平衡滤波器的计算方法

文章介绍了将多级平衡滤波器转换成多级等效梯形网络的物理思想和具体方法，再利用计算梯形网络的程序算出多级平衡滤波器的输入阻抗和插入损耗。     

包含外壳磁阻的微型平衡电枢换能器仿真模型的阻抗和振动分析

现有的“E”型平衡电枢等效磁路模型仿真研究通常不考虑金属外壳磁阻带来的影响。为了解决平衡电枢换能器中因金属外壳和平衡电枢紧密接触带来的非线性磁阻问题，在现有的平衡电枢换能器等效磁路模型上加入了外壳磁阻影响。分析等效磁路模型磁通部分和力学部分的状态空间方程在不同参数条件下的仿真结果，总结不同参数对该模型阻抗和振膜位移的影响情况。通过仿真对比，外壳磁阻对平衡电枢换能器位移频率响应曲线的影响为1～3 dB。对于组装后包含金属外壳的平衡电枢耳机、助听器产品降低频率响应曲线偏差具有一定的指导意义。     

用非平衡电桥测电阻抗在医学上的应用

通常用平衡交流电桥测量确定的电阻抗值,但在医学上常会遇到随时间变化的电阻抗,测量这种变化电阻抗可用非平衡交流电桥。这种变化的电阻抗可以给出许多医学信息,因而使得用电桥测电阻抗这一非常普通的物理方法,在医学上得到实际应用。     

力系平衡是刚体平衡的充要条件吗

力系平衡只是刚体平衡的必要条件,刚体平衡的充分必要条件是,在初始时刻刚体静止,力系的主矢和对任意点的主矩在某时间段内等于零.笔者指出了某些力学教材在这个问题上的错误并列举了3种典型的证法.     

验光中的双眼平衡

验光后应进行双眼平衡检验，但一般仅将它理解为视力平衡。本文论述了视力平衡、光焦度平衡和双眼像平衡三种平衡及其相互关系，并提出一种新的散光眼像平衡的估算方法。     

声阻抗转换器的研究

描述不均匀传输线中的声反射系数可以归结为求解一种非线性微分方程（Riccati方程），本文探索一条寻找此类方程解析解的新方法，并利用此方法研究了声阻抗转换器，从而大大拓宽了声阻抗转换器阻抗分布的研究范围，作为例子，本文提出了几种阻抗变换形式，并把它们的功率传输效率与已有的均匀及指数型转换器作比较，计算结果表明：当转换器两端与传输介质的阻抗跃变很大时，某些阻抗转换器的传输效率比均匀及指数型转换器高得多．     

气动激光器喷管非平衡流2维数值仿真

将Anderson的两振型三温度弛豫模型和严海星整理的弛豫数据相结合,采用2维守恒型方程组对按照最小长度喷管型面设计方法设计的、面积比分别为50和20的气动激光器喷管非平衡流场进行了数值仿真。小信号增益计算结果在每个计算点都和J.S.Vamos等人针对这两种喷管的小信号增益测量试验结果符合很好.解决了传统的准1维非平衡流分析方法不能很好地和试验结果相符的问题.对气动激光器喷管性能设计提供了更精确的评估方法。     

高速离心泵轴向力平衡方法研究

根据高速离心泵轴向力产生的原因提出了平衡孔与平衡管、斜齿轮传动和平衡鼓与平衡盘的组合结构等轴向力平衡方法,通过理论分析和设计实践,得出采用平衡孔和平衡管及斜齿轮传动是简单但又实用的高速离心泵的主要轴向力平衡方法。     

基于新型自耦合非对称阶梯阻抗谐振器的小型化三通带平衡滤波器

针对平衡滤波器的高集成度和高选择性的应用需求,基于一种新型自耦合非对称阶梯阻抗谐振器提出了一种具有高选择性的小型化三通带平衡滤波器。首先通过该平衡滤波器的差模和共模等效电路详细分析了其谐振器结构的谐振特性,并利用差模等效电路下的前三个谐振模式来分别实现三个通带。另外通过在电路对称面上加载电容和电阻元件来提高对共模噪声的抑制,且不影响差模频率响应特性。基于提出的多模平衡滤波器结构和设计方法,设计了一个通带频率为2.75/4.46/6.21 GHz的三通带平衡滤波器,并对其进行了加工和测试,结果表明,该结构可以实现紧凑的体积和高选择特性,并且具有很好的共模抑制特性。     

液态Co物性的平衡及非平衡分子动力学模拟

本文采用EAM作用势,通过平衡分子动力学(EMD)模拟的方法计算了Co熔体的自扩散系数、剪切粘度等物理性质.同时采用非平衡分子动力学(NEMD)方法计算了Co的剪切粘度.研究表明有关传输性质的计算是可与实验比较的,能够反映出液态Co典型的动力学特性.     

光网络节点设备信噪比恶化的测量

从理论与实验两方面分析了光网络节点设备光信噪比（OSNR）恶化量的测量,导出了单节点设备输出,输入侧光信噪比之差的公式。理论分析表明：节点设备输出,输入侧光信噪比之差值在一般测试条件下不能反映节点设备对光信号的影响,能作为节点设备光信噪比的恶化量来衡量设备对光信号的影响;为使测量结果能反映光节点参数对光信号的综合影响,测试系统应当满足文中给出的增益条件C＝1。实验结果支持了理论分析,实验结果表明,对于C＝1时光信噪比恶化量为4.5dB的光点设备,当C从3变化与0.16时,其输入、输出侧光信噪比之差从1．8dB变化到11．3dB.     

典型生化网络构建子块中噪声传播的机制

活性细胞的内部过程是一个内在的随机过程,而细胞内分子丰度的波动对细胞的生长和发育会产生重大影响. 对生化网络（包括信号转导网络和代谢网络）的典型构建子块,采用统一的生化反应模式,应用线性噪声逼近理论刻画出噪声传播的一般机制：信号转导网络中存在噪声传播,且噪声强度满足加和规则;代谢网络中没有噪声传播,稳定态处物种分子数的分布相互独立. 这为理解细胞内部过程奠定了理论基础. 关键词： 噪声传播 线性噪声逼近 信号转导网络 代谢网络     

非对称双稳耦合网络系统的尺度随机共振研究

研究了不同周期信号调制下非对称双稳耦合网络系统的尺度随机共振问题. 针对该网络系统, 首先运用高斯近似和役使原理对其进行了降维, 推导了其简化模型. 在绝热近似条件下, 利用Fokker-Planck方程分别得到了余弦信号和矩形信号调制下信噪比的解析表达式. 在此基础上, 研究了系统的尺度随机共振行为, 并讨论了非对称性、噪声强度、周期信号的振幅和耦合系数对系统尺度随机共振的影响. 结果表明, 两种情形下信噪比均是系统尺度的非单调函数, 说明在此网络系统中产生了共振现象. 关键词： 尺度随机共振 非对称双稳耦合网络系统 余弦信号 矩形信号     

Bursting synchronization in clustered neuronal networks

Neuronal networks in the brain exhibit the modular (clustered) property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intra- coupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network. Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.     

Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.     

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR.     

Nonlinear signal transduction network with multistate

Signal transduction is an important and basic mechanism to cell life activities. The stochastic state transition of receptor induces the release of signaling molecular, which triggers the state transition of other receptors. It constructs a nonlinear sigaling network, and leads to robust switchlike properties which are critical to biological function. Network architectures and state transitions of receptor affect the performance of this biological network. In this work, we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca²⁺-induced Ca²⁺ release (CICR) mechanism, where fast and slow processes are involved and the receptor has four conformational states. Three types of networks, Erdös-Rényi (ER) network, Watts-Strogatz (WS) network, and BaraBási-Albert (BA) network, are considered with different parameters. The dynamics of the biological networks exhibit different patterns at different time scales. At short time scale, the second open state is essential to reproduce the quasi-bistable regime, which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point. The pattern at short time scale is not sensitive to the network architecture. At long time scale, only monostable regime is observed, and difference of network architectures affects the results more seriously. Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.     

航空通信网络中复杂干扰过滤技术研究

航空通信环境中存在复杂的干扰信号,对正常的通信信号造成强烈的干扰。利用传统算法进行干扰信号过滤,无法避免由于干扰信号过于复杂导致过滤不充分的缺陷。提出一种基于加权残差优化算法的航空通信网络中复杂干扰过滤方法。对原始信号进行降频处理,能够消除峰值干扰信号,提高了信号的准确性。根据加权残差修正优化算法的相关原理,对通信信号进行残差优化处理,经过处理后的通信信号在受到干扰时会形成零陷,实现了对复杂干扰信号的过滤。实验结果表明,利用该算法进行航空通信网络中复杂干扰信号过滤,能够有效提高过滤的准确性,效果令人满意。     

一种具有正弦基函数权值的反馈型神经网络模型

提出了一种新的两层反馈型神经网络模型. 该网络采用正弦基函数作为权值, 神经元激活函数为线性函数, 连接形式为两层反馈型结构. 研究并定义了该反馈型神经网络的能量函数, 分析了网络运行的稳定性问题, 并证明了在Liapunov意义下网络运行的稳定性. 网络运行过程中, 其权值不做调整(但随时间按正弦规律变化), 网络状态不断地转换. 随着网络状态变化其能量不断减小, 最终在达到稳定时能量到达极小点. 由于该反馈型神经网络权值为正弦函数, 特别适合于周期信号的自适应逼近和检测, 为实际中周期性信号检测与处理提供了一种新的、有效的网络模型和方法. 作为应用实例把该网络应用于电力系统中电压凹陷特征量实时检测, 仿真结果表明, 网络用于信号检测不仅有很高的静态精度, 而且有非常好的动态响应特性.