As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an ele...As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.展开更多
In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infe...In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact tracing.Through this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance pressure.The practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.展开更多
Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced b...Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’motion.While existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information utilization.To this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view.Particularly,the first-level view is the inter-trajectory view.In this level,we observe that the difficulty in predicting different trajectory samples varies.We define trajectory difficulty and train the proposed framework in an“easy-to-hard”schema.The second-level view is the intra-trajectory level.We find the influencing factors for a particular trajectory can be divided into two parts.The first part is global features,which keep stable within a trajectory,i.e.,the expected destination.The second part is local features,which change over time,i.e.,the current position.We believe that the two types of information should be handled in different ways.The hierarchical view is beneficial to take full advantage of the information in a fine-grained way.Experimental results validate the effectiveness of the proposed model-agnostic framework.展开更多
基金Supported by the National Natural Science Foundation of China(No.61672494,61402437)the National High Technology Research and Development Program of China(No.2015AA016005)
文摘As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.
文摘In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact tracing.Through this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance pressure.The practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.
基金supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No.2023112the National Natural Science Foundation of China under Grant No.62206266Zhao Zhang is supported by the China Postdoctoral Science Foundation under Grant No.2021M703273.
文摘Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’motion.While existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information utilization.To this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view.Particularly,the first-level view is the inter-trajectory view.In this level,we observe that the difficulty in predicting different trajectory samples varies.We define trajectory difficulty and train the proposed framework in an“easy-to-hard”schema.The second-level view is the intra-trajectory level.We find the influencing factors for a particular trajectory can be divided into two parts.The first part is global features,which keep stable within a trajectory,i.e.,the expected destination.The second part is local features,which change over time,i.e.,the current position.We believe that the two types of information should be handled in different ways.The hierarchical view is beneficial to take full advantage of the information in a fine-grained way.Experimental results validate the effectiveness of the proposed model-agnostic framework.
