New Optimal Power Allocation for Bidirectional Communications in Cognitive Relay Network Using Analog Network Coding

Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information with the assistance of a relay node under interference power constraints.In order to enhance the transmit rate and maintain fairness between two source terminals,a practical 2-phase analog network coding protocol is adopted and its optimal power allocation algorithm is proposed.Numerical results verify the superiority of the proposed algorithm over the conventional direct transmission protocol and 4-phase amplify-and-forward relay protocol.

Performance analysis of amplify-and-forward cooperative diversity with multiple-antenna nodes via analog network coding

Network coding (NC), which works in the network layer, is an effective technology to improve the network throughput, by allowing the relay to encode the information from different users and ensuring the destination to retrieve the desired information. Employing network coding technique in a cooperative network can improve the network performance further. In this paper, we introduce analog network coding (ANC) to a simple two-user cooperative diversity network, which adopts amplify-and-forward (AF) mode and all nodes use multiple antennas. The impact of the number of antenna on the system achievable rate is investigated. And the bit error rate (BER) performances of the traditional relay cooperative network and the cooperative network based on analog network coding under different propagation conditions are discussed. The simulation results show that the performance of the traditional cooperative network has improved significantly due to the employ of network coding.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.

Improved RBF network application in analog circuit fault isolation

One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.

RESEARCHES ON THE STABILITIES OF ANALOG ELECTRONIC NEURAL NETWORKS

A new method for analyzing the stabilities of analog electronic neural networks ispresented.The energy functions with clear physical meaning are derived by introducing the staticequivalent circuit models,which has expanded the Tellegen Theorem for application on circuitanalysis.The method used to derive the energy functions of nets from first order differentialequations is valid for all first order continuous autonomous systems.The stability analysis ofcellular neural networks is made by the use of the stationary cocontent theorem.Some resultsare instructive for the network implementation on circuits.

Analog-Circuit Model of FGH96 Superalloy Hot Deformation Behaviors Based on Artificial Neural Network

At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.

Fault Diagnosis of Analog Circuit Based on PSO and BP Neural Network

In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.

STUDIES ON PROGRAMMING FEATURES AND METHODS OF FLOATING GATE MOSFET AS ANALOG MEMORY FOR SYNAPTIC WEIGHTS IN NEURAL NETWORKS

The features of the floating gate devices as analog memory have been investigatedexperimentally.Programming properties of the devices,compatibility and endurance of program-ming,and programming methods are presented in this paper.The results illustrate that thedevice can be used to store the analog weights for the neural networks,and the method that thestored value is adjusted continuously to approach to a given analog values is a rather practicalmethod for storing weights of neural networks.

Research on Digital and Analog Electronic Experiment Teaching Course Management based on UltraLab Network Experiment Platform

Digital circuit and analog circuit courses are basic courses for students of science and engineering universities. Among them,the practical courses are of great significance for students to master the knowledge of electronics. In order to make teachers teaching more efficiently and students studying more quickly,how to update the experimental course in teaching reform is the key point. This paper analyzing the present situation of teaching in the digital circuit and analog circuit courses,the teaching questions in universities. On the basis of it,the innovation measures of experimental teaching methods and contents are discussed. Our school tries to introduce the UltraLab network experiment platform,reform and optimize the teaching methods of related courses.And it’ s accelerating the construction and development of emerging engineering education’ s process,reducing effectively the teacher’s time for managing in equipment,improving the students’ ability to use instruments.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

数字专网的通信终端与加密组件测试系统设计

为提高数字专网的通信终端与配套加密组件测试效率,降低实装测试环境的构建、运维及人力成本,开展了基于半实物仿真的测试系统设计;对通信终端与加密组件的测试需求进行了分析,提出并实现了一种模拟数字专网的通信建链流程和加密通信业务流程的方法;采用了通用AXIe(仪器与测试高级电信计算架构的扩展)测试平台与扩展测试仪器的架构进行系统设计,实现与不同测试对象的物理接口适配、信号调理以及报文数据交互控制,为通信终端及加密组件的加密业务成功率测试、语音质量评测以及远供工作电流测量提供自动化测试技术手段;经验证,测试系统通信链路时延<100 ns,可为通信终端提供良好的语音质量评测环境,PESQ(感知语音质量评测模型)测试≥4.20,可支持加密业务成功率指标≥99.9%的无人值守测试。

模拟电子电路的两端口网络阻抗等效变换

“模拟电子技术”中的很多基本电路都可视为三端子两端口网络。在学习过程中,为有效分析其输入-输出特性,往往需要对其进行输入回路和输出回路的拆分式解耦等效分析,而阻抗参数等效变换成为一大难点。基于电路理论基础知识,对三端子两端口网络的串联式和并联式阻抗参数等效变换进行了详细推导,并将所提的参数等效变换方法应用于三个典型模拟电路的特性分析,有效简化了电路分析过程。

网络技术的若干基础问题研究

基础技术研究的缺失是下一代网络研究的最大问题,定义不明、概念不清,导致缺乏共同的研究方向。文章研讨网络的定义和组成、网络的核心功能和网络的辅助功能、互联网技术、网络功能虚拟化和融合承载网技术等五个方面的网络基础技术问题,提出一些看法和想法。

基于小波神经网络的模拟集成电路短路故障诊断方法

针对传统方法短路故障诊断效果较差的问题,本文提出基于小波神经网络的模拟集成电路短路故障诊断方法。首先重构模拟集成电路原始信号,提取短路故障信号特征,然后利用确定的故障位置通过小波神经网络识别故障类型,最后判别短路故障严重程度,实现模拟集成电路短路故障诊断。实验结果表明:所提设计方法判别的短路故障程度与目标值十分接近,具有实际应用价值。

甘草酸类制剂与核苷(酸)类似物联用对慢性乙型肝炎患者临床疗效与安全性的网状Meta分析

目的:比较与分析甘草酸类制剂与核苷(酸)类似物联用对慢性乙型肝炎(chronic hepatitis B,CHB)患者的临床疗效与安全性。方法:在Pubmed、Embase和中国知网、万方医学网、维普等数据库中,检索建库至2023年10月甘草酸类制剂与核苷(酸)类似物联用对CHB患者临床疗效与安全性的随机对照试验(randomized controlled trial)研究,采用Stata1和Revman等软件进行甘草酸类制剂与核苷(酸)类似物联用对CHB患者的临床疗效与安全性的网状Meta分析。结果:筛选后共纳入文献42篇,共涉及病例3991例;网状Meta分析结果显示,甘草酸类制剂与核苷(酸)类似物联用对CHB患者在丙氨酸转氨酶、天冬氨酸转氨酶、总胆红素等指标上的疗效均优于单用核苷(酸)类似物(P<0.05);在用药安全性方面,甘草酸类制剂与核苷(酸)类似物联用和单用核苷(酸)类似物的药物不良反应发生率经比较其差异无统计学意义(P>0.05)。结论:甘草酸类制剂与核苷(酸)类似物联用对CHB患者的疗效较为肯定,安全性也较高。

RBF神经网络在船舶模拟电路故障诊断中的应用

针对船舶模拟电路元件复杂交互,故障信号在大量的正常信号中难以凸显,故障特征提取识别难度较大的问题,提出基于RBF神经网络的船舶模拟电路故障诊断方法。由基于小波包的船舶模拟电路故障特征提取方法,以小波分解重构的方式,捕捉电路频带能量变化特征;使用基于状态转移算法优化RBF神经网络的故障诊断模型,由状态转移算法优化RBF神经网络参数,构建用于诊断电路故障的RBF神经网络模型后,学习所提取故障特征与类型之间关系,诊断新输入的船舶模拟电路输出信号故障类型。实验测试结果显示,此方法在有效捕捉船舶模拟电路故障频带能量变化特征后,对多种船舶模拟电路故障的诊断结果均未出现明显错误。

基于IMODA自适应深度信念网络的复杂模拟电路故障诊断方法

针对传统DBN在无监督训练过程中预训练耗时久、诊断精度差等问题,提出了一种基于改进多目标蜻蜓优化自适应深度信念网络(IMODA-ADBN)的模拟电路故障诊断方法。首先,根据参数更新方向的异同提出了自适应学习率,提高网络收敛速度;其次,传统DBN在有监督调优过程利用BP算法,然而BP算法存在易陷入局部最优的问题,为了改善该问题,利用改进的MODA算法取代BP算法提高网络分类精度。在IMODA算法中,添加Logistic混沌印射和基于对立跳跃以获得帕累托最优解,增加算法的多样性,提高算法的性能。在7个多目标数学基准问题上测试该算法,并与3种元启发式优化算法(MODA、MOPSO和NSGA-II)进行比较,证明了IMODA-ADBN网络模型具有稳定性。最后将IMODAADBN运用到二级四运放双二阶低通滤波器的诊断实验中,实验结果表明该方法在收敛速度快的基础上保证了分类精度,诊断率更高,能够实现高难故障的分类与定位。

一种星载数模混合阵列结构及波束形成算法

6G星地融合网络对卫星通信系统提出了高速率低时延低能耗的要求,由于星地链路信道环境复杂多变且容易受到干扰,需要研究干扰场景下的低成本可实现阵列架构及算法。针对这一问题,设计了一种数模混合阵列结构,子阵采用50%稀疏馈电以及最小二乘波束形成算法,超阵采用最小二乘与线性约束最小方差波束形成算法,并理论分析了阵列结构的可行性,证明子阵的零点能够抑制超阵的栅瓣。仿真结果表明,该结构以更低的复杂度实现了大规模阵列的效果,波束方向图指向明确且零陷深度足够,对干扰的抑制明显,获得了接近理想阵列增益的BER性能。

Adaptive neural network based sliding mode altitude control for a quadrotor UAV

Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.

仿突变生物合成调控对土曲霉C23-3次生代谢产物的影响

【目的】丁内酯I是土曲霉(Aspergillus terreus)产生的一种具有多样生物活性的小分子天然产物,对其结构多样性拓展的探索具有重要意义。以一株高产丁内酯I的海洋来源土曲霉C23-3作为基础菌株,研究丁内酯前体小分子类似物和前体合成酶抑制剂对其次生代谢的影响。【方法】以海水马铃薯液体培养基为基础培养基,以3种前体小分子类似物和3种对羟基苯丙酮酸的合成酶抑制剂作为化学诱导剂,在静置发酵条件下进行菌株的化学调控培养。采用高效液相色谱、高效液相色谱-离子阱质谱联用及基于质谱的分子网络和数据库挖掘分析次生代谢产物的产量和多样性。【结果】多数前体小分子类似物和合成酶抑制剂对丁内酯I产量均有不同程度抑制,而高浓度的前体小分子类似物3,4-二羟基苯丙酮酸、两种合成酶抑制剂联用及这三者的联用表现出了较强的抑制效果;并且在丁内酯I的合成受到强烈抑制条件下,丁内酯类、土震素类、洛伐他汀类等多类次生代谢产物的合成也下降,但一种环肽类化合物产量超过10倍的大幅提升,可能是菌体对作为群体感应信号及全局性转录因子lae A诱导子的丁内酯I合成受到压制胁迫的一种应激反应,具体机制有待深入研究。【结论】丁内酯I的前体确为对羟基苯丙酮酸,3,4-二羟基苯丙酮酸及对羟基苯丙酮酸合成酶抑制剂可用作抑制丁内酯I合成的小分子工具,以用于仿突变生物合成的进一步探索以获得多样化丁内酯衍生物,也可诱导产生环肽类化合物。