Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.

Restoration of the focal parameters for an extreme-power laser pulse with ponderomotively scattered proton spectra by using a neural network algorithm

A neural network-based approach is proposed both for reconstructing the focal spot intensity profile and for estimating the peak intensity of a high-power tightly focused laser pulse using the angular energy distributions of protons accelerated by the pulse from rarefied gases.For these purposes,we use a convolutional neural network architecture.Training and testing datasets are calculated using the test particle method,with the laser description in the form of Stratton-Chu integrals,which model laser pulses focused by an off-axis parabolic mirror down to the diffraction limit.To demonstrate the power and robustness of this method,we discuss the reconstruction of axially symmetric intensity profiles for laser pulses with intensities and focal diameters in the ranges of 10^(21)-10^(23) W cm^(−2) and ~(1-4)λ,respectively.This approach has prospects for implementation at higher intensities and with asymmetric laser beams,and it can provide a valuable diagnostic method for emerging extremely intense laser facilities.

Artificial neural network algorithm for pulse shape discrimination in 2πα and 2πβ particle surface emission rate measurements

To enhance the accuracy of 2πα and 2πβ particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network(ANN) algorithms: back-propagation(BP) and genetic algorithm-based back-propagation(GA-BP). These algorithms classify pulse signals from distinct α and β particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing α and β particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2πα and 2πβ particles. The BP neural network realizes approximately 99% accuracy for 2πα particles and approximately 95% for 2πβ particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing β particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2πα and 2πβ surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Study on the Classification of Pulse Signal Based on the BP Neural Network

The objectification of the pulse signal analysis is a practical problem. The classification of the pulse signal is studied based on the BP neural network. It is first analyzed how to select the characteristic factors of the pulse signal. Then the method of nondimensionalization/normalization on the pulse signal is presented to preprocess the characteristic factors. The classification of the pulse signal and the effects of the selection of characteristic factors are studied by using the normalized data and BP neural network. It is shown that nondimensionalization/normalization of the data is in favor of the training and forecasting of the network. The selection of characteristic factors affects the accuracy of forecasting obviously. The results of forecasting by selection of 8, 6 and 4 factors respectively show that the less the factors are, the worse the effects are.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

FPGA implementation of neural network accelerator for pulse information extraction in high energy physics

Extracting the amplitude and time information from the shaped pulse is an important step in nuclear physics experiments.For this purpose,a neural network can be an alternative in off-line data processing.For processing the data in real time and reducing the off-line data storage required in a trigger event,we designed a customized neural network accelerator on a field programmable gate array platform to implement specific layers in a convolutional neural network.The latter is then used in the front-end electronics of the detector.With fully reconfigurable hardware,a tested neural network structure was used for accurate timing of shaped pulses common in front-end electronics.This design can handle up to four channels of pulse signals at once.The peak performance of each channel is 1.665 Giga operations per second at a working frequency of 25 MHz.

Deinterleaving of radar pulse based on implicit feature

In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

Nonsubsampled contourlet 变换(NSCT ) 为图象提供灵活 multiresolution， anisotropy，和方向性的扩大。与原来的 contourlet 变换相比，它是移动不变的并且能在奇特附近克服 pseudo-Gibbs 现象。脉搏联合了神经网络(PCNN ) 是一个视觉启发外皮的神经网络并且由全球联合和神经原的脉搏同步描绘。它为图象处理被证明合适并且成功地在图象熔化采用。在这份报纸， NSCT 与 PCNN 被联系并且在图象熔化使用了充分利用他们的特征。在 NSCT 领域的空间频率是输入与大开火的时间在 NSCT 领域激发 PCNN 和系数作为熔化图象的系数被选择。试验性的结果证明建议算法超过典型基于小浪，基于 contourlet，基于 PCNN，并且 contourlet-PCNN-based 熔化算法以客观标准和视觉外观。

Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G

Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.

Neural Network Modeling and System Simulating for the Dynamic Process of Varied Gap Pulsed GTAW with Wire Filler

As the base of the research work on the weld shape control during pulsed gas tungsten arc welding （GTAW） with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length Lt, maximum width Wt and half-length ratio Rh1 were selected to depict topside weld pool shape, and were measured on-line by vision sensing. A dynamic neural network model was constructed to predict the usually unmeasured backside width and topside height of the weld through topside shape parameters and welding parameters. The inputs of the model were the welding parameters （peak current, pulse duty ratio, welding speed, filler rate）, the joint gap, the topside pool shape parameters （Lt, Wt, and Rh1）, and their history values at two former pulse, a total of 24 numbers. The validating experiment results proved that the artificial neural network （ANN） model had high precision and could be used in process control. At last, with the developed dynamic model, steady and dynamic behavior was analyzed by simulation experiments, which discovered the variation rules of weld pool shape parameters under different welding parameters, and further knew well the characteristic of the welding process.

Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network

To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.

Self-learning fuzzy neural network control for backside width of weld pool in pulsed GTAW with wire filler

The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNNC) for backside width of weld pool in pulsed gas tungsten arc welding (GTAW) with wire filler was designed. In FNNC, the fuzzy system was expressed by an equivalence neural network, the membership functions and inference rulers were decided through the learning of the neural network. Then, the FNNC control arithmetic was analyzed, simulating experiment was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were implemented. The maximum error between the real value and the given one was 0.39mm, the mean error was 0.014mm, and the root-mean-square was 0.14mm. The real backside width was maintained around the given value. The results show that the self-learning fuzzy neural network control strategy can achieve a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

Linear Pulse-Coupled Oscillators Model¬—A New Approach for Time Synchronization in Wireless Sensor Networks

Mutual synchronization is a ubiquitous phenomenon that exists in various natural systems. The individual participants in this process can be modeled as oscillators, which interact by discrete pulses. In this paper, we analyze the synchronization condition of two- and multi-oscillators system, and propose a linear pulse-coupled oscillators model. We prove that the proposed model can achieve synchronization for almost all conditions. Numerical simulations are also included to investigate how different model parameters affect the synchronization. We also discuss the implementation of the model as a new approach for time synchronization in wireless sensor networks.

High current pulse forming network switched by static induction thyristor

A high-current pulse forming network (PFN) has been developed for applications to artificial solar-wind generation. It is switched by staticinduction thyristor (SIThy) and is capable of generating pulsed current of ~9.7 kA for a time duration of ~1 ms. The SIThy switch module ismade that it can be controlled by an optical signal and it can be operated at elevated electrical potential. The experiments reported in this paperused two switch modules connected in series for maximum operating voltage of 3.5 kV. The experimental results have demonstrated a pulsedhigh-current generator switched by semiconductor devices, as well as the control and operation of SIThy for pulsed power application.