyright © 2011 John Wiley & Sons, Ltd.     

基于运动状态改变的在线全球定位系统轨迹数据压缩

针对基于偏移量计算的轨迹数据压缩算法中对于关键点的评估不足以及基于在线轨迹数据压缩算法中累积误差和对偏移量考虑不足的问题,提出一种基于运动状态改变的在线全球定位系统(GPS)轨迹数据压缩算法——限定同步欧氏距离(SED)的阈值结合算法(SLTA)。该算法通过轨迹点的转向角度大小和速度变化大小来评估轨迹点信息量的大小;同时用SED限制点的偏移量,以达到较好的信息保留度。实验结果表明,SLTA的轨迹压缩率能够达到50%左右,与阈值结合算法(TA)相比,SLTA的平均SED误差(5 m以内)可以忽略不计;相对于基于偏移量计算的轨迹数据压缩算法,SLTA的平均角度误差最小(1.5°~2.3°),运行时间最稳定。SLTA能够稳定有效地进行在线GPS轨迹数据压缩。     

A Hybrid Approach to Online and Offline Partial Evaluation

This article presents a hybrid method of partial evaluation (PE), which is exactly as precise as naive online PE and nearly as efficient as state-of-the-art offline PE, for a statically typed call-by-value functional language.PE is a program transformation that specializes a program with respect to a subset of its input by reducing the program and leaving a residual program. Online PE makes the reduction/residualization decision during specialization, while offline PE makes it before specialization by using a static analysis called binding-time analysis. Compared to offline PE, online PE is more precise in the sense that it finds more redexes, but less efficient in the sense that it takes more time.To solve this dilemma, we begin with a naive online partial evaluator, and make it efficient without sacrificing its precision. To this end, we (1) use state (instead of continuations) for let-insertion, (2) take a so-called cogen approach (instead of self-application), and (3) remove unnecessary let-insertion, unnecessary tags, and unnecessary values/expressions by using a type-based representation analysis, which subsumes various monovariant binding-time analyses.We implemented and compared our method and existing methods—both online and offline—in a subset of Standard ML. Experiments showed that (1) our method produces as fast residual programs as online PE and (2) it does so at least twice as fast as other methods (including a cogen approach to offline PE with a polyvariant binding-time analysis) that produce comparable residual programs.     

基于RSSI信号特征的室内分区指纹定位算法

为提高室内定位精度和算法效率,提出基于RSSI信号特征的分区指纹定位算法。在离线阶段,区别于传统的使用RSSI信号构建离线指纹库的方法,设计使用RSSI信号衰减率建立离线指纹库;在在线定位阶段,针对使用欧式距离进行相似度计算时,容易出现两个点RSSI信号欧式距离较近而物理距离较远的情况,提出使用RSSI信号衰减率进行子区域划分,引入SSD的思想使用二级指纹进行精确定位。通过实验验证了该算法的适应性与有效性。     

Online And Offline Scheduling Schemes to Maximize the Weighted Delivered Video Packets Towards Maritime Cpss

In this paper, the online and offline scheduling schemes towards maritime Cyber Physical Systems (CPSs), to transmit video packets generating from the interior of vessel. During the sailing from the origin port to destination port, the video packets could be delivered via the infostations shoreside. The video packets have their respective release times, deadlines, weights and processing time. The video packets only could be successfully transmitted before their deadlines. A mathematic job-machine problem is mapped. Facing distinguished challenges with unique characteristics imposed in maritime scenario, we focus on the heterogeneous networking and resource optimal scheduling technology to provide valuable insights on the data transmission scheduling via this system. We aim to maximize the weight of delivered packets totally, three algorithms, an offline algorithm, an online ADMISSION Algorithm with no bounded processing times, as well as Exponential-Capacity Algorithm with bounded processing times are developed. Moreover, we induct the approximation ratio and competitive ratios of the proposed algorithms respectively. Finally, we verify the performance of the potential solutions for resource scheduling through comparison simulation.     

基于偏移量计算的在线GPS轨迹数据压缩

针对现有基于偏移量计算的在线GPS轨迹数据压缩算法不能有效评估关键点的问题,提出基于偏移量计算的在线GPS轨迹数据压缩算法--关键点前继修正算法（KPFA）。该算法通过计算同步欧式距离（SED）累积偏移量来发现轨迹点中信息量较大的关键点,同时设置阈值对关键点之前和上一个关键点之后的轨迹点进行修正,更好地保留轨迹信息。实验结果表明,和按时间比例的开窗算法（OPW-TR）及启发式空间质量简化算法的改进算法（SQUISH-E）相比,压缩率相同时KPFA的平均SED误差最小,并且运行时间最快且维持在100 000 ms。KPFA算法对轨迹点的信息量评估准确度更高,运行时间更稳定。     

基于改进遗传算法的加权模糊C均值聚类算法

针对模糊C均值(FCM)聚类算法具有初始聚类中心敏感和容易陷入局部最优的问题,提出了一种基于改进遗传算法(GA)的加权模糊c均值聚类算法,采用高斯变异算子,提高了遗传算法在每个峰值附近的局部搜索能力,用基于复相关系数的加权欧式距离代替欧式距离,改进了FCM算法的聚类目标函数.用改进的算法对国际标准测试数据Iris进行测试,实验结果表明改进后的算法具有更好的稳定性和健壮性,提高了聚类的效果.     

切线距离的SVD方法在汉字识别细分问题中的研究

针对手写体汉字字形变化复杂，而传统的欧式距离对汉字形变比较敏感。将基于切线距离的SVD分解选取切线向量方法应用于大字符集脱机手写体汉字识别细分类中，取得了较好的识别结果。实验证明，采用SVD选取切线向量的方法具有较好的通用性和重复性，适合应用于手写体汉字识别。     

Approximate algorithms for document placement in distributed Web servers

We study approximate algorithms for placing a set of documents into M distributed Web servers in this paper. We define the load of a server to be the summation of loads induced by all documents stored. The size of a server is defined in a similar manner. We propose five algorithms. Algorithm 1 balances the loads and sizes of the servers by limiting the loads to k/sub l/ and the sizes to k/sub s/ times their optimal values, where 1/k/sub l/-1 + 1/k/sub n/-1. This result improves the bounds on load and size of servers in (L.C. Chen et al., 2001). Algorithm 2 further reduces the load bound on each server by using partial document replication, and algorithm 3 by sorting. Algorithm 4 employs both partial replication and sorting. Last, without using sorting and replication, we give algorithm 5 for the dynamic placement at the cost of a factor Q(log M) in the time-complexity.     

无线传感器网络部分覆盖算法及连通性研究

研究了无线传感器网络在部分覆盖下的节点配置及网络连通性问题。首先,基于最优正六边形拓扑架构,给出了节点密集分布条件下的覆盖率与相邻工作节点间距的解析关系,并在已有的最优完全覆盖算法OGDC的基础上进行了扩展和改进,从而得到了一种新的网络节点配置算法EGDC（Extended OGDC Algorithm）。该算法可以有效地选择出合适的工作节点以达到任意给定覆盖率下的部分覆盖。此外,还给出了一种检验和评价网络连通性的方法,通过该方法可以对网络的连通性进行量化分析,并给出了一项评价网络连通性的指标。仿真表明,EGDC可以有效地实现任意期望覆盖率下的网络配置并保持网络的连通。     

基于交互式遗传算法的个性化建筑物外观设计

交互式遗传算法通过交互式的手段以用户对个体的评估来替代传统遗传算法的适应度函数设计,在艺术创作、设计等偏向于人类主观感受的领域具有很高的应用价值和广泛的现实意义。该文提出并设计了一个面向建筑物外观设计的基于交互式遗传算法的建筑设计计算机辅助系统,通过交互式手段以用户对个体评估来替代传统GA对适应度函数值进行自动计算,将两个专业领域知识的结合和互补,为建筑设计人员能够更快、更好地满足客户提出的个性化要求提供帮助。该系统从应用层次验证了算法的可行性和实用性。     

基于量子遗传聚类算法的入侵检测

针对传统入侵检测算法当面临未知攻击时所缺乏的自适应性和智能化日益突出的问题,提出一种新的无监督、自适应的检测算法——量子遗传聚类算法（CQGA）。该算法利用各实例之间的欧氏距离作为相似度量标准,通过量子遗传算法寻找聚类中心以达到在无监督的条件下对数据集自动分类的目的。实验仿真结果显示,该算法能较为准确地对测试数据集进行分类,有效地解决自适应性和智能化问题。     

基于贝叶斯网络的脱机手写体汉字智能识别

针对汉字识别的超多类问题,将贝叶斯网络分类器引入小样本字符集脱机手写体汉字识别中．对手写大写数字汉字的小样本字符集构造识别系统,同时与传统的欧氏距离方法进行比较,实验表明该算法将识别率提高到92．4％,在小样本字符集脱机手写体识别中具有较强的实用性和良好的扩展性．     

人工智能中A*算法的局部改进及其实现

本文对人工智能中的经典启发式搜索算法—A     

A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

Fast template matching using bounded partial correlation

This paper describes a novel, fast template-matching technique, referred to as bounded partial correlation (BPC), based on the normalised cross-correlation (NCC) function. The technique consists in checking at each search position a suitable elimination condition relying on the evaluation of an upper-bound for the NCC function. The check allows for rapidly skipping the positions that cannot provide a better degree of match with respect to the current best-matching one. The upper-bounding function incorporates partial information from the actual cross-correlation function and can be calculated very efficiently using a recursive scheme. We show also a simple improvement to the basic BPC formulation that provides additional computational benefits and renders the technique more robust with respect to the parameters choice. Received: 2 November 2000 / Accepted: 25 July 2001 Correspondence to: L. Di Stefano     

A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights

Identification of system predictors forms the first step towards formulating a predictive model. Approaches for identifying such predictors are often limited by the need to assume a relationship between the predictor and response. To address this limitation, (Sharma and Mehrotra, 2014) presented a nonparametric predictive model using Partial Informational Correlation (PIC) and Partial Weights (PW). This study describes the open source Nonparametric Prediction (NPRED) R-package. NPRED identifies system predictors using the PIC logic, and predicts the response using a k-nearest-neighbor regression formulation based on a PW based weighted Euclidean distance. The capabilities of the package are demonstrated using synthetic examples and a real application of predicting seasonal rainfall in the Warragamba dam near Sydney, Australia. The results show clear improvements in predictability as compared to the use of linear predictive alternatives, as well as nonparametric alternatives that use an un-weighted Euclidean distance.     

Unknown odor recognition using Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm

To deal with unknown odor recognition problem for a developed artificial odor discrimination system, Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm (EF-SONIA) is proposed. Euclidean fuzzy similarity enables a zero similarity calculation between an unknown odor vector and hidden unit vectors, so that the system can recognize the unknown odor. In addition, an elliptical approach for fuzziness determination is proposed. The elliptical approach can approximate an appropriate fuzziness, so that the unknown odor recognition accuracy is improved. Experiments on three datasets of three-mixture vegetal odors show that the recognition accuracy of the proposed method is 20% better than those of the conventional method. The system is very promising to be used for a real development of dog robot that enables localization and identification of dangerous natural gas.