车载自组网中基于滑动窗口的网络编码传输策略

由于节点的高速移动和拓扑的快速变化,使得在车载自组网中传输时延敏感的数据是一个很大的挑战。针对此问题,提出了一种在机会路由上使用基于滑动窗口的网络编码传输策略。该策略根据网络状态自适应地调整滑动窗口的大小,来编码不同长度的编码包,去容忍ACK的延迟,使得在各种网络条件下都能保持较高的吞吐率;使用下三角形式的渐进编码使接收端逐步解码,从而平滑接收端的解码时间间隔。仿真结果表明,该策略具有更高的吞吐率,同时能够在接收端形成时延抖动小的数据流,为车载自组网中流媒体等时延敏感的数据流传输提供更好的服务质量。     

Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine

In this paper, we introduce a new learning method for composite function wavelet neural networks (CFWNN) by combining the differential evolution (DE) algorithm with extreme learning machine (ELM), in short, as CWN-E-ELM. The recently proposed CFWNN trained with ELM (CFWNN-ELM) has several promising features. But the CFWNN-ELM may have some redundant nodes due to the number of hidden nodes assigned a priori and the input weight matrix and the hidden node parameter vector randomly generated once and never changed during the learning phase. The introduction of DE into CFWNN-ELM is to search for the optimal network parameters and to reduce the number of hidden nodes used in the network. Simulations on several artificial function approximations, real-world data regressions and a chaotic signal prediction problem show some advantages of the proposed CWN-E-ELM. Compared with CFWNN-ELM, CWN-E-ELM has a much more compact network size and Compared with several relevant methods, CWN-E-ELM is able to achieve a better generalization performance.     

Matrix-product neural network based on sequence block matrix product

Convolution neural networks (CNNs) based on the discrete convolutional operation have achieved great success in image processing, voice and audio processing, natural language processing and other fields. However, it is still an open problem how to develop new models instead of CNNs. Using the idea of the sequence block matrix product, we propose a novel operation and its corresponding neural network, namely two-dimensional discrete matrix-product operation (TDDMPO) and matrix-product neural network (MPNN). We present the definition of the TDDMPO, a series of its properties and matrix-product theorem in detail, and then construct its corresponding MPNN. Experimental results on Fashion-MNIST, SVHN, FLOWER17 and FLOWER102 datasets show that MPNNs obtain 1.65–13.04% relative performance improvement in comparison with the corresponding CNNs, and the amount of calculation of matrix-product layers of MPNNs obtains 41× to 57× reduction in comparison with the corresponding convolutional layers of CNNs. Hence, it is a potential model that may open some new directions for deep neural networks, particularly alternatives to CNNs.

     

基于改进概率神经网络的纹理图像识别

引入差异演化（DE）算法来弥补基本概率神经网络的不足,从而提出一种基于改进概率神经网络（MPNN）的纹理图像识别方法。首先用树形结构小波包变换提取纹理图像的能量特征,用基于统计的纹理特征方法提取统计均值、平均能量、标准差和平均残余特征,得到纹理图像的特征矢量;然后用改进的概率神经网络训练纹理图像的特征矢量,从而实现纹理图像的识别。实验结果表明：采用基于改进概率神经网络的纹理图像识别方法较BP神经网络、RBF神经网络和基本的PNN有更高的识别正确率,且收敛更快。     

一种多重信道感知的主动机制网络编码算法

针对网络编码算法的能量开销大、解码出错率高等问题,提出一种多重信道感知的主动机制网络编码算法,该算法建立了系统模型来分析网络编码的工作方式,以最小化样本作为代价,采用多重信道感知的方法来检验中继节点的编码效果,使网络从中继节点的编码效果中进行学习,从而将源信息发送给编码效果较好的中继节点。为了减少解码出错率,算法采用了一种主动机制,该机制依据源节点的发射功率和源信息的大小来控制终端的解码出错率,提高网络解码能力。实验仿真对比结果表明,提出的网络编码算法能够有效控制网络编码的出错率,并能有效减少网络开销。     

Predictive Tracking Control of Network-Based Agents With Communication Delays

This paper investigates the remote tracking control problem of Network-based Agents with communication delays existing in both forward and feedback communication channels. A networked predictive tracking controller is proposed to compensate the negative effects caused by bilateral time-delays in a wireless network. Furthermore, the problem of consecutive data loss in the feedback channel is solved using aforementioned controller, where lateral movement perturbations are introduced. Simulations and experiments are provided for several cases, which verify the realizability and effectiveness of the proposed controller.      

协同系统中基于误比特率的波束成形设计

针对频率选择性信道下放大转发模式的多中继协同系统的波束成形因子设计问题进行研究。以最小化接收端总误比特率(BER)为目标,提出了一种两阶段的波束成形设计方法。所提方法可以有效降低设计的难度和复杂度,并且可以使接收端的总BER最小。仿真结果表明,与现有算法相比,本算法可以显著降低接收端的误比特率。     

稠密自组网的网关选举策略

自组网是没有固定设施的临时无线系统．已经有多种路由算法被提出．因为自组网的网络拓扑动态改变且带宽有限,路由应当是可扩展且高效的．基于簇的算法是最有效和可以扩展的,然而,它不能有效地处理高密度网络环境．为了减少冗余广播以缓解该问题,该文给出了在高密度节点的网络环境下,存在隐藏网关的可能性定理,提出网关选举算法并证明了其正确性．仿真结果表明,在保证广播成功率的情况下,该方法可以有效地节省重播包比率和广播等待时间。     

基于残差收缩网络的关系抽取算法

An improved algorithm based on residual shrinkage network with soft threshold module was proposed to solve the problem of noise caused by interference between words within a sentence in relation extraction. Firstly, the threshold was trained in each feature channel of the residual network. The threshold had two characteristics： first, its absolute value would not be too large, if it was too large, effective information would be eliminated; second, the threshold had different results for different input training. Secondly, according to the characteristics of soft threshold, the channel features lower than the threshold were deleted, and those higher than the threshold were reduced. Compared with direct deletion of negative features, soft threshold was able to save useful information of negative features. Finally, an optimization model of attention module was added to reduce the influence of mislabeling problem in distant supervision. Piecewise Convolutional Neural Network （PCNN）, Bi-directional Long Short-Term Memory （BiLSTM） network and ordinary Residual Network （ResNet） were selected as baseline models for comparison experiments. Experimental results show that the precision-recall curves of the proposed model include the curves of other models and the F1 scores of the proposed model are increased by 6.0 percentage points, 3.9 percentage points and 1.4 percentage points respectively compared to the baseline models, which verifies that addition of soft thresholding network model can improve accuracy of relation extraction by reducing in-sentence noise.     

模糊神经网络分类器在盲均衡算法中的应用

提出一种基于模糊神经网络分类器的盲均衡算法,将盲信道估计与模糊神经网络分类器相结合,先对通信信道进行盲估计,然后利用卷积原理重建信号,用模糊神经网络替代原有的判决器,从而实现了盲均衡。通过仿真实验证明,该算法加快了收敛速度,减小了剩余误差,降低了误码率。     

模糊神经网络分类器在盲均衡算法中的应用

提出一种基于模糊神经网络分类器的盲均衡算法,将盲信道估计与模糊神经网络分类器相结合,先对通信信道进行盲估计,然后利用卷积原理重建信号,用模糊神经网络替代原有的判决器,从而实现了盲均衡。通过仿真实验证明,该算法加快了收敛速度,减小了剩余误差,降低了误码率。     

一种改进的协同中继节点选择算法*

机会主义中继选择算法（ORS）基于瞬时信道质量选择一个最优中继节点帮助源节点进行协同通信,可以取得与其他复杂协议相同的分集阶次;但该算法存在碰撞问题,其采取的退避机制会增加网络延时。为此,提出一种碰撞时的改进处理方法,可以避免延时,提高网络容量。仿真结果表明,与原方法相比,该方法在保证BER性能时可有效地提高吞吐量。     

WSN中一种改进的基于LEACH-C算法的簇间路由算法研究

在WSN(Wireless Senor Network,无线传感器网络)中的分级路由算法中,如果簇头仅仅能够进行单跳通信或者多跳通信,都会造成网络负载不均衡以及簇头能量消耗过快的问题出现。针对这一问题,文章提出了一种改进的基于LEACH-C(Low Energy Adaptive Clustering Hierarchy Centralized,低功耗自适应集中分层型)算法的簇间路由(Cluster Routing based on LEACH-C Algorithm,简称CRLA)算法。该算法通过距离阀值来控制簇头是进行单挑通信还是多跳通信。仿真分析表明,CRLA算法能够实现网络负载的均衡以及减少簇头能量的消耗,从而实现网络生存时间的延长。     

传感器网络多路径传输效用-寿命折中优化研究

网络效用和网络寿命是无线传感器网络速率控制研究中两个极为重要而又互相冲突的设计目标.为兼顾网络性能需对二者进行折衷处理.通过引入折衷因子,建立网络效用和寿命的组合优化模型,利用拉格朗日对偶分解方法对优化问题进行求解,设计分布式的最优速率控制算法.仿真结果表明,通过调节折衷因子,可实现网络寿命和效用的均衡,并验证了提出算法的收敛性能及全局最优性.     

Doherty功放的贝叶斯正则化神经网络逆向建模研究

针对直接逆向建模方法精度低、稳定性差等缺点,提出了一种采用规则化函数为L1/2范数的贝叶斯正则化神经网络逆向建模方法,L1/2正则化使得网络结构具有稀疏性,能够缩小网络的规模、加快网络的训练速度,用贝叶斯正则化方法可以使网络的输出更加平滑,提高网络的稳定性和泛化能力。将此方法应用到Doherty功率放大器的设计中,在已知Doherty主功放效率、输出匹配端的S11和S21的情况下,分别仿真得出相对应的输出功率和f,可以简化设计过程。实验结果表明,此逆向模型求得的输出功率、与S11相对的f、与S21相对的f比直接逆向建模方法的均方误差分别减少了8.83%、9.30%和9.00%,运行时间分别减少了99.34%、99.40%和99.23%,解决了设计中的多解问题,可用于设计射频微波器件。     

Broadcast quality video over IP

We consider the problem of designing systems for the transmission of video signals of the quality found in current television broadcasts, over high-speed segments of the public IP network. Our most important contribution is the definition of a network/coder interface for IP networks which gathers channel state information, and then sets parameters of the video coder to maximize the quality of the signal delivered to the receiver, while remaining fair to other data or video connections. This interface plays a role analogous to that of a Leaky Bucket controller, in that it specifies traffic shaping parameters which result in simultaneous good Quality-of-Service (QoS) for the source and good network performance. Since the network is not assumed to provide any form of QoS guarantee, fundamental to our construction is a hidden Markov model for the channel, based on which the interface solves a problem of optimal stochastic control, to decide how to configure the encoder. Other contributions are: a) modifications to the standard Internet transport protocol, to make it suitable for the transport of delay-constrained traffic and to gather channel state information, and b) the design of an error-resilient video coder. Experimental studies reveal that the proposed system is able to stream video signals of the quality of current TV-broadcasts, among hosts in wide-area networks connected to the experimental vBNS backbone     

Structure minimization using the impact factor in neural networks

Despite many advances, the problem of determining the proper size of a neural network is important, especially for its practical implications in such issues as learning and generalization. Unfortunately, it is not usually obvious which size is best; a system that is too small will not be able to learn the data, while one that is just big enough may learn very slowly and be very sensitive to initial conditions and learning parameters. There are two types of approach to determining the network size: pruning and growing. Pruning consists of training a network which is larger than necessary, and then removing unnecessary weights/nodes. Here, a new pruning method is developed, based on the penalty-term method. This method makes the neural networks good for generalization, and reduces the retraining time needed after pruning weights/nodes. This work was presented, in part, at the 6th International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001.     

面向移动自组网的分布式认知MAC协议改进设计

针对移动自组网MAC协议未考虑节点移动性、缺乏功率控制机制、算法开销较大等问题,改进设计基于信道可维持时间的分布式认知MAC协议。新协议建立了节点存储信息结构,根据节点功率与移动速度的关系,构建信道可维持时间模型,并改进信道感知和数据传输算法,增加移动性和信道增益考虑,降低速度和功率对信道感知的影响,提高协议对移动性的适应能力。结果表明,对于中低速网络而言,该协议在不增加控制开销的基础上,丢包率、时延和频谱利用率等性能得到较大改善。     

融合局部与全局特征的肺炎医学影像分类

针对现有肺炎医学影像识别研究在浅层网络忽略全局特征导致特征提取不全且模型规模较大的问题, 提出了一种基于CNN和注意力机制的轻量化模型提高肺炎类型的识别效率. 采用轻量化模型结构减少模型参数量, 通过增大卷积核, 引入高效通道注意力和自注意力机制解决网络重要信息丢失和无法提取底层全局信息的问题, 通过双分支并行提取局部和全局信息并使用多尺度通道注意力提高二者融合质量, 使用CLAHE算法优化原始数据. 实验结果表明, 该模型在保证轻量性的同时准确率、灵敏度、特异性较原模型分别提高2.59%, 3.1%, 1.38%, 并优于当前优秀的其他分类模型, 具有更强的实用性.     

基于蚁群算法和BP神经网络的信道分配策略的研究

研究无线传感器网络信道分配策略的主要目标是提高网络吞吐量和容量,减小网络的传输时延,最大限度的利用有限的网络带宽资源。多信道MAC协议的应用,可以有效地提高网络通信的可靠性和吞吐量,以及解决由于信道受干扰而造成的网络瘫痪等问题。根据无线传感器网络多信道的特点提出了一种基于蚁群算法的动态反馈负载均衡信道分配策略。本策略首先应用BP神经网络对信道负载情况进行预测,然后通过基于蚁群算法的负载均衡算法对信道进行筛选,最后利用最大离散化算法进行信道分配。在NS2平台下对所设计的协议进行了仿真实现,并与应用最为广泛的多信道MMAC协议以及SMAC进行了对比分析。根据仿真结果可知,本文设计的MAC协议在网络吞吐量、网络传输时延等性能方面比MMAC协议及SMAC都有了很大程度的提升。可以有效减小网络传输时延,提高网络吞吐量和抗干扰能力。