In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.展开更多
The concentration of biomarkers in sweat can be used to evaluate human health,making efficient sweat sensing a focus of research.While flow channel design is often used to detect sweat velocity,it is rarely incorporat...The concentration of biomarkers in sweat can be used to evaluate human health,making efficient sweat sensing a focus of research.While flow channel design is often used to detect sweat velocity,it is rarely incorporated into the sensing of biomarkers,limiting the richness of sensing results.In this study,we report a time sequential sensing scheme for uric acid in sweat through a sequential design of Tesla valve channels.Graphene electrodes for detecting uric acid and directional Tesla valve flow channels were fabricated using laser engraving technology to realize time sequential sensing.The performance of the channels was verified through simulation.The time sequential detection of uric acid concentration in sweat can help researchers improve the establishment of human health management systems through flexible wearable devices.展开更多
The growth in wireless technologies applications makes the necessity of providing a reliable communication over wireless networks become obvious.Guaranteeing real time communication in wireless medium poses a signific...The growth in wireless technologies applications makes the necessity of providing a reliable communication over wireless networks become obvious.Guaranteeing real time communication in wireless medium poses a significant challenge due to its poor delivery reliability.In this study,a recovery and redundancy model based on sequential time division multiple access(S-TDMA)for wireless communication is developed.The media access control(MAC)layer of the S-TDMA determines which station should transmit at a given time slot based on channel state of the station.Simulations of the system models were carried out using MATLAB SIMULINK software.SIMULINK blocks from the signal processing and communication block sets were used to model the communication system.The S-TDMA performance is evaluated with total link reliability,system throughput,average probability of correct delivery before deadline and system latency.The evaluation results displayed in graphs when compared with instant retry and drop of frame were found to be reliable in recovering loss packets.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
The time-scaling transformation is a widely used approach within the computational framework of control parameterization for optimizing the switching times of control variables.However,the conventional time-scaling tr...The time-scaling transformation is a widely used approach within the computational framework of control parameterization for optimizing the switching times of control variables.However,the conventional time-scaling transformation has the limitation that the switching times and the number of switches for each control component must be the same.In this paper,we present a novel technique to solve constrained optimal control problems that allows for adaptively optimizing the switching times for each control component.Numerical results demonstrate that this proposed method provides better flexibility in control strategy and yields improved performance.展开更多
Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ens...Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.展开更多
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
基金supported by the National Key R&D Program of China(No.2018YFA0108100)the National Natural Science Foundation of China(No.62104009)Experiments on human sweat were conducted in accordance with the approved protocol from the institutional review board at Peking University Third Hospital,Beijing,China(No.M2021610).
文摘The concentration of biomarkers in sweat can be used to evaluate human health,making efficient sweat sensing a focus of research.While flow channel design is often used to detect sweat velocity,it is rarely incorporated into the sensing of biomarkers,limiting the richness of sensing results.In this study,we report a time sequential sensing scheme for uric acid in sweat through a sequential design of Tesla valve channels.Graphene electrodes for detecting uric acid and directional Tesla valve flow channels were fabricated using laser engraving technology to realize time sequential sensing.The performance of the channels was verified through simulation.The time sequential detection of uric acid concentration in sweat can help researchers improve the establishment of human health management systems through flexible wearable devices.
文摘The growth in wireless technologies applications makes the necessity of providing a reliable communication over wireless networks become obvious.Guaranteeing real time communication in wireless medium poses a significant challenge due to its poor delivery reliability.In this study,a recovery and redundancy model based on sequential time division multiple access(S-TDMA)for wireless communication is developed.The media access control(MAC)layer of the S-TDMA determines which station should transmit at a given time slot based on channel state of the station.Simulations of the system models were carried out using MATLAB SIMULINK software.SIMULINK blocks from the signal processing and communication block sets were used to model the communication system.The S-TDMA performance is evaluated with total link reliability,system throughput,average probability of correct delivery before deadline and system latency.The evaluation results displayed in graphs when compared with instant retry and drop of frame were found to be reliable in recovering loss packets.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice(No.22DZ2229014)Science and Technology Commission of Shanghai Municipality(No.20JC1413900).
文摘The time-scaling transformation is a widely used approach within the computational framework of control parameterization for optimizing the switching times of control variables.However,the conventional time-scaling transformation has the limitation that the switching times and the number of switches for each control component must be the same.In this paper,we present a novel technique to solve constrained optimal control problems that allows for adaptively optimizing the switching times for each control component.Numerical results demonstrate that this proposed method provides better flexibility in control strategy and yields improved performance.
基金supported by the National Key R&D Program of China(2017YFB0902200)Science and Technology Project of State Grid Corporation of China(4000-202255057A-1-1-ZN,5228001700CW).
文摘Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.
