Optimal Power Control for OFDM Signals over Two-Way Relay with Physical Network Coding

This paper describes an optimal power allocation scheme for orthogonal frequency division multiple access two-way relay networks with physical network coding. The aim is to enhance the achievable sum rate of the terminals for a constrained total transmit power. Convex optimization is used to derive a closed-form solution for the power allocation between the relay node and the two terminals. This reduces the variable dimensionality of the objective function for the power assignment problem among multiple carriers when the total transmit power is constrained. This solution is then used to derive the optimal power control scheme. This method reduces the implementation complexity compared with the traditional resource allocation scheme. Numerical and simulation results show that the approach achieves almost the optimal sum rate and outperforms the fixed power assignment method with less computational load in various scenarios.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

ESR-PINNs:Physics-informed neural networks with expansion-shrinkage resampling selection strategies

Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.

Cognitive Intelligence Based 6G Distributed Network Architecture

5G is envisioned to guarantee high transmission rate,ultra-low latency,high reliability and massive connections.To satisfy the above requirements,the 5G architecture is designed with the properties of using service-based architecture,cloud-native oriented,adopting IT-based API interfaces and introduction of the Network Repository Function.However,with the wide commercialization of 5G network and the exploration towards 6G,the 5G architecture exposes the disadvantages of high architecture complexity,difficult inter-interface communication,low cognitive capability,bad instantaneity,and deficient intelligence.To overcome these limitations,this paper investigates 6G network architecture,and proposes a cognitive intelligence based distributed 6G network architecture.This architecture consists of a physical network layer and an intelligent decision layer.The two layers coordinate through flexible service interfaces,supporting function decoupling and joint evolution of intelligence services and network services.With the above design,the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.

Physical informed memory networks for solving PDEs:implementation and applications

With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs.Unlike the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network.Meanwhile,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling points.Finally,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are discussed.Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.

Discovering Phase Field Models from Image Data with the Pseudo-Spectral Physics Informed Neural Networks

In this paper,we introduce a new deep learning framework for discovering the phase-field models from existing image data.The new framework embraces the approximation power of physics informed neural networks(PINNs)and the computational efficiency of the pseudo-spectral methods,which we named pseudo-spectral PINN or SPINN.Unlike the baseline PINN,the pseudo-spectral PINN has several advantages.First of all,it requires less training data.A minimum of two temporal snapshots with uniform spatial resolution would be adequate.Secondly,it is computationally efficient,as the pseudo-spectral method is used for spatial discretization.Thirdly,it requires less trainable parameters compared with the baseline PINN,which significantly simplifies the training process and potentially assures fewer local minima or saddle points.We illustrate the effectiveness of pseudo-spectral PINN through several numerical examples.The newly proposed pseudo-spectral PINN is rather general,and it can be readily applied to discover other FDE-based models from image data.

A new method to solve the Reynolds equation including mass-conserving cavitation by physics informed neural networks(PINNs)with both soft and hard constraints

In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries.

The Impact of Internet of Things in Manufacturing Management

The study investigated the impact of the Internet of Things in manufacturing management. Specifically, the study examined how IoT implementation and management affect organizational efficiency in Camanov Ltd.;and to what extent IoT implementation contributes to the saving of cost and time of the organization. The research design is a survey. The population of this study consisted of all 141 staff of Camanov Ltd. Port Harcourt. Since the population is not large, the researcher conducted a census of all, and 126 staff completed a structured questionnaire. The two research questions were analyzed using simple percentages and all two hypotheses were tested using sample proportion statistics (Z test) at a 0.05 level of significance. The result showed that the Internet of Things has a significant impact on organizational efficiency in Camanov Ltd. (Z = 4.73);and that the Internet of Things significantly contributes toward saving cost and time of the organization Camanov Ltd (Z = 4.95). It was recommended that organizations should encourage training of personnel in the improved limitless possibility of information gathered from the Internet of Things framework which supports planning, budgeting and monitoring approaches, providing more reliable information to support actions, in particular in the decision-making process, to enhance productivity.

Optimal energy-efficient scheme for two-way relay channel using physical layer network coding

Systems are always designed and optimized based on full traffic load in the current literatures. However, practical systems are seldom operating at full load, even at peak traffic hours. Instead of maximizing system rate to achieve the full load, an optimal energy-efficient scheme to minimize the transmit power with required rates is investigated in this article. The considered scenario is a two-way relay channel using amplify-and-forward protocol of physical layer network coding, where two end nodes exchange messages via multiple relay nodes within two timeslots. A joint power allocation and relay selection scheme is designed to achieve the minimum transmit power. Through convex optimization theory, we firstly prove that single relay selection scheme is the most energy-efficient way for physical layer network coding. The closed-form expressions of power allocation are also given. Numerical simulations demonstrate the performance of the designed scheme as well as the comparison among different schemes.

Forced Collision:Detecting Wormhole Attacks with Physical Layer Network Coding

Previous research on security of network coding focused on the protection of data dissemination procedures and the detection of malicious activities such as pollution attacks. The capabilities of network coding to detect other attacks have not been fully explored. In this paper, we propose a new mechanism based on physical layer network coding to detect wormhole attacks. When two signal sequences collide at the receiver, the starting point of the collision is determined by the distances between the receiver and the senders. Therefore, by comparing the starting points of the collisions at two receivers, we can estimate the distance between them and detect fake neighbor connections via wormholes. While the basic idea is clear, we have proposed several schemes at both physical and network layers to transform the idea into a practical approach. Simulations using BPSK modulation at the physical layer show that the wireless nodes can effectively detect fake neighbor connections without the adoption of special hardware or time synchronization.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.

An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss function.However,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,e.g.because they fall into local optima.In other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the convergence.In addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the boundaries.Overall,these problems have made PINN-based methods of limited use on practical situations.In this paper,we propose a novel physics-informed neural network,i.e.an adaptive physics-informed neural network with a two-stage training process.Our algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal training.The pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss functions.In order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical situations.The Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden errors.Experimental results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction errors.The codes developed in this manuscript are publicy available at https://github.com/callmedrcom/ATPINN.

The prediction of external flow field and hydrodynamic force with limited data using deep neural network

Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless framework for combining the measured data with the deep neural network,making the neural network capable of executing certain physical constraints.Unlike the data-driven model to learn the end-to-end mapping between the sensor data and high-dimensional flow field,PINN need no prior high-dimensional field as the training dataset and can construct the mapping from sensor data to high dimensional flow field directly.However,the extrapolation of the flow field in the temporal direction is limited due to the lack of training data.Therefore,we apply the long short-term memory(LSTM)network and physics-informed neural network(PINN)to predict the flow field and hydrodynamic force in the future temporal domain with limited data measured in the spatial domain.The physical constraints(conservation laws of fluid flow,e.g.,Navier-Stokes equations)are embedded into the loss function to enforce the trained neural network to capture some latent physical relation between the output fluid parameters and input tempo-spatial parameters.The sparsely measured points in this work are obtained from computational fluid dynamics(CFD)solver based on the local radial basis function(RBF)method.Different numbers of spatial measured points(4–35)downstream the cylinder are trained with/without the prior knowledge of Reynolds number to validate the availability and accuracy of the proposed approach.More practical applications of flow field prediction can compute the drag and lift force along with the cylinder,while different geometry shapes are taken into account.By comparing the flow field reconstruction and force prediction with CFD results,the proposed approach produces a comparable level of accuracy while significantly fewer data in the spatial domain is needed.The numerical results demonstrate that the proposed approach with a specific deep neural network configuration is of great potential for emerging cases where the measured data are often limited.

Applying physics informed neural network for flow data assimilation

Data assimilation(DA)refers to methodologies which combine data and underlying governing equations to provide an estimation of a complex system.Physics informed neural network(PINN)provides an innovative machine learning technique for solving and discovering the physics in nature.By encoding general nonlinear partial differential equations,which govern different physical systems such as fluid flows,to the deep neural network,PINN can be used as a tool for DA.Due to its nature that neither numerical differential operation nor temporal and spatial discretization is needed,PINN is straightforward for implementation and getting more and more attention in the academia.In this paper,we apply the PINN to several flow problems and explore its potential in fluid physics.Both the mesoscopic Boltzmann equation and the macroscopic Navier-Stokes are considered as physics constraints.We first introduce a discrete Boltzmann equation informed neural network and evaluate it with a one-dimensional propagating wave and two-dimensional lid-driven cavity flow.Such laminar cavity flow is also considered as an example in an incompressible Navier-Stokes equation informed neural network.With parameterized Navier-Stokes,two turbulent flows,one within a C-shape duct and one passing a bump,are studied and accompanying pressure field is obtained.Those examples end with a flow passing through a porous media.Applications in this paper show that PINN provides a new way for intelligent flow inference and identification,ranging from mesoscopic scale to macroscopic scale,and from laminar flow to turbulent flow.

Aerodynamicmodeling using an end-to-end learning attitude dynamics network for flight control

A novel identification method of aerodynamicmodels using a physics neural network,named the attitude dynamics network,which incorporates the attitude dynamics of an aircraft without any prior aerodynamic knowledge,is proposed.Then a learning controller,which combines feedback linearization with sliding mode control,is developed by introducing the learned aerodynamicmodels.The merit of the identification method is that the aerodynamicmodels can be learned end-to-end by the physics network directly from the flight data.Consequently,the paper uses an offline scheme and an online scheme to combine the identification process and the control process.In the offline scheme,learning the aerodynamic models and controlling the aircraft compose a cascade system,whereas the online scheme,similar to Learn-to-Fly,is a parallel system.Specifically,in the offline scheme,the physics neural network is trained by sufficient offline flight data,and then the trained network is substituted into the controller.The online scheme refers to the controller making the aircraft fly to generate flight data and sending these data to the deep network at the time of training,while the deep network provides the trained aerodynamic models to the controller at other times.Simulation results show that both under nominal and disturbance aerodynamic conditions,the network trained offline with a large amount of nominal data approximate the aerodynamicmodels well.Thus,the performance of the controller reaches a good level;for the online scheme,the predictive capability of the network increases and the performance of the controller improves with more training data.

Physical neural networks with self-learning capabilities

Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out computations effectively,potentially surpassing the constraints of conventional digital neural networks.A recent advancement known as“physical self-learning”aims to achieve learning through intrinsic physical processes rather than relying on external computations.This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems.Prevailing learning strategies that contribute to the realization of physical self-learning are discussed.Despite challenges in understanding the fundamental mechanism of learning,this work highlights the progress towards constructing intelligent hardware from the ground up,incorporating embedded self-organizing and self-adaptive dynamics in physical systems.

PHYSICS INFORMED NEURAL NETWORKS (PINNs) FOR APPROXIMATING NONLINEAR DISPERSIVE PDEs

We propose a novel algorithm,based on physics-informed neural networks(PINNs)to efficiently approximate solutions of nonlinear dispersive PDEs such as the KdV-Kawahara,Camassa-Holm and Benjamin-Ono equations.The stability of solutions of these dispersive PDEs is leveraged to prove rigorous bounds on the resulting error.We present several numerical experiments to demonstrate that PINNs can approximate solutions of these dispersive PDEs very accurately.

Physics Informed Neural Network-based High-frequency Modeling of Induction Motors

The high-frequency(HF)modeling of induction motors plays a key role in predicting the motor terminal overvoltage and conducted emissions in a motor drive system.In this study,a physics informed neural network-based HF modeling method,which has the merits of high accuracy,good versatility,and simple parameterization,is proposed.The proposed model of the induction motor consists of a three-phase equivalent circuit with eighteen circuit elements per phase to ensure model accuracy.The per phase circuit structure is symmetric concerning its phase-start and phase-end points.This symmetry enables the proposed model to be applicable for both star-and delta-connected induction motors without having to recalculate the circuit element values when changing the motor connection from star to delta and vice versa.Motor physics knowledge,namely per-phase impedances,are used in the artificial neural network to obtain the values of the circuit elements.The parameterization can be easily implemented within a few minutes using a common personal computer(PC).Case studies verify the effectiveness of the proposed HF modeling method.

kεNet湍流模型研究及其在低雷诺数槽道流中的应用

我们提出了一种基于物理信息的深度学习网络(kεNet),可用于RANS方程中发现封闭的湍流模型.kεNet由一个传统的典型神经网络结构和若干个基于物理信息的方程组成,如雷诺应力方程、k方程和ε方程.以低雷诺数下的槽道流动的湍流模型的修正为例,通过训练基于物理信息的神经网络,模型参数得到了修正.修正后的湍流模型参数应用于OpenFOAM软件进行计算,能够非常好地预测Re_(τ)=5200和2000下的槽道流动.

通过融合物理神经网络重构稀疏或不完整数据流场的实用方法

高分辨率流场重构被普遍认为是实验流体力学领域的一项艰巨任务,因为测量数据在时间和空间上通常是稀疏或不完整的.具体而言,由于实验设备或测量技术的限制,某些关键区域的数据无法测量.本文提出了一种基于融合物理神经网络(PINN)的不完美数据重建流场的实用方法,该网络将已知数据与物理原理相结合.通过圆柱体的尾流作为测试算例.研究了两种不完美数据训练集,一种是不同稀疏度的速度数据,另一种是不同区域缺失的速度数据.为了加速训练收敛,本文采用了余弦退火算法以提高PINN的计算效率.计算结果表明,该方法不仅可以高精度地重建真实的速度场,而且即使在数据稀疏度达到1%或核心流动区域数据被截断的情况下,也可以精确地预测压力场.这项研究提供了令人鼓舞的结论,即PINN可以作为实验流体力学的有潜力的数据同化方法.