A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial d...A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.展开更多
We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwi...We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.展开更多
In recent years,numerous recurrent neural network(RNN)models have been reported for solving time-dependent nonlinear optimization problems.However,few existing RNN models simultaneously involve nonlinear equality cons...In recent years,numerous recurrent neural network(RNN)models have been reported for solving time-dependent nonlinear optimization problems.However,few existing RNN models simultaneously involve nonlinear equality constraints,direct discretization,and noise suppression.This limitation presents challenges when existing models are applied to practical engineering problems.Additionally,most current discrete-time RNN models are derived from continuous-time models,which may not perform well for solving essentially discrete problems.To handle these issues,a robust direct-discretized RNN(RDD-RNN)model is proposed to efficiently realize time-dependent optimization constrained by nonlinear equalities(TDOCNE)in the presence of various time-dependent noises.Theoretical analyses are provided to reveal that the proposed RDD-RNN model possesses excellent convergence and noise-suppressing capability.Furthermore,numerical experiments and manipulator control instances are conducted and analyzed to validate the superior robustness of the proposed RDD-RNN model under various time-dependent noises,particularly quadratic polynomial noise.Eventually,small target detection experiments further demonstrate the practicality of the RDD-RNN model in image processing applications.展开更多
This paper proposes a new personal tour planning problem with time-dependent satisfactions, traveling and activity duration times for sightseeing. It is difficult to represent the time-dependent model using general st...This paper proposes a new personal tour planning problem with time-dependent satisfactions, traveling and activity duration times for sightseeing. It is difficult to represent the time-dependent model using general static network models, and hence, Time-Expanded Network (TEN) is introduced. The TEN contains a copy to the set of nodes in the underlying static network for each discrete time step, and it turns the problem of determining an optimal flow over time into a classical static network flow problem. Using the proposed TEN-based model, it is possible not only to construct various variations with time of costs and satisfactions flexibly in a single network, but also to select optimal departure places and accommodations according to the tour route with tourist’s favorite places and to obtain the time scheduling of tour route, simultaneously. The proposed model is formulated as a 0 - 1 integer programming problem which can be applied by existing useful combinatorial optimization and soft computing algorithms. It’s also equivalently transformed into several existing tour planning problems using some natural assumptions. Furthermore, comparing the proposed model with some previous models using a numerical example with time-dependent parameters, both the similarity of these models in the static network and the advantage of the proposed TEN-based model are obtained.展开更多
A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as mea...A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.展开更多
Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(A...Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(AI)approach has been applied.Thus,in this study,a new hybrid model based on deep neural networks(DNNs),particle swarm optimization(PSO),and genetic algorithms(GAs)is proposed to solve this problem.The DNN can effectively simulate any sophisticated equation,and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model.In the present study,analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under timedependent loading.The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.展开更多
The target coverage is an important yet challenging problem in wireless sensor networks, especially when both coverage and energy constraints should be taken into account. Due to its nonlinear nature, previous studies...The target coverage is an important yet challenging problem in wireless sensor networks, especially when both coverage and energy constraints should be taken into account. Due to its nonlinear nature, previous studies of this problem have mainly focused on heuristic algorithms; the theoretical bound remains unknown. Moreover, the most popular method used in the previous literature, i.e., discretization of continuous time, has yet to be justified. This paper fills in these gaps with two theoretical results. The first one is a formal justification for the method. We use a simple example to illustrate the procedure of transforming a solution in time domain into a corresponding solution in the pattern domain with the same network lifetime and obtain two key observations. After that, we formally prove these two observations and use them as the basis to justify the method. The second result is an algorithm that can guarantee the network lifetime to be at least （1 - ε） of the optimal network lifetime, where ε can be made arbitrarily small depending on the required precision. The algorithm is based on the column generation （CG） theory, which decomposes the original problem into two sub-problems and iteratively solves them in a way that approaches the optimal solution. Moreover, we developed several constructive approaches to further optimize the algorithm. Numerical results verify the efficiency of our CG-based algorithm.展开更多
Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and oth...Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate (>50%), which shows the robustness and efficiency of the proposed model.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61174021 and 61473136)
文摘A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.
基金This work was supported by the National Natural Science Foundation of China(Grant No.12072340)the China Postdoctoral Science Foundation(Grant No.2022M720727)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2022ZB130).
文摘We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.
基金supported in part by the National Key Research and Development Program of China(2023YFC3011100)the National Natural Science Foundation of China(62476294)+1 种基金the Science and Technology Planning Project of Guangdong Province,China(2021B1212040017)the Guangdong Basic and Applied Basic Research Foundation(2025A1515010377,2023A1515110697).
文摘In recent years,numerous recurrent neural network(RNN)models have been reported for solving time-dependent nonlinear optimization problems.However,few existing RNN models simultaneously involve nonlinear equality constraints,direct discretization,and noise suppression.This limitation presents challenges when existing models are applied to practical engineering problems.Additionally,most current discrete-time RNN models are derived from continuous-time models,which may not perform well for solving essentially discrete problems.To handle these issues,a robust direct-discretized RNN(RDD-RNN)model is proposed to efficiently realize time-dependent optimization constrained by nonlinear equalities(TDOCNE)in the presence of various time-dependent noises.Theoretical analyses are provided to reveal that the proposed RDD-RNN model possesses excellent convergence and noise-suppressing capability.Furthermore,numerical experiments and manipulator control instances are conducted and analyzed to validate the superior robustness of the proposed RDD-RNN model under various time-dependent noises,particularly quadratic polynomial noise.Eventually,small target detection experiments further demonstrate the practicality of the RDD-RNN model in image processing applications.
文摘This paper proposes a new personal tour planning problem with time-dependent satisfactions, traveling and activity duration times for sightseeing. It is difficult to represent the time-dependent model using general static network models, and hence, Time-Expanded Network (TEN) is introduced. The TEN contains a copy to the set of nodes in the underlying static network for each discrete time step, and it turns the problem of determining an optimal flow over time into a classical static network flow problem. Using the proposed TEN-based model, it is possible not only to construct various variations with time of costs and satisfactions flexibly in a single network, but also to select optimal departure places and accommodations according to the tour route with tourist’s favorite places and to obtain the time scheduling of tour route, simultaneously. The proposed model is formulated as a 0 - 1 integer programming problem which can be applied by existing useful combinatorial optimization and soft computing algorithms. It’s also equivalently transformed into several existing tour planning problems using some natural assumptions. Furthermore, comparing the proposed model with some previous models using a numerical example with time-dependent parameters, both the similarity of these models in the static network and the advantage of the proposed TEN-based model are obtained.
基金Project(71001079)supported by the National Natural Science Foundation of China
文摘A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.
文摘Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(AI)approach has been applied.Thus,in this study,a new hybrid model based on deep neural networks(DNNs),particle swarm optimization(PSO),and genetic algorithms(GAs)is proposed to solve this problem.The DNN can effectively simulate any sophisticated equation,and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model.In the present study,analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under timedependent loading.The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.
基金partially supported by the National Natural Science Foundation of China under Grant Nos.60872009,6002016the Hi-Tech Research and Development 863 Program of China under Grant Nos.2007AA01Z428,2009AA01Z148the Post Doctoral Fellowship(ID No.P10356)for Scientific Research of Japan Society for Promotion of Science(JSPS)
文摘The target coverage is an important yet challenging problem in wireless sensor networks, especially when both coverage and energy constraints should be taken into account. Due to its nonlinear nature, previous studies of this problem have mainly focused on heuristic algorithms; the theoretical bound remains unknown. Moreover, the most popular method used in the previous literature, i.e., discretization of continuous time, has yet to be justified. This paper fills in these gaps with two theoretical results. The first one is a formal justification for the method. We use a simple example to illustrate the procedure of transforming a solution in time domain into a corresponding solution in the pattern domain with the same network lifetime and obtain two key observations. After that, we formally prove these two observations and use them as the basis to justify the method. The second result is an algorithm that can guarantee the network lifetime to be at least （1 - ε） of the optimal network lifetime, where ε can be made arbitrarily small depending on the required precision. The algorithm is based on the column generation （CG） theory, which decomposes the original problem into two sub-problems and iteratively solves them in a way that approaches the optimal solution. Moreover, we developed several constructive approaches to further optimize the algorithm. Numerical results verify the efficiency of our CG-based algorithm.
文摘Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate (>50%), which shows the robustness and efficiency of the proposed model.
