We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that prov...We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.展开更多
With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application scenarios.Specifically,the issue of classifying data streams based on mobile sensors can be for...With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application scenarios.Specifically,the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors.Existing incremental learning methods are often single-task single-view,which cannot learn shared representations between relevant tasks and views.An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges,utilizing the idea of multi-task multi-view learning.Specifically,the attention mechanism is first used to align different sensor data of different views.In addition,MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning.Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.展开更多
The ubiquity of smartphones together with their ever-growing computing,networking,and sensing powers have been changing the landscape of people＇s daily life.Among others,activity recoginition,which takes the raw sens...The ubiquity of smartphones together with their ever-growing computing,networking,and sensing powers have been changing the landscape of people＇s daily life.Among others,activity recoginition,which takes the raw sensor reading as inputs and predicts a user＇s motion activity,has become an active research area in recent years.It is the core building block in many high-impact applications,ranging from health and fitness monitoring,personal biometric signature,urban computing,assistive technology,and elder-care,to indoor localization and navigation,etc.This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones＇ sensors.We start with the basic concepts such as sensors,activity types,etc.We review the core data mining techniques behind the main stream activity recognition algorithms,analyze their major challenges,and introduce a variety of real applications enabled by activity recognition.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No.62073045)。
文摘We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.
文摘With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application scenarios.Specifically,the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors.Existing incremental learning methods are often single-task single-view,which cannot learn shared representations between relevant tasks and views.An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges,utilizing the idea of multi-task multi-view learning.Specifically,the attention mechanism is first used to align different sensor data of different views.In addition,MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning.Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.
基金supported by the National Science Foundation (Nos. IIS1017415 and CNS0904901)sponsored by the Army Research Laboratory+1 种基金accomplished under Cooperative Agreement Number W911NF-09-2-0053provided by Defense Advanced Research Projects Agency (DARPA) under Contract Number W911NF11-C-0200 and W911NF-12-C-0028
文摘The ubiquity of smartphones together with their ever-growing computing,networking,and sensing powers have been changing the landscape of people＇s daily life.Among others,activity recoginition,which takes the raw sensor reading as inputs and predicts a user＇s motion activity,has become an active research area in recent years.It is the core building block in many high-impact applications,ranging from health and fitness monitoring,personal biometric signature,urban computing,assistive technology,and elder-care,to indoor localization and navigation,etc.This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones＇ sensors.We start with the basic concepts such as sensors,activity types,etc.We review the core data mining techniques behind the main stream activity recognition algorithms,analyze their major challenges,and introduce a variety of real applications enabled by activity recognition.
