Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

Coexisting fast–slow dendritic traveling waves in a 3D-array electric field coupled neuronal network

Coexistence of fast and slow traveling waves without synaptic transmission has been found in hhhippocampal tissues,which is closely related to both normal brain activity and abnormal neural activity such as epileptic discharge. However, the propagation mechanism behind this coexistence phenomenon remains unclear. In this paper, a three-dimensional electric field coupled hippocampal neural network is established to investigate generation of coexisting spontaneous fast and slow traveling waves. This model captures two types of dendritic traveling waves propagating in both transverse and longitude directions: the N-methyl-D-aspartate(NMDA)-dependent wave with a speed of about 0.1 m/s and the Ca-dependent wave with a speed of about 0.009 m/s. These traveling waves are synaptic-independent and could be conducted only by the electric fields generated by neighboring neurons, which are basically consistent with the in vitro data measured experiments. It is also found that the slow Ca wave could trigger generation of fast NMDA waves in the propagation path of slow waves whereas fast NMDA waves cannot affect the propagation of slow Ca waves. These results suggest that dendritic Ca waves could acted as the source of the coexistence fast and slow waves. Furthermore, we also confirm the impact of cellular spacing heterogeneity on the onset of coexisting fast and slow waves. The local region with decreasing distances among neighbor neurons is more liable to promote the onset of spontaneous slow waves which, as sources, excite propagation of fast waves. These modeling studies provide possible biophysical mechanisms underlying the neural dynamics of spontaneous traveling waves in brain tissues.

A physics-informed neural network for simulation of finite deformation in hyperelastic-magnetic coupling problems

Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite deformation problems.In this paper,a PINN framework for tackling hyperelastic-magnetic coupling problems is proposed.Since the solution space consists of two-phase domains,two separate networks are constructed to independently predict the solution for each phase region.In addition,a conscious point allocation strategy is incorporated to enhance the prediction precision of the PINN in regions characterized by sharp gradients.With the developed framework,the magnetic fields and deformation fields of magnetorheological elastomers(MREs)are solved under the control of hyperelastic-magnetic coupling equations.Illustrative examples are provided and contrasted with the reference results to validate the predictive accuracy of the proposed framework.Moreover,the advantages of the proposed framework in solving hyperelastic-magnetic coupling problems are validated,particularly in handling small data sets,as well as its ability in swiftly and precisely forecasting magnetostrictive motion.

An improved pulse coupled neural networks model for semantic IoT

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network

The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Application of the N + 2 Transversal Network Method to the Study of a Coupled Resonator Filter

This paper presents a new approach to synthesize admittance function polynomials and coupling matrices for coupled resonator filters. The N + 2 transversal network method is applied to study a coupled resonator filter. This method allowed us to determine the polynomials of the reflection and transmission coefficients. A study is made for a 4 poles filter with 2 transmission zeros between the N + 2 transversal network method and the one found in the literature. A MATLAB code was designed for the numerical simulation of these coefficients for the 6, 8, and 10 pole filter with 4 transmission zeros.

Stability and multistability of synchronization in networks of coupled phase oscillators

Coupled phase oscillators usually achieve synchronization as the coupling strength among oscillators is increased beyond a critical value. The stability of synchronous state remains an open issue. In this paper, we study the stability of the synchronous state in coupled phase oscillators. It is found that numerical integration of differential equations of coupled phase oscillators with a finite time step may induce desynchronization at strong couplings. The mechanism behind this instability is that numerical accumulated errors in simulations may trigger the loss of stability of the synchronous state.Desynchronization critical couplings are found to increase and diverge as a power law with decreasing the integral time step. Theoretical analysis supports the local stability of the synchronized state. Globally the emergence of synchronous state depends on the initial conditions. Other metastable ordered states such as twisted states can coexist with the synchronous mode. These twisted states keep locally stable on a sparse network but lose their stability when the network becomes dense.

Quasi-synchronization of fractional-order complex networks with random coupling via quantized control

We investigate the quasi-synchronization of fractional-order complex networks(FCNs) with random coupling via quantized control. Firstly, based on the logarithmic quantizer theory and the Lyapunov stability theory, a new quantized feedback controller, which can make all nodes of complex networks quasi-synchronization and eliminate the disturbance of random coupling in the system state, is designed under non-delay conditions. Secondly, we extend the theoretical results under non-delay conditions to time-varying delay conditions and design another form of quantization feedback controller to ensure that the network achieves quasi-synchronization. Furthermore, the error bound of quasi-synchronization is obtained.Finally, we verify the accuracy of our results using two numerical simulation examples.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

Coupled CUBIC Congestion Control for MPTCP in Broadband Networks

Recently,multipath transmission control protocol(MPTCP)was standardized so that data can be transmitted through multiple paths to utilize all available path bandwidths.However,when high-speed long-distance networks are included in MPTCP paths,the traffic transmission performance of MPTCP is severely deteriorated,especially in case the multiple paths’characteristics are heavily asymmetric.In order to alleviate this problem,we propose a“Coupled CUBIC congestion control”that adopts TCP CUBIC on a large bandwidth-delay product(BDP)path in a linked increase manner for maintaining fairness with an ordinary TCP traversing the same bottleneck path.To verify the performance excellence of the proposed algorithm,we implemented the Coupled CUBIC Congestion Control into Linux kernels by modifying the legacy MPTCP linked-increases algorithm(LIA)congestion control source code.We constructed asymmetric heterogeneous network testbeds mixed with large and small BDP paths and compared the performances of LIA and Coupled CUBIC by experiments.Experimental results show that the proposed Coupled CUBIC utilizes almost over 80%of the bandwidth resource in the high BDP path,while the LIA utilizes only less than 20%of the bandwidth for the same path.It was confirmed that the resource utilization and traffic transmission performance have been greatly improved by using the proposed Coupled CUBIC in high-speed multipath networks,as well as maintaining MPTCP fairness with competing single-path CUBIC or Reno TCP flows.

Improvement of atmospheric jet-array plasma uniformity assisted by artificial neural networks

Atmospheric pressure plasma jet(APPJ)arrays have shown a potential in a wide range of applications ranging from material processing to biomedicine.In these applications,targets with complex three-dimensional structures often easily affect plasma uniformity.However,the uniformity is usually crucially important in application areas such as biomedicine,etc.In this work,the flow and electric field collaborative modulations are used to improve the uniformity of the plasma downstream.Taking a two-dimensional sloped metallic substrate with a 10°inclined angle as an example,the influences of both flow and electric field on the electron and typical active species distributions downstream are studied based on a multi-field coupling model.The electric and flow fields modulations are first separately applied to test the influence.Results show that the electric field modulation has an obvious improvement on the uniformity of plasma while the flow field modulation effect is limited.Based on such outputs,a collaborative modulation of both fields is then applied,and shows a much better effect on the uniformity.To make further advances,a basic strategy of uniformity improvement is thus acquired.To achieve the goal,an artificial neural network method with reasonable accuracy is then used to predict the correlation between plasma processing parameters and downstream uniformity properties for further improvement of the plasma uniformity.An optional scheme taking advantage of the flexibility of APPJ arrays is then developed for practical demands.

热液成矿系统构造控矿理论

构造对成矿的控制是热液成矿系统的典型特征之一,系统剖析多重尺度控矿构造的几何学、运动学、动力学、流变学和热力学对认识矿床成因和预测找矿至关重要;而如何实现控矿构造格架、渗透性结构、成矿流体通道和矿化变形网络由静态到多尺度时-空四维动态的转变,查明流体通道和矿床增量生长过程与控制因素,揭示热液成矿系统的构造-流体耦合成矿机制和定位规律是亟待解决的关键科学难题。为此,我们在对已有相关成果系统梳理的基础上,提出了科学构建热液成矿系统构造控矿理论的基本要点与对应方法及应用范畴:(1)流体而非构造是构造控矿理论的中心,热液系统的流体流动与成矿作用受控于断裂带格架及其渗透性结构,其中渗透率是将流体流动与流体压力变化联系起来理解控矿构造的核心;(2)不同控矿构造组合的关键控制是构造差应力和流体压力的大小,而矿化类型的变化可能是由于构造应力场引起的容矿构造方位的不同和赋矿围岩之间的强度差异所致;(3)流体通道的生长始于超压流体储库上游围岩中孤立的微裂隙沿流体压力梯度最大的方向、随裂隙发育且相互连结而形成新的长裂隙,并最终连通形成断裂网络内的流体通道,矿床的增量生长发生在高流体通量的短爆发期,断层反复滑动驱动其内流体压力、流速和应力快速变化,当由此诱发的流体通道生长破坏了流体系统的动态平衡时,随之而来的流体快速降压就成为金属沉淀成矿的关键驱动因素;(4)以热液裂隙-脉系统野外地质观测和构造-蚀变-矿化网络三维填图为基础,通过宏观与微观各级控矿构造相结合、地质历史与构造应力分析相结合、局部与区域点-线-面相结合、浅部与深部相结合、时间与空间相结合、定性和定量相结合,对各种控矿因素开展多学科、多尺度、多层次、全方位综合研究,是应遵循的基本原则;(5)通过构造-蚀变-矿化网络填图,将蚀变-矿化体与控矿构造的类型、形态、规模、产状和间距等几何学特征联系起来,利用热液裂隙-脉系统和断裂网络拓扑学及矿体三维几何结构分析等定量方法查明控矿构造格架和渗透性结构并揭示矿化变形网络的连通性与成矿潜力;(6)合理构建地质模型,选取合适的热力学参数和动力学边界条件,利用HCh和COMSOL等方法,定量模拟成矿过程中的流体流动、热-质传递、应力变形和化学反应等的时-空变化,是揭示构造-流体耦合成矿机理和定位规律、预测矿化中心和确定找矿目标的有效途径。进而提出了构造控矿理论的研究流程:聚焦构造-流体耦合成矿机制和定位规律这一关键科学问题,选择热液裂隙-脉系统和构造-蚀变-矿化网络为重点研究对象;通过几何学描述、运动学判断、流变学分析、动力学解析和热力学综合,厘定控矿构造格架,定位矿化中心,示踪成矿流体通道和多种矿化样式的增量生长过程及其关键控制,揭示渗透性结构的时-空演变规律及构造再活化与成矿定位的成因关联,建立构造-流体耦合成矿模式,服务新一轮战略找矿突破。以胶东焦家金矿田为例,开展控矿构造理论研究和成矿预测应用实践,证实了其科学性和有效性。

基于频率切换实现恒流/恒压输出的电场耦合无线电能传输系统

电场耦合无线电能传输(electric-field coupled wireless power transfer,EC-WPT)技术的实际应用中,有些用电设备需要系统具有不同的恒定输出特性(恒流/恒压),即系统输出电压或输出电流与负载解耦,此外,系统还需具备在恒流、恒压模式间按需切换的功能。针对该需求,基于LC-CLC谐振网络提出1种具有恒流/恒压输出特性的EC-WPT系统,分析LC-CLC谐振网络特性,推导恒流/恒压输出特性的实现条件以及恒流频率和恒压频率的计算方法,并给出系统参数设计方法,分析系统对工作频率的敏感性。最后通过仿真和实验验证所提出的EC-WPT系统恒流/恒压输出特性及其参数设计方法的正确性和有效性。实验结果表明所提出的系统在输入电压恒定的不同负载工况下分别实现2 A的恒流输出以及96 V的恒压输出,其最大传输效率分别为87.83%及88.17%。

服务创新网络中企业社会资本如何影响创新绩效——知识流耦合与知识共创的链式中介作用

基于社会网络理论与知识基础观,构建“企业社会资本—知识流耦合—知识共创—服务创新绩效”的链式中介模型,运用层次回归分析法对405份企业样本进行分析。研究发现:结构性、认知性、关系性社会资本均对服务创新绩效有显著正向影响,且影响程度依次减弱;知识流耦合、知识共创在企业社会资本与服务创新绩效之间起链式中介作用。研究可进一步夯实创新网络、社会资本理论基础,揭示企业社会资本通过知识作用路径影响服务创新绩效的内在机制,为服务企业提高创新绩效提供参考。

基于漏斗函数双电机伺服系统跟踪与同步控制

针对双电机驱动伺服系统具有未知非线性的问题,提出一种基于漏斗函数的跟踪与同步控制方案。首先,利用神经网络逼近和补偿复杂的非线性,在此基础上,引入滤波技术解决传统反步控制的“计算爆炸”问题,同时引入非光滑漏斗误差面确保系统的状态量被约束在预定义的漏斗边界内,结合改进的漏斗函数和反步设计技术设计了一种自适应量化漏斗跟踪控制方案。为了同时保证双电机的同步运行,同步控制器采用了平均偏差耦合策略,实现了双电机伺服系统的跟踪与同步控制。仿真结果表明,该方法可以实现对负载的跟踪以及双电机的同步。

基于多层耦合网络的装备保障体系级联失效及抗毁性分析

为准确描述装备保障体系结构特征与毁伤失效流程,进而提升装备保障体系功能与抗毁能力,基于杀伤层、保障层和储供层构建装备保障体系的多层耦合网络模型。基于杀伤链给出装备维修保障链与储供保障链的特征和典型样式,在此基础上,提出考虑装备保障体系多层耦合网络特性的级联失效模型与节点重要度评估方法。给出对应的网络抗毁性评估指标。仿真结果表明,考虑级联失效的多层耦合网络模型能较好地反映遭受攻击时整个装备保障体系的性能变化情况,所提出的使命任务节点重要度能准确识别关键节点。与传统网络抗毁性指标相比,杀伤链数量对节点失效产生的装备保障体系抗毁性变化更加敏感。

大规模复杂换热网络联动进化障碍分析及策略改进

针对启发式算法在优化大规模换热网络时出现优化精细度不足的问题,提出一种以小负荷公用工程为导向的换热单元耦合联动强制进化策略。该策略以小负荷公用工程作为导向,构建具有耦合关系的换热单元环路,并对环路内的换热单元热负荷进行联动调整,同时保持环路外换热单元的热负荷和流股匹配不变。该策略旨在最大程度回收流股的能量,以减少小负荷公用工程使用带来的费用增加。将策略应用于强制进化随机游走算法并对两个算例进行测试,算例H8C7和H10C10的优化结果分别为1495292$/a和1713637$/a。与改进前相比,分别降低了7466$/a和7533$/a;与文献中最优解相比,分别减少了2033$/a和1450$/a,证明了该策略的有效性。

基于“生态-气候适应性-游憩”多功能耦合的复合绿地生态网络格局优化

城市生态环境问题日益突出,如何构建多功能耦合的绿地生态网络格局是促进人居环境可持续发展的重要议题。以成都市为研究区,基于“源-汇”理论,综合运用形态学空间格局分析(Morphological Spatial Pattern Analysis,MSPA)、景观连通性指数、电路理论等方法对生物多样性保护、热环境改善、游憩服务构建单因子网络,分析源地、廊道、生态战略点等空间要素特征,形成基于“生态-气候适应性-游憩”多层级、多功能复合生态网络优化格局。结果表明:(1)筛选出“源”-“汇”景观(生态159—29个,气候适应性30—14个,游憩208—40个)。生物迁徙廊道在研究区中部、西南部呈团簇状分布;气候适应性廊道呈中部集聚、东西稀疏的分布特征;游憩廊道相对密集,以中心城区为核心向四周扩散。(2)多层级“源-汇”景观网络中,总长度上,游憩廊道>生物迁徙廊道>气候适应性廊道;总面积上,生物迁徙廊道>游憩廊道>气候适应性廊道。一、二级廊道的适宜宽度为生态廊道200、100m,气候适应性廊道100、60m;游憩廊道60、30m。(3)叠加多目标廊道获得生态关键点753个,干扰点2371个。游憩源地中8.18%的面积应退让给生态廊道和气候适应性廊道。多功能耦合的生态网络优化格局可激活不同景观资源的潜在价值,对协调生态保护与城市发展的双向需求,实现绿地资源在城市空间中的最优配置具有重要意义。

基于最优经济运行区域的主动配电网日前日内协同调度方法

传统的日前-日内协同调度通常以与日前时序计划曲线偏差最小作为日内目标函数,当日内新能源出力预测值与日前相差较大时,储能装置(energy storage systems,ESS)等由于其时间耦合约束日内调整范围有限,导致经济性和灵活性较差。对此,提出了基于最优经济运行区域(optimal economic operation region,OEOR)的主动配电网(active distribution networks,ADN)日前-日内协同调度方法。在日前阶段,构建线性化ADN调度模型,基于拉丁超立方采样法生成的大量随机场景下调控设备优化曲线,以全时间段内设备出力上下界内所包含的随机场景优化结果数量最大为目标,考虑储能装置荷电状态的相邻时段约束和微型燃气轮机的爬/滑坡率,构建OEOR生成模型。最后,在日内阶段,调控设备在OEOR内滚动优化调整,当该时段优化值贴近OEOR边界时,考虑相邻时段出力约束,将OEOR扩展为最优经济极限运行区域(E-OEOR)。算例结果表明,所提方法相比于传统方法能够更有效地提升配电网经济性。