基于Gammatone滤波器组时频谱和卷积神经网络的海底底质分类

为了有效利用海底底质信号完成海底底质的分类识别,该文提出一种将深度学习方法和底质信号相结合实现底质分类识别的方法.首先利用Gammatone滤波器组计算底质侧扫图像信号的时频谱,然后通过卷积神经网络对得到的时频谱进行分类识别完成底质分类.利用加利福尼亚州Scott Creek近海采集的侧扫声呐图像数据进行数据分析,结果...     

Pi-Sigma网络在水声目标分类中的应用

长期以来,由于受许多因素的影响,使得水声目标的分类已成为一个十分困难的问题。现在,随着人工神经网络技术的发展,众多的研究人员已经致力于基于人工神经网络的水声目标分类的研究.本文介绍了一种高阶神经网络即Pi-Sigma网络,研究了它的两种学习算法(基于梯度下降法和共轭梯度法的学习算法),并将Pi-Sigma网络用于水声目标辐射噪声的分类。和多层感知器(MLP)网相比,Pi-Sigma网络具有结构简单、收敛速度快及存储量少等优点。Pi-Sigma网络分类器的输入为一个常Q带通滤波器组作特征提取形成的特征向量。对不同类别的实际水声数据的分类结果表明取得了令人满意的分类正确率(达到或超过了95%)。     

联合特征选择与改进集成学习模型的海底底质分类

针对侧扫声呐获取类型复杂的海底底质数据分类问题,提出联合特征选择与改进Stacking模型的数据自驱动分类方法。该方法首先在海底散射数据多域态特征的基础上采用ReliefF算法提取有效的低维度特征,然后将人工鱼群算法与Stacking模型结合形成改进集成学习分类器,完成海底底质分类。海上数据处理结果表明该方法可对多种海底底质类型进行分类,分类准确率、Kappa系数和F1-score分别达到85.55%,0.857和0.887,证明了该方法的有效性。     

浅海底质高光谱反射率测量系统的设计及应用

海底光谱反射率是光学浅水中太阳辐射传输的重要组成部分,影响着海水表面离水辐亮度的光谱特性,因此底质光谱信息的准确获取对于浅海遥感工作的开展至关重要。专门设计了一套海底光谱反射率测量系统填补了国际上在这方面的空白。采用可自由伸缩并旋转角度的参考白板贴近目标物测量,以消除探头到目标物之间水体吸收衰减的影响,双光路采集系统同步测量的设计避免了水下光场迅速变化对辐射测量的影响。于2018年9月3日—8日,用该系统在三亚珊瑚礁保护区进行原位海底反射率测量试验,测量对象包括珊瑚、海草、泥沙、沙滩等多种底质。各底质类型之间具有光谱可分性,具体表现为,在波长大于580 nm的长波段,浅海沙子底质与岸上沙滩光谱反射率特征差异明显,表明相对于空气中,水体和水中微藻介质的吸收散射作用严重影响着水下光谱辐射的测量,证实了空气中测量的目标光谱不可替代水中的结果。珊瑚和水草的光谱反射率特征主要区别在于海草反射率光谱在540~600 nm波段有一个宽反射峰,而珊瑚的典型特征是在575,600和650 nm附近有三个特征反射峰。此外,珊瑚、沙子和沙滩三种碳酸盐质底质在395,430,490和520 nm存在反射峰,485和585 nm处有一个小吸收峰,而海草则相反,在395,430,490和520 nm存在吸收峰,485和585 nm处显示反射峰。以上数据为将来利用底质反射率提取底栖物质组成信息奠定了基础,同时其结果也能够证实系统的可靠性和有效性。     

AGA-BP模型在遥感影像分类中的应用研究

作为遥感研究的关键技术,遥感影像分类一直是遥感研究热点;针对目前采用BP神经网络模型进行遥感影像分类时存在的对初始权阈值敏感、易陷入局部极值和收敛速度慢的问题,为了提高BP模型遥感影像分类精度,将自适应遗传算法引入到BP网络模型参数选择中;首先运用自适应遗传算法对BP模型权阈值参数进行初始寻优,再用改进BP算法对优化的网络模型权阈值进一步精确优化,随后建立基于自适应遗传算法的BP网络分类模型,并将其应用到遥感影像数据分类研究中;仿真结果表明,新模型有效提高了遥感影像分类准确性,为遥感影像分类提出了一种新的方法,具有广泛研究价值。     

概率神经网络及FAAS在植物药分类研究中的应用

用火焰原子吸收法（FAAS）测定了植物药中Fe、Mg、Mn、Cu、Zn和Ca元素的含量,采用主成分分析法对所测数据进行预处理,结合概率神经网络模型对中药功效类别进行识别预测研究,取得了较满意的结果。     

基于SFLA优化BP神经网络的语音信号分类

BP神经网络是一种多层前馈网络,数据经过网络的输入层、隐含层逐层处理后,由输出层进行输出,通过和期望输出的对比进行反向传播,调整网络参数使输出不断逼近期望输出;在使用BP神经网络对语音特征信号进行分类的过程中,会出现BP神经网络易陷入局部最优解、学习收敛速度慢的问题;针对此问题提出一种基于SFLA优化BP神经网络权值和阀值的方法,引入SFLA算法优化网络权值和阀值,利用SFLA优化后的BP网络模型进行语音特征信号分类;仿真结果表明,经SFLA优化后的BP神经网络与未优化的神经网络相比,不仅训练速度快, 而且误差小,语音特征信号分类的正确率平均提高1.31%。     

基于主成分分析的自组织竞争神经网络在多光谱遥感影像分类中的应用

多光谱遥感影像具有波段多、信息量大的特点,传统的分类方法难以达到提高精度的要求.利用主成分分析对多波段遥感图像进行降维,再采用竞争型自组织神经网络对图像进行非监督分类.这种方法的分类精度为87.5%,Kappa系数为0.86,明显高于最大似然法,最小距离法和基于像元的自组织竞争神经网络法.实验结果表明该方法在多光谱遥感影像分类中具有较好的适用性.     

基于一维卷积神经网络的化爆和地震次声分类

次声事件的分类识别方法应用广泛,传统分类方法在很多方面进行了尝试,但由于次声信号具备非线性的特点,致使分类难度较大,分类精度不高,这对次声事件的分类工作提出了挑战.针对次声事件中的化学爆炸与天然地震信号分类问题,文章构建了一种改进的深度卷积神经网络分类模型用于实现两类次声信号的分类.论文采用"全面禁止核试验条约组织"官...     

超声提取-气相色谱法测定底质中的邻苯二甲酸酯

样品经风干、研磨、过筛,用10mL二氯甲烷/丙酮(1+1,V/V)超声提取二次,每次10min;过滤后,提取液经氮吹浓缩再用正己烷进行溶剂置换,以乙醚/正己烷(1+4,V/V)为洗脱液用氧化铝柱净化;净化后的提取液用带电子捕获检测器的气相色谱仪检测4种邻苯二甲酸酯(DMP、DEP、DBP和DEHP。在优化的条件下,4种PAEs的检出限均在0.62-1.21(g/kg之间;相对标准偏差在0.05%-9.65%之间;回收率在88%-101%之间。并用此方法对实际样品进行了测定。     

A one-dimension convolutional neural network based interference classification method

Interference is a common problem in wireless communication, navigation and radar systems. A wide variety of interferences are used to degrade the communication quality especially in electronic warfare environment. In modern military communication systems, interference classification is an important module for its ability to obtain prior interference information before adopting related anti-interference method. This paper proposes a deep learning based interference classification method, which applies one-dimension convolutional neural networks to automatically extract interference features for classification. Computer simulations show better classification performance and lower computational complexity. Meanwhile, this proposed method is implied on software defined radios (SDR) hardware, more than 99% correct classification probability can be achieved with limited samples of the received signal, which verifies the robustness of this proposed method.     

In vitro microemboli classification using neural network models and RF signals

Emboli classification is of high clinical importance for selecting appropriate treatment for patients. Several ultrasonic (US) methods using Doppler processing have been used for emboli detection and classification as solid or gaseous matter. We suggest in this experimental study exploiting the Radio-Frequency (RF) signal backscattered by the emboli since they contain additional information on the embolus than the Doppler signal. The aim of the study is the analysis of RF signals using Multilayer Perceptron (MLP) and Radial-Basis Function Network (RBFN) in order to classify emboli.Anthares scanner with RF access was used with a transmit frequency of 1.82 MHz at two mechanical indices (MI) 0.2 and 0.6. The mechanical index is given as the peak negative pressure (in MPa) divided by the square root of the frequency (in MHz). A Doppler flow phantom was used containing a 0.8 mm diameter vessel surrounded by a tissue mimicking material. To imitate gas emboli US behaviour, Sonovue microbubbles were injected at two different doses (10μl and 5μl) in a nonrecirculating at a constant flow. The surrounding tissue was assumed to behave as a solid emboli. In order to mimic real clinical pathological situations, Sonovue concentration was chosen such that the fundamental scattering from the tissue and from the contrast were identical. The amplitudes and bandwidths of the fundamental and the 2nd harmonic components were selected as input parameters to the MLP and RBFN models. Moreover the frequency bandwidths of the fundamental and the 2nd harmonic echoes were approximated by Gaussian functions and the coefficients were used as a third input parameter to the neural network models. The results show that the Gaussian coefficients provide the highest rate of classification in comparison to the amplitudes and the bandwidths of the fundamental and the 2nd harmonic components. The classification rates reached 89.28% and 92.85% with MLP and RBFN models respectively.This short communication demonstrates the opportunity to classify emboli based on a RF signals and neural network analysis.     

A multi-target approach for isolated word recognition using artificial neural network

I.TntroductionStatisticalandneuralnetworkmcthodsforpatternclassiflcationusesignificantlydifTerentapproachesintrainingaclassificr.Inthestatistica1approach,thcformationofaclassifier1arge1ydependsonthestatisticsofthetrainingpatternsand,insomecases,theassumptionsaboutthedistributionofthepopulation.Theneuralnetworkmethodisnon-parametricandcanbeadaptivcinthetrainingprocessl'l.Becauseofitssimplicityandflexibility,theneuralnetworkhasbecnincreasing1yusedforpatternclassiflcation.Itisnowwe11knownthatan…     

一种递归神经网络空间分集均衡器

水声信道中的衰变多途特性常引起难以克服的码间干扰(ISI)。为了降低误码率、改善系统性能,本文提出了一种递归神经网络空间分集均衡器。它在传统的空间分集结构中融入了多层感知器,该结构能够充分利用训练信号的信息,在可调权数较少的情况下,能够得到较好的均衡效果;针对该结构,采取了初始设定权值的办法,从而使该均衡器的收敛速度得到大大提高。仿真与湖试试验结果表明,该均衡器结构合理,对空间分集均衡性能有一定的改善。     

Microstructural characterization of materials by neural network technique

Ultrasonic signals received by pulse echo technique from plane parallel Zircaloy 2 samples of fixed thickness and of three different microstructures, were subjected to signal analysis, as conventional parameters like velocity and attenuation could not reliably discriminate them. The signals, obtained from these samples, were first sampled and digitized. Modified Karhunen Loeve Transform was used to reduce their dimensionality. A multilayered feed forward Artificial Neural Network was trained using a few signals in their reduced domain from the three different microstructures. The rest of the signals from the three samples with different microstructures were classified satisfactorily using this network.     

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera,particle filter and neural network

A powerful approach in the area of real-time mobile objects tracking in crowded environments, utilizing 3D video frames analysis is now taken into real consideration, as a candidate to be improved. The method presented here is able to track a number of real-time mobile objects in the real complex situations in the presence of occlusion, overlapping and various shifts. This is a development of probabilistic estimation theory via particle filter. In one such case, the whole of chosen new features of mobile objects, which are unconsidered in the present probabilistic estimation, should first be analyzed through a novel neural network. Subsequently, the probabilistic estimation in each one of frames may be made in a better outcome, as long as all the mentioned components are integrated. Evaluation of the proposed approach through PETS-09 database has been finally carried out, once the results with respect to a number of standard benchmark procedures indicate that 12% accuracy improvement is acquired.     

神经网络的声场景自动分类方法*

声场景探察和自动分类能帮助人类制定应对特定环境的正确策略,具有重要的研究价值。随着卷积神经网络的发展,出现了许多基于卷积神经网络的声场景分类方法。其中时频卷积神经网络(TS-CNN)采用了时频注意力模块,是目前声场景分类效果最好的网络之一。为了在保持网络复杂度不变的前提下进一步提高网络的声场景分类性能,该文提出了一种基于协同学习的时频卷积神经网络模型(TSCNN-CL)。具体地说,该文首先建立了基于同构结构的辅助分支参与网络的训练。其次,提出了一种基于KL散度的协同损失函数,实现了分支与主干的知识协同,最后,在测试过程中,为了不增加推理计算量,该文提出的模型只使用主干网络预测结果。在ESC-10、ESC-50和UrbanSound8k数据集的综合实验表明,该模型分类效果要优于TS-CNN模型以及当前大部分的主流方法。     

束晕-混沌的神经网络自适应控制

 理论分析了强流离子束在周期磁场聚焦通道中传输时产生的束晕 混沌动力学行为,给出了近似反映实际聚焦磁场的余弦函数形式。然后利用神经网络方法对非线性复杂系统控制的优越性,提出前馈反传神经网络方法对强流离子束中束晕 混沌进行自适应控制。通过适当选择的神经网络控制结构和线性反馈系数以及自适应调整神经网络的权系数,可将强流离子束的包络半径达到束匹配半径的控制目标,且束包络的抖动大小明显减少,束晕 混沌现象得到了明显的抑制。     

遗传优化神经网络的水声信道盲均衡

不需要训练序列的盲均衡技术可以有效地节省水声通信带宽，消除码间干扰，提高水声通信效率和质量。以前馈神经网络（FNN）作为盲均衡器，既适用于最小相位信道，也适用于非最小相位信道，包括非线性信道，但是前馈神经网络在实际的应用中其网络拓扑结构的选取和初始权重的确定缺乏理论依据，且其训练主要依靠BP算法，存在收敛速度慢、容易陷入局部极值及“过学习”的问题。为此，本文提出了一种遗传优化神经网络的水声信道盲均衡算法（GA—BP），对前馈神经网络拓扑结构和网络权重同时优化，有效地克服了传统前馈神经网络盲均衡的缺陷，提高了前馈神经网络盲均衡的泛化性能并加强了跟踪时变信道的能力和对信道突变的适应能力。水池试验结果证明了文中提出的遗传优化神经网络水声信道盲均衡算法的有效性，与直接前馈神经网络盲均衡相比较，均衡性能明显得到了提高。