A graph neural network approach to the inverse design for thermal transparency with periodic interparticle system

Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and intriguing.Our earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for manipulating thermal transport behavior and achieving thermal transparency.In this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a thermal metamaterial-based periodic interparticle system with the desired thermal transport behavior.Our work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.

Analysis and Optimization on Partition-Based Caching and Delivery in Satellite-Terrestrial Edge Computing Networks

As a viable component of 6G wireless communication architecture,satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial networks.In the paper,we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul load.Focusing on the interference effect from varied nodes in different geographical distances,we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic geometry.Considering the constraint of nodes cache space and file sets parameters,we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical user.The complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization(PSO)method and greedy based multiple knapsack choice problem(MKCP)optimization method.Numerical results show that compared with the terrestrial only cache strategy,Ground Popular Strategy,Satellite Popular Strategy,and Independent and identically distributed popularity strategy,the performance of the proposed scheme improve by 30.5%,9.3%,12.5%and 13.7%.

High Stable and Accurate Vehicle Selection Scheme Based on Federated Edge Learning in Vehicular Networks

Federated edge learning(FEEL)technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users.In the FEEL system,vehicles upload data to the edge servers,which train the vehicles’data to update local models and then return the result to vehicles to avoid sharing the original data.However,the cache queue in the edge is limited and the channel between edge server and each vehicle is time-varying.Thus,it is challenging to select a suitable number of vehicles to ensure that the uploaded data can keep a stable cache queue in edge server while maximizing the learning accuracy.Moreover,selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training,which further affects the model accuracy.In this paper,we propose a vehicle selection scheme,which maximizes the learning accuracy while ensuring the stability of the cache queue,where the statuses of all the vehicles in the coverage of edge server are taken into account.The performance of this scheme is evaluated through simulation experiments,which indicates that our proposed scheme can perform better than the known benchmark scheme.

Incremental Learning of Radio Modulation Classification Based on Sample Recall

Radio modulation classification has always been an important technology in the field of communications.The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks.In this paper,we propose a sample memory and recall framework for incremental learning of radio modulation classification.For data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for memorizing.We compare the performance of our proposed method with three baselines through a large number of simulation experiments.Results show that our method achieves far higher classification accuracy than finetuning method and feature extraction method.Furthermore,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

Cooperative Content Caching and Delivery in Vehicular Networks: A Deep Neural Network Approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network.As a promising alternative,cooperative content caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and limited caching capacities.In addition,the high mobility of vehicles renders the more complicated caching environment.Therefore,performing efficient cooperative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communication link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is formulated.Furthermore,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition

The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.

亚言语的理论与应用

成晓光,辽 宁 师 范 大

一种面向无线体域网的功率优化路由算法

由于无线体域网传感器节点的能量有限,所以优化体域网的路由、降低网络能耗成为路由算法设计的关键。针对无线体域网中路由算法的能耗问题,文中提出了一种基于功率控制的路由优化算法(Power Control-AODV,PC-AODV)。算法采用节点独立性传输链路,将能耗问题拟合为节点的发射功率,剩余能量和路径跳数的函数,进行建模研究,仿真结果验证,对比AODV算法,PC-AODV算法能够降低无线体域网的能耗,有效延长网络的生存期。

能量感知休眠的ELSS算法在WSN中的优化研究

针对无线传感网络中簇头传感器能耗相对过大导致网络整体生存时间下降的问题,提出一种根据剩余能量选择休眠簇头的算法-ELSS。算法根据区域中心原则一次性选定工作簇头和休眠簇头,在工作过程中,当工作簇头剩余能量低于网络中所有节点平均能量时,工作簇头通过感知休眠簇头剩余能量多少选择能量最高者来替换其进行工作。依此反复,形成簇头组内部轮循工作机制,以分散簇头传感器相对集中的能耗,从而延长网络整体生存时间。仿真表明,ELSS算法相比于LEACH协议和S_LEACH协议能有效延长网络生命周期。

GaN基光子晶体LED的Purcell效应

针对目前商用LED光源带宽较窄的问题,我们引入光子晶体结构提高LED的调制带宽。首先使用时域有限差分法(FDTD)仿真LED外延结构对带宽的影响,然后在正装结构中引入光子晶体结构,并仿真光子晶体的周期、高度和占空比对带宽的影响,最后实验上制备了周期600 nm,占空比0.58的光子晶体LED。仿真结果表明:引入光子晶体结构的LED,带宽可以得到有效提升;当光子晶体周期为600 nm,占空比0.5时,Purcell因子为1.2,相比普通平片LED,带宽提高8%。实验结果显示:光子寿命减小5.9%,Purcell因子达到1.06。在普通平面LED中引入光子晶体结构,可以提高光源的调制带宽。

Optimal Power Allocation for Multiuser OFDM-Based Cognitive Heterogeneous Networks

Heterogeneous network(HetNet) as a promising technology to improve spectrum efficiency and system capacity has been concerned by many scholars, which brings huge challenges for power allocation and interference management in multicell network structures. Although some works have been done for power allocation in heterogeneous femtocell networks, most of them focus centralized schemes for single-cell network under interference constraint of macrocell user. In this paper, a sum-rate maximization based power allocation algorithm is proposed for a downlink cognitive Het Net with one macrocell network and multiple microcell networks. The original power allocation optimization problem with the consideration of cross-tier interference constraint, maximum transmit power constraint of microcell base station and inter-cell interference of microcell networks is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results demonstrate the performance and effectiveness of the proposed algorithm by comparing with the equal power allocation scheme.

Constructive Approximation by Superposition of Sigmoidal Functions

In this paper, a constructive theory is developed for approximating func- tions of one or more variables by superposition of sigmoidal functions. This is done in the uniform norm as well as in the L^p norm. Results for the simultaneous approx- imation, with the same order of accuracy, of a function and its derivatives （whenever these exist）, are obtained. The relation with neural networks and radial basis func- tions approximations is discussed. Numerical examples are given for the purpose of illustration.

Three-Dimensional Planning of Arrival and Departure Route Network Based on Improved Ant-Colony Algorithm

In order to improve safety,economy efficiency and design automation degree of air route in terminal airspace,Three-dimensional(3D)planning of routes network is investigated.A waypoint probability search method is proposed to optimize individual flight path.Through updating horizontal pheromones by negative feedback factors,an antcolony algorithm of path searching in 3Dterminal airspace is implemented.The principle of optimization sequence of arrival and departure routes is analyzed.Each route is optimized successively,and the overall optimization of the whole route network is finally achieved.A case study shows that it takes about 63 sto optimize 8arrival and departure routes,and the operation efficiency can be significantly improved with desirable safety and economy.

Adaptive coupled synchronization of non-autonomous systems in ring networks

The adaptive coupled synchronization method for non-autonomous systems is proposed. This method can avoid estimating the value of coupling coefficient. Under the uniform Lipschitz assumption, we derive the asymptotical synchronization for a general coupling ring network with N identical non-autonomous systems~ even when N is large enough. Strict theoretical proofs are given. Numerical simulations illustrate the effectiveness of the present method.

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China＇s mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.

Synchronization in a fractional-order dynamic network with uncertain parameters using an adaptive control strategy

This paper studies synchronization of all nodes in a fractional-order complex dynamic network. An adaptive control strategy for synchronizing a dynamic network is proposed. Based on the Lyapunov stability theory, this paper shows that tracking errors of all nodes in a fractional-order complex network converge to zero. This simple yet prac- tical scheme can be used in many networks such as small-world networks and scale-free networks. Unlike the existing methods which assume the coupling configuration among the nodes of the network with diffusivity, symmetry, balance, or irreducibility, in this case, these assumptions are unnecessary, and the proposed adaptive strategy is more feasible. Two examples are presented to illustrate effectiveness of the proposed method.

Environmental Sound Event Detection in Wireless Acoustic Sensor Networks for Home Telemonitoring

In this paper, we present an approach to improve the accuracy of environmental sound event detection in a wireless acoustic sensor network for home monitoring. Wireless acoustic sensor nodes can capture sounds in the home and simultaneously deliver them to a sink node for sound event detection. The proposed approach is mainly composed of three modules, including signal estimation, reliable sensor channel selection, and sound event detection. During signal estimation, lost packets are recovered to improve the signal quality. Next, reliable channels are selected using a multi-channel cross-correlation coefficient to improve the computational efficiency for distant sound event detection without sacrificing performance. Finally, the signals of the selected two channels are used for environmental sound event detection based on bidirectional gated recurrent neural networks using two-channel audio features. Experiments show that the proposed approach achieves superior performances compared to the baseline.

解运输问题的减运价法

解运输问题的减运价法符秀华在经济活动中，常常遇到各类物资的运输问题。这些问题可以分为产销平衡与产销不平衡两大类。产销不平衡的运输问题可以转化成产销平衡的运输问题来解决。对产销平衡的运输问题，人们就如何寻找总运费最小的调运方案（又称为最优解）作了大量的...

The Nitrogen-Cycling Network of Bacterial Symbionts in the Sponge Spheciospongia vesparium

The microbes associated with sponges play important roles in the nitrogen cycle of the coral reefs ecosystem,e.g.,nitrification,denitrification,and nitrogen fixation.However,the whole nitrogen-cycling network has remained incomplete in any individual sponge holobiont.In this study,454 pyrosequencing of the 16S rRNA genes revealed that the sponge Spheciospongia vesparium from the South China Sea has a unique bacterial community(including 12 bacterial phyla),dominated particularly by the genus Shewanella(order Alteromonadales).A total of 10 functional genes,nifH,amoA,narG,napA,nirK,norB,nosZ,ureC,nrfA,and gltB,were detected in the microbiome of the sponge S.vesparium by gene-targeted analysis,revealing an almost complete nitrogen-cycling network in this sponge.Particularly,bacterial urea utilization and the whole denitrification pathway were highlighted.MEGAN analysis suggests that Proteobacteria(e.g.,Shewanella)and Bacteroidetes(e.g.,Bizionia)are probably involved in the nitrogen cycle in the sponge S.vesparium.