基于多学习单元卷积神经网络的雷达辐射源信号识别

现有基于人工提取特征的复杂体制雷达辐射源信号识别方法时效性低,识别准确率不佳. 为此,提出了一种基于多学习单元卷积神经网络的识别方法. 首先对辐射源信号的模糊函数进行高斯平滑,以校正噪声带来的毛刺与畸变;然后提取其正交切片作为进一步的特征提取对象;最后构建多学习单元卷积神经网络,学习和提取正交切片深层、泛在的特征,并通过softmax分类器进行分类识别. 仿真实验结果表明,所提方法在信噪比为-2 dB时对6类典型雷达信号的整体平均识别率均保持在99.86%以上,即便是在-6 dB环境中,雷达信号的识别率也可达到88.50%,在极低信噪比条件下具有良好的性能和可行性.     

Language independent search in MediaEval's Spoken Web Search task

In this paper, we describe several approaches to language-independent spoken term detection and compare their performance on a common task, namely “Spoken Web Search”. The goal of this part of the MediaEval initiative is to perform low-resource language-independent audio search using audio as input. The data was taken from “spoken web” material collected over mobile phone connections by IBM India as well as from the LWAZI corpus of African languages. As part of the 2011 and 2012 MediaEval benchmark campaigns, a number of diverse systems were implemented by independent teams, and submitted to the “Spoken Web Search” task. This paper presents the 2011 and 2012 results, and compares the relative merits and weaknesses of approaches developed by participants, providing analysis and directions for future research, in order to improve voice access to spoken information in low resource settings.     

基于IVIF-VIKOR的雷达辐射源信号分选识别特征性能综合评价方法

为解决雷达辐射源信号分选识别特征评价不够客观和缺乏评价依据等问题,提出了一种基于区间模糊原理、模糊交叉熵和多准则折衷法的特征评价方法. 首先通过区间模糊原理建立信噪比分级评价模型,并基于汉明距离进行寻优得出信噪比权重;其次结合信噪比权重和区间直觉模糊加权平均算子将分级模型整合成群决策矩阵,使用熵最大化法计算属性权重;最后基于多准则折衷法框架,采取模糊交叉熵实现特征方案排序. 仿真实验结果表明,所提方法能够给出与实际仿真实验相一致的分选识别特征评价排序结果,并优于逼近理想点方法,验证了所提方法的可行性和有效性.     

Automated extraction of fragments of Bayesian networks from textual sources

Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by Big Data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered.In this article we propose and analyse a novel method to extract and build fragments of Bayesian networks (BNs) from unstructured large data sources. The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources.The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios.     

Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance

The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications. However, the structure of a CMAC network which will influence the learning performance is difficult to select. This paper proposes a dynamic structure CMAC network (DSCN) which the network structure can grow or prune systematically and their parameters can be adjusted automatically. Then, an adaptive dynamic CMAC neural control (ADCNC) system which is composed of a computation controller and a robust compensator is proposed via second-order sliding-mode approach. The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L₂ tracking performance with a desired attenuation level. Moreover, a proportional–integral (PI)-type adaptation learning algorithm is derived to speed up the convergence of the tracking error in the sense of Lyapunov function and Barbalat’s lemma, thus the system stability can be guaranteed. Finally, the proposed ADCNC system is applied to control a chaotic system. The simulation results are demonstrated that the proposed ADCNC scheme can achieve a favorable control performance even under the variations of system parameters and initial point.     

Mechanical properties and failure characteristics of fractured sandstone with grouting and anchorage简

Based on uniaxial compression experimental results on fractured sandstone with grouting and anchorage, we studied the strength and deformation properties, the failure model, crack formation and evolution laws of fractured sandstone under different conditions of anchorage. The experimental results show that the strength and elastic modulus of fractured sandstone with different fracture angles are significantly lower than those of intact sandstone. Compared with the fractured samples without anchorage, the peak strength, residual strength, peak and ultimate axial strain of fractured sandstone under different anchorage increase by 64.5–320.0%, 62.8–493.0%, and 31.6–181.4%, respectively. The number of bolts and degree of pre-stress has certain effects on the peak strength and failure model of fractured sandstone. The peak strength of fractured sandstone under different anchorage increases to some extent, and the failure model of fractured sandstone also transforms from tensile failure to tensile–shear mixed failure with the number of bolts. The pre-stress can restrain the formation and evolution process of tensile cracks, delay the failure process of fractured sandstone under anchorage and impel the transformation of failure model from brittle failure to plastic failure.     

CroMatcher: An ontology matching system based on automated weighted aggregation and iterative final alignment

In order to perform ontology matching with high accuracy, while at the same time retaining applicability to most diverse input ontologies, the matching process generally incorporates multiple methods. Each of these methods is aimed at a particular ontology component, such as annotations, structure, properties or instances. Adequately combining these methods is one of the greatest challenges in designing an ontology matching system. In a parallel composition of basic matchers, the ability to dynamically set the weights of the basic matchers in the final output, thus making the weights optimal for the given input, is the key breakthrough for obtaining first-rate matching performance. In this paper we present CroMatcher, an ontology matching system, introducing several novelties to the automated weight calculation process. We apply substitute values for matchers that are inapplicable for the particular case and use thresholds to eliminate low-probability alignment candidates. We compare the alignments produced by the matchers and give less weight to the matchers producing mutually similar alignments, whereas more weight is given to those matchers whose alignment is distinct and rather unique. We also present a new, iterative method for producing one-to-one final alignment of ontology structures, which is a significant enhancement of similar non-iterative methods proposed in the literature. CroMatcher has been evaluated against other state-of-the-art matching systems at the OAEI evaluation contest. In a large number of test cases it achieved the highest score, which puts it among the state-of-the-art leaders.     

Growth mechanisms of multilayered anodic-titanium-oxide nanotube membranes

Anodic titanium oxide TiO₂ nanotube membranes with one, two and three layers have been prepared by the anodic oxidation method via adjusting applied bias during the oxidation process in glycol electrolytes containing 0.25 wt% or 0.50 wt% NH₄F. The morphologies of the as-prepared samples were presented in detail using scanning and transmission electron microscopy. The results indicated that the glycol electrolyte containing 0.25 wt% NH₄F yielded smooth double-walled nanotubes, and their tubes remained open in multilayers; while the glycol electrolyte containing 0.50 wt% NH₄F yielded single-wall nanotubes with ripples, and most of the nanotubes have closed bottoms. In the case of nanotubes with closed bottoms, it was proposed that the newly grown nanotubes on the succeeding layer formed in the gap between the nanotubes of the previous layer. In order to keep track of the growth of the nanotubes, samples were anodized for different periods of time from 1 to 90 min to study their formation process. Growth mechanisms were proposed for different morphologies.     

贝叶斯网络推理的研究与分析

贝叶斯网络是人工智能中不确定知识表示和推理的有力工具。介绍了贝叶斯网络的概念,给出一个实例,分析了贝叶斯网络推理的方法和过程。     

Stochastic finite element analysis of shells

In the present paper the stochastic formulation of the triangular composite (TRIC) facet shell element is presented using the weighted integral and local average methods. The elastic modulus of the structure is considered to be a two-dimensional homogeneous stochastic field which is represented via the spectral representation method. As a result of the proposed derivation and the special features of the element, the stochastic stiffness matrix is calculated in terms of a minimum number of random variables of the stochastic field giving a cost-effective stochastic matrix. Under the assumption of a pre-specified power spectral density function of the stochastic field, it is possible to compute the response variability of the shell structure. Numerical tests are provided to demonstrate the applicability of the proposed methodologies.