Antenna design for a massive multiple input environmental sensor network

This article describes the design and simulation of a pair of antennas on a small PCB with minimal coupling for a massive multiple input sensor network. The two antennas are planar inverted-F antennas （PIFA） that are fed with microstrip feed lines. The critical design factors are minimizing mass while creating ISM band and GPS L1 band antennas and developing data transmission schemes for maximum usage of all communication channels. The designed board is a 60 mm diameter, 0.6 mm thick circular FR4 board that weighs approximately 5 g.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.

Neural Network Based Scheduling for Variable-Length Packets in Gigabit Router with Crossbar Switch Fabric and Input Queuing

A high-speed and effective packet scheduling method is crucial to the performance of Gigabit routers. The paper studies the variable-length packet scheduling problem in Gigabit router with crossbar switch fabric and input queuing, and a scheduling method based on neural network is proposed. For the proposed method, a scheduling system structure fit for the variable-length packet case is presented first, then some rules for scheduling are given. At last, an optimal scheduling method using Hopfield neural network is proposed based on the rules. Furthermore, the paper discusses that the proposed method can be realized by hardware circuit. The simulation result shows the effectiveness of the proposed method.

Nonlinear Systems Identification via an Input-Output Model Based on a Feedforward Neural Network

This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

CONVERGENCE OF ONLINE GRADIENT METHOD WITH A PENALTY TERM FOR FEEDFORWARD NEURAL NETWORKS WITH STOCHASTIC INPUTS

Online gradient algorithm has been widely used as a learning algorithm for feedforward neural network training. In this paper, we prove a weak convergence theorem of an online gradient algorithm with a penalty term, assuming that the training examples are input in a stochastic way. The monotonicity of the error function in the iteration and the boundedness of the weight are both guaranteed. We also present a numerical experiment to support our results.

融合复制机制和input-feeding方法的中文自动摘要模型

针对中文自动摘要准确率不高的问题,在含有注意力机制的序列到序列(sequence-to-sequence,seq2seq)基础模型的解码器中融合了复制机制和input-feeding方法,提出了准确率更高的中文自动摘要模型。首先,该模型使用指针网络将出现在源序列中的OOV(out-of-vocabulary)词扩展到固定词典,以实现从源序列复制OOV词到生成序列中;其次,input-feeding方法用于跟踪已生成序列的注意力决定信息以提升模型输出准确率。在NLPCC2018数据集上的实验结果表明,与基础模型相比,所提出模型获得了更高的ROUGE得分,验证了该模型的可行性。

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input(NARX) algorithm

As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the "thermal damping effect" will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input(NARX) algorithm was used as a novel method to predict the dry-bulb temperature(Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network(ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical "damping coefficient" for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Tdat the bottom of intake shafts.

Existence of Periodic Solutions for an Output Hidden Feedback Elman Neural Network

We first recall the sufficient conditions for the existence of a periodic output of a modified Elman neural network with a periodic input found by using Mawhin’s continuation theorem of coincidence degree theory. Using this result, we obtain sufficient conditions for the existence of a periodic output for an output hidden feedback Elman neural network with a periodic input. Examples illustrating these sufficient conditions are given.

基于复杂网络分析的广东省城镇居民碳足迹研究

应用投入产出模型和复杂网络分析方法,计算了广东省城镇居民直接碳足迹、间接碳足迹,构建了城镇居民间接碳足迹复杂网络,并对碳足迹重点产业部门进行了结构分解分析。研究结果表明:(1)广东省城镇居民直接碳足迹的主要来源是汽油和液化石油气,间接碳足迹网络的核心产业部门是电力、热力的生产和供应业。(2)广东省城镇居民碳足迹的主要来源为间接碳足迹,对所构建的碳足迹网络进行整体结构和个体特征两方面的分析发现,城镇居民间接碳足迹网络的通达性逐年向好,但各产业部门之间的协同碳关联关系的发挥尚有提升空间。(3)根据产业部门在碳足迹网络中的地位和作用,运用相关指标将其划分为碳核心社区、碳中介社区、碳边缘社区,在对碳核心社区的结构分解分析中发现,碳排放强度效应均为减碳因素,消费水平效应、城镇化水平效应、人口效应为增碳因素,且消费水平效应的增碳贡献率最大。

基于MaxViT和改进几何特征点法的车载单目视觉测速方法研究

车载视觉测速技术作为自动驾驶车辆组合测速技术的重要组成,具有硬件成本低、算法拓展性强、低速下测量准确等特点,应用前景广阔。为进一步提高视觉测速算法在各类工况下的精度和鲁棒性,将几何特征点法在特征点充足时测速精度高和深度学习方法在多场景下测速稳定的优势进行结合,提出一种基于MaxViT和改进几何特征点法的车载单目视觉测速算法。该算法构建基于双输入MaxViT网络和改进几何特征点法的双通道,并行处理车载前视相机获取的连续3帧输入图像序列,滚动估计车辆当前速度,其中双输入MaxViT网络差异化提取图像不同区域的光流特征,估计当前速度所在的置信度为90%的速度区间,改进特征点法基于特征点运动计算当前速度估计值。当速度估计值落在双输入MaxViT网络估计的速度区间时,以该估计值作为实时车速测量值,否则以速度区间中值作为实时车速测量值。当算法迭代运行多帧后,将速度区间中值作为本帧速度输出以减小累积误差。使用6个车速小于40 km/h且包括加减速等工况与直弯道等场景的自建数据集进行实验验证,以理论测速精度0.1 m/s的GPS速度信号为参考速度,本文方法平均相对测速误差少于1.37%,最大相对测速误差少于6.13%。实验结果表明,提出的新方法有效提高了车载视觉测速精度与鲁棒性,可为多元车载视觉测速方法融合提供理论支撑。

制造业关联网络承压能力研究:基于投入产出表的特征分析与压力测试

数字经济快速发展对制造业关联网络带来诸多不确定性影响,制造业关联网络承压能力研究尤为重要。为应对这一形势,基于2017年、2018年和2020年投入产出表,借助产业集群三角形化方法识别并构建制造业关联网络,结合最大权树法分析制造业关联网络特征,并测试不同程度冲击对制造业关联网络产生的压力。研究发现:①化学产品是制造业关联网络的核心产业,但其核心地位呈下降趋势;②总体而言,制造业对整个关联网络的后向关联效应较强,非制造业的前向关联效应较强;③随着压力源头产业直接关联产业数量的增加,制造业关联网络所承受的压力呈下降趋势;随着压力源头产业与其它产业关联程度的下降,制造业关联网络承受的压力呈下降趋势。研究结论可为制造企业应对承压状态、促进经济高质量发展提供理论依据与实践参考。

Passivity Analysis of Impulsive Complex Networks

In this paper, we first investigate input passivity and output passivity for a class of impulsive complex networks with time-varying delays. By constructing suitable Lyapunov functionals, some input passivity and output passivity conditions are derived for the impulsive complex networks. Finally, an example is given to show the effectiveness of the proposed criteria.

基于输入均压与虚拟直流电机相结合的直流电能路由器控制策略

针对中压直流配电网接入下的直流电能路由器在新能源出力等工况下,使用传统控制策略对直流母线的控制效果一般、电压易越限等问题,基于模块化的输入串联输出并联(input-series output-parallel,ISOP)型拓扑结构,提出一种基于输入均压与虚拟直流电机相结合的直流电能路由器控制策略。首先,研究输入均压控制过程中模块间的功率均衡控制特性并与输出均流控制进行比较;接着,将虚拟直流电机控制应用到控制算法中,使变流器模拟出直流电机的惯性特性;然后,对虚拟直流电机建立小信号数学模型,分析其工作机理以及参数对系统的影响;最后,在MATLAB/Simulink中搭建仿真模型进行验证。结果表明:所提控制策略能够在实现直流电能路由器各模块间功率均衡的同时,具有类似直流电机的惯量特性与阻尼特性,可显著提高直流配电网直流母线电压稳定性。

Synthesization of high-capacity auto-associative memories using complex-valued neural networks

In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.

数字化投入对服务业贸易网络地位的影响——基于社会网络分析法

伴随着互联网、云计算、区块链等数字技术在全球范围内应用广度与深度的不断拓展,数字经济逐渐成为重塑国际贸易的主要力量。本文利用UIBE全球价值链数据库和WIOD数据库,构建2000至2014年42个国家27个服务业的国内增加值贸易网络,研究数字化投入对其在服务贸易网络中地位的影响。研究显示:服务业数字化投入有利于提高行业在服务贸易网络中的地位。从影响机制看:服务业数字化投入主要通过降低服务贸易成本、缓解资源错配、有效替代部分劳动力等渠道来提高国家在服务贸易网络中的地位。异质性结果表示:服务业数字化投入水平的提升对不同国家的影响效果存在差异,加大数字化投入更有利于提高发展中国家服务业在贸易网络中的地位;相较于国内数字化投入,服务业在贸易网络中的地位提升受国外数字化投入的影响更大;数字化投入对服务贸易网络中消费性服务业地位的提高效果更加显著。本文实证结果为中国如何在数字化浪潮中实现产业升级,提高自身在服务贸易中的地位提供了有益启示。

Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks

For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.