探讨在版面分析中的聚类稳定性问题

版面分析过程可以理解为同模式类对象间聚类(合并)的过程,而这种聚类存在的风险(hazard)是伴随整个聚类过程中的。而且越是在后期,该风险值越高,即一旦出现聚类错误则将导致前期正确的聚类结果付诸东流。该文将就此问题展开关于版面分析中的聚类稳定性问题的探讨,并提出相应的逻辑规则——逻辑判别函数(logic differentiation function)用来指导聚类和其在聚类算法中的应用;实验结果表明,建立在定性分析基础上的该规则能解决聚类过程的稳定性问题,同时该规则可以应用在存在若干模式类对象聚类的场合中。     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

A Genetic‐Algorithm‐Based Optimized Clustering for Energy‐Efficient Routing in MWSN

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy‐efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near‐optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near‐optimal energy‐efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy‐efficient routing technique produces a longer network lifetime and achieves better energy efficiency.     

基于改进的蛙跳算法与支持向量机的实用语音情感识别

支持向量机是一种基于统计学习理论的新型机器学习算法,在高维特征空间中寻找最优分类超平面,具有很好的分类精度和泛化性能。支持向量机的训练需要求解一个带约束的二次规划问题,针对数据规模很大情况下经典训练方法变得很困难的缺点,提出一种基于改进混合蛙跳算法的支持向量机分类器参数优化方法,既提高了混合蛙跳算法的收敛速度和精度,又能借助混合蛙跳算法的全局随机搜索能力,为支持向量机参数的优化选择提供一条有效途径。本文提取情感语句的韵律特征、音质特征和混沌特征参数,提出一种基于改进蛙跳算法的数据融合方法,并利用基于改进蛙跳算法的支持向量机进行实用语音情感的识别研究。在实验中比较了PCA方法、BP神经网络和数据融合方法用于语音情感识别的识别率,研究结果表明本文所提的各项改进机制能有效提升情感识别率,为实用语音情感的识别提供一种新方法和新思路。      

SAR图像舰船目标检测算法的对比研究

SAR图像舰船目标检测有二种经典算法:双参数CFAR算法和K-分布CFAR算法。本文分析了二种算法的特点,使用RADARSAT卫星不同模式SAR图像分别进行实验,给出二种算法的适应性。     

一种基于"当前"统计模型的自适应滤波算法

针对机动目标跟踪问题 ,在“当前”统计模型的基础上 ,利用机动加速度与方差的自适应关系 ,提出了一种新的自适应滤波算法。大量仿真结果表明 ,该算法在跟踪机动目标时 ,具有良好的跟踪性能。     

基于无线数据业务智能优化系统算法的研究与应用

针对业界GPRS/EDGE无线数据优化方法、算法不完善,工作效率低的问题,广西联通通过长期研究建立了一套优化经验方法和较适合现网的参数配置、算法,并将这套方法、算法应用到自主开发的无线数据业务智能优化系统中,使系统实现自动采集分析数据、自动提供优化方案、自动执行优化方案、自动生成优化报告功能,使无线数据业务优化工作朝着智能化方向迈进。     

基于PR信道的BCJR检测

为了满足磁盘存储系统的高数据传输速率和高数据存储密度的需求,造成数字序列之间产生的码间干扰成为接收端获得可靠信息的主要阻碍。本文是以部分响应信号作为离散时间的ISI信道模型,采用基于MAP算法的对数域BCJR（log—MAP）算法进行信道检测。文章介绍了PR信道的模型,对算法的检测原理进行理论分析,并给出几种不同的部分响应信道下的计算机仿真。结果表明该算法能有效地对ISI信道进行检测,能应用于磁记录系统。     

Automatic, robust global motion estimation using clustering

Global motion estimation (GME) is a vital part of many video compression and computer vision applications. However, the large moving foreground objects that are present in many video scenes make the task of GME more challenging. In this paper, we propose an automatic, efficient, and robust approach for GME that addresses the issue of large foreground objects. The proposed GME algorithm is based on two key ideas: a new clustering technique, to automate the initial segmentation of background and foreground blocks, and a modified Lorentzian estimator, to reduce the impact of any remaining foreground blocks on the GME process. We also apply an up-sampling technique to the estimated motion parameters to remove any errors caused by under-sampling during the warping process. These ideas provide a significant improvement in performance when combined into a common framework. Simulation results and analyses demonstrate the improved performance of our proposed algorithm over other state-of-the-art methods.     

A pre-placement individual net length estimation model and an application for modern circuits

With the advances in integrated circuit (IC) technology, managing the individual and total interconnect is becoming one of the main challenges facing designers. An individual a-priori length estimation model can be a useful tool in helping designers obtain lower net lengths and congestion of interconnect. In this paper, the main characteristics that need to be considered while designing an individual a-priori length estimation technique for today's integrated circuits are discussed. A model that includes some of the most prevalent characteristics is designed and tested using the most current benchmark circuits released by IBM. In addition, one application of the length estimation is proposed in which a predictor-corrector framework for clustering that can be used to improve the results of placement is implemented. This model shows that the corrector step can improve the final placement results by up to 33% for special cases.