This article deals with the problem of minimizing ruin probability under optimal control for the continuous-time compound binomial model with investment. The jump mechanism in our article is different from that of Liu...This article deals with the problem of minimizing ruin probability under optimal control for the continuous-time compound binomial model with investment. The jump mechanism in our article is different from that of Liu et al [4]. Comparing with [4], the introduction of the investment, and hence, the additional Brownian motion term, makes the problem technically challenging. To overcome this technical difficulty, the theory of change of measure is used and an exponential martingale is obtained by virtue of the extended generator. The ruin probability is minimized through maximizing adjustment coefficient in the sense of Lundberg bounds. At the same time, the optimal investment strategy is obtained.展开更多
In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as...In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.展开更多
The study was to investigate the role of pinhole single photon emission computed tomography (SPECT), the human pulmonary adenocarcinoma bone-seeking metastasis cell line SPC-A-1BM was used.These cells form typical ost...The study was to investigate the role of pinhole single photon emission computed tomography (SPECT), the human pulmonary adenocarcinoma bone-seeking metastasis cell line SPC-A-1BM was used.These cells form typical osteolytic bone metastases when inoculated into the arterial circulation of NIH-Beige-Nude-XD (BNX) mice via the left ventricle.In order to evaluate the irradiation impact of ^(99m)Tc-MDP versus X-ray on cells growth,we used six groups of SPC-A-1BM cells in our imaging scheme and irradiated by various doses of ^(99m)Tc-MDP (37,74,111, 370,740 MBq) and X-ray(40 kV,2 mA,6 s) respectively.The cell's number of each group was well recorded in different exposure time(4,8,12,24,48,72,96 hours).After that,SPC-A-1BM cells(1×106) were inoculated into the mice via left ventricle.We compared the results obtained with those different doses of ^(99m)Tc-MDP using pinhole SPECT and conventional X-ray skeletal surveys.The data show that the cell-survival number of 111 MBq group has insignificant difference with that of X-ray and the dose is adequate to have an ideal image.Besides,it is important that the chromosome of the cells in the group of 111 MBq showed no irradiation-related damages in our test.These results implied that ^(99m)Tc-MDP pinhole SPECT may provide another way other than conventional X-ray skeletal surveys in detecting bone metastasis of pulmonary adenocarcinoma in BNX mice.展开更多
The current Internet has evolved during the last decade to a global provider of diverse applications. However, the underlying structure of routing and addressing has not evolved in the same pace and is somewhat inflex...The current Internet has evolved during the last decade to a global provider of diverse applications. However, the underlying structure of routing and addressing has not evolved in the same pace and is somewhat inflexible. How to provide diverse routing services, support emerging communication paradigms based on limited and definite network resources has become an urgent challenge. This paper investigates the adaptive matching between routing and application through network function decomposition and composition, and proposes a polymorphic routing model to support diverse applications and emerging communication paradigms. The model splits complex routing functions into its constituents, and derives customized routing mechanisms supporting various applications by composing the routing constituents. The derivation process is modeled as a Markov Decision Process (MDP), and a polymorphic derivation algorithm is also proposed to derive customized routing instances for diverse applications. The model enables the network to self-adjust routing services dynamically to adapt to the different requirements of applications, supports coexistence of multiple routing modes and communication paradigms, and provides a feasible solution for the network compatibility and evolvement. We describe the key design and demonstrate the feasibility of polymorphic derivation by simulations. We also present case studies that demonstrate key functionalities the polymorphic routing model enables.展开更多
Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy t...Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models,which inadequately represent the continuous dynamics of electroencephalogram(EEG)signals.To overcome this limitation,we introduce an innovative approach that employs Neural Ordinary Differential Equations(NODEs)to model EEG signals as continuous-time systems.This allows for effective management of irregular sampling and intricate temporal patterns.In contrast to conventional techniques,such as Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs),which necessitate fixedlength inputs and often struggle with long-term dependencies,our framework incorporates:(1)a NODE block to capture continuous-time EEG dynamics,(2)a feature extraction module tailored for seizure-specific patterns,and(3)an attention-based fusion mechanism to enhance interpretability in classification.When evaluated on three publicly accessible EEG datasets,including those from Boston Children’s Hospital and the Massachusetts Institute of Technology(CHB-MIT)and the Temple University Hospital(TUH)EEG Corpus,the model demonstrated an average accuracy of 98.2%,a sensitivity of 97.8%,a specificity of 98.3%,and an F1-score of 97.9%.Additionally,the inference latency was reduced by approximately 30%compared to standard CNN and Long Short-Term Memory(LSTM)architectures,making it well-suited for real-time applications.The method’s resilience to noise and its adaptability to irregular sampling enhance its potential for clinical use in real-time settings.展开更多
This paper investigates the safe energy management of emerging shared renewables and refined oil transmission systems(SRROTSs)during the energy transition.Specifically,a continuous-time energy management model that co...This paper investigates the safe energy management of emerging shared renewables and refined oil transmission systems(SRROTSs)during the energy transition.Specifically,a continuous-time energy management model that considers the SRROTSs'multi-product sequential transmission characteristics is proposed to guide safe and efficient system operation.This model is also convenient for on-site dispatchers to operate.Correspondingly,a solver-free physics-informed particle swarm optimisation(PI-PSO)algorithm is tailored,utilising physical rules to regulate particle mutation and adapted to solve the proposed model,thereby enhancing the optimality and stability of the solution.Case studies on real-world SRROTSs are utilised to validate the proposed model and PI-PSO algorithm,which are expected to be generalised to other pipeline transmission systems.Especially,the PI-PSO algorithm achieves a 25.6%energy reduction compared to the original PSO algorithm,although a trade-off between improving the objective value and the number of iterations needed for convergence is observed.展开更多
In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compre...In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compressed least squares(LS) algorithm to deal with the challenges of parameter sparsity.At each sampling time instant,the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter.Then,the original high-dimensional sparse unknown parameter is recovered by a reconstruction method.By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods,the authors establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals.At last,a simulation example is provided to verify the theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.展开更多
Recently a novel algebraic method was proposed for linear continuous-time model identification,which has attracted extensive attention in the literature.This work reveals its connection to classic identification metho...Recently a novel algebraic method was proposed for linear continuous-time model identification,which has attracted extensive attention in the literature.This work reveals its connection to classic identification methods,discusses a limitation and presents a useful modification of the method.The discussions are supported by analysis and numerical experiments.展开更多
基金supported by the Nature Science Foundation of Hebei Province(A2014202202)supported by the Nature Science Foundation of China(11471218)
文摘This article deals with the problem of minimizing ruin probability under optimal control for the continuous-time compound binomial model with investment. The jump mechanism in our article is different from that of Liu et al [4]. Comparing with [4], the introduction of the investment, and hence, the additional Brownian motion term, makes the problem technically challenging. To overcome this technical difficulty, the theory of change of measure is used and an exponential martingale is obtained by virtue of the extended generator. The ruin probability is minimized through maximizing adjustment coefficient in the sense of Lundberg bounds. At the same time, the optimal investment strategy is obtained.
基金supported by the General Program (No.60774022)the State Key Program of National Natural Science Foundation of China(No.60834001)the State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University (No.RCS2009ZT011)
文摘In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.
基金Supported by the Key Project for Basicl Research in Shanghai Science and Technology Commission,China (Grant No.071409011)
文摘The study was to investigate the role of pinhole single photon emission computed tomography (SPECT), the human pulmonary adenocarcinoma bone-seeking metastasis cell line SPC-A-1BM was used.These cells form typical osteolytic bone metastases when inoculated into the arterial circulation of NIH-Beige-Nude-XD (BNX) mice via the left ventricle.In order to evaluate the irradiation impact of ^(99m)Tc-MDP versus X-ray on cells growth,we used six groups of SPC-A-1BM cells in our imaging scheme and irradiated by various doses of ^(99m)Tc-MDP (37,74,111, 370,740 MBq) and X-ray(40 kV,2 mA,6 s) respectively.The cell's number of each group was well recorded in different exposure time(4,8,12,24,48,72,96 hours).After that,SPC-A-1BM cells(1×106) were inoculated into the mice via left ventricle.We compared the results obtained with those different doses of ^(99m)Tc-MDP using pinhole SPECT and conventional X-ray skeletal surveys.The data show that the cell-survival number of 111 MBq group has insignificant difference with that of X-ray and the dose is adequate to have an ideal image.Besides,it is important that the chromosome of the cells in the group of 111 MBq showed no irradiation-related damages in our test.These results implied that ^(99m)Tc-MDP pinhole SPECT may provide another way other than conventional X-ray skeletal surveys in detecting bone metastasis of pulmonary adenocarcinoma in BNX mice.
基金supported in part by the Cernet Network (NGII20160103)National Natural Science Foundation of China, National Natural Science Foundation of China(No.61672471)+3 种基金Fundamental Research Funds for the He’nan Province University (No.17KYYWF0202)He’nan Province University science and technology innovation team(No.18IRTSTHN012)Plan For Scientific Innovation Talent of Henan Province (No.184200510010)Zhengzhou University of Light Industry Doctoral Fund (2016BSJJ041) funding
文摘The current Internet has evolved during the last decade to a global provider of diverse applications. However, the underlying structure of routing and addressing has not evolved in the same pace and is somewhat inflexible. How to provide diverse routing services, support emerging communication paradigms based on limited and definite network resources has become an urgent challenge. This paper investigates the adaptive matching between routing and application through network function decomposition and composition, and proposes a polymorphic routing model to support diverse applications and emerging communication paradigms. The model splits complex routing functions into its constituents, and derives customized routing mechanisms supporting various applications by composing the routing constituents. The derivation process is modeled as a Markov Decision Process (MDP), and a polymorphic derivation algorithm is also proposed to derive customized routing instances for diverse applications. The model enables the network to self-adjust routing services dynamically to adapt to the different requirements of applications, supports coexistence of multiple routing modes and communication paradigms, and provides a feasible solution for the network compatibility and evolvement. We describe the key design and demonstrate the feasibility of polymorphic derivation by simulations. We also present case studies that demonstrate key functionalities the polymorphic routing model enables.
基金extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group No.KSRG-2024-223.
文摘Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models,which inadequately represent the continuous dynamics of electroencephalogram(EEG)signals.To overcome this limitation,we introduce an innovative approach that employs Neural Ordinary Differential Equations(NODEs)to model EEG signals as continuous-time systems.This allows for effective management of irregular sampling and intricate temporal patterns.In contrast to conventional techniques,such as Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs),which necessitate fixedlength inputs and often struggle with long-term dependencies,our framework incorporates:(1)a NODE block to capture continuous-time EEG dynamics,(2)a feature extraction module tailored for seizure-specific patterns,and(3)an attention-based fusion mechanism to enhance interpretability in classification.When evaluated on three publicly accessible EEG datasets,including those from Boston Children’s Hospital and the Massachusetts Institute of Technology(CHB-MIT)and the Temple University Hospital(TUH)EEG Corpus,the model demonstrated an average accuracy of 98.2%,a sensitivity of 97.8%,a specificity of 98.3%,and an F1-score of 97.9%.Additionally,the inference latency was reduced by approximately 30%compared to standard CNN and Long Short-Term Memory(LSTM)architectures,making it well-suited for real-time applications.The method’s resilience to noise and its adaptability to irregular sampling enhance its potential for clinical use in real-time settings.
基金supported by the National Natural Science Foundation of China 52177089the Fundamental Research Funds for the Central Universities YCJJ20230463PipeChina South China Company GWHT20200001399.
文摘This paper investigates the safe energy management of emerging shared renewables and refined oil transmission systems(SRROTSs)during the energy transition.Specifically,a continuous-time energy management model that considers the SRROTSs'multi-product sequential transmission characteristics is proposed to guide safe and efficient system operation.This model is also convenient for on-site dispatchers to operate.Correspondingly,a solver-free physics-informed particle swarm optimisation(PI-PSO)algorithm is tailored,utilising physical rules to regulate particle mutation and adapted to solve the proposed model,thereby enhancing the optimality and stability of the solution.Case studies on real-world SRROTSs are utilised to validate the proposed model and PI-PSO algorithm,which are expected to be generalised to other pipeline transmission systems.Especially,the PI-PSO algorithm achieves a 25.6%energy reduction compared to the original PSO algorithm,although a trade-off between improving the objective value and the number of iterations needed for convergence is observed.
基金supported by the Major Key Project of Peng Cheng Laboratory under Grant No.PCL2023AS1-2Project funded by China Postdoctoral Science Foundation under Grant Nos.2022M722926 and2023T160605。
文摘In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compressed least squares(LS) algorithm to deal with the challenges of parameter sparsity.At each sampling time instant,the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter.Then,the original high-dimensional sparse unknown parameter is recovered by a reconstruction method.By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods,the authors establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals.At last,a simulation example is provided to verify the theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.
基金This work was supported in part by NTU[startup grant number M4080181.050]MOE AcRF[Tier 1 grant number RG 33/10 M4010492.050].
文摘Recently a novel algebraic method was proposed for linear continuous-time model identification,which has attracted extensive attention in the literature.This work reveals its connection to classic identification methods,discusses a limitation and presents a useful modification of the method.The discussions are supported by analysis and numerical experiments.
