MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application prospects.However,the low accuracy limits its range of applications.To ensure data accuracy and safe...MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application prospects.However,the low accuracy limits its range of applications.To ensure data accuracy and safety we need to calibrate MEMS accelerometers.Many authors have improved accelerometer accuracy by calculating calibration parameters,and a large number of published calibration methods have been confusing.In this context,this paper introduces these techniques and methods,analyzes and summarizes the main error models and calibration procedures,and provides useful suggestions.Finally,the content of the accelerometer calibration method needs to be overcome.展开更多
Functional electrical stimulation is a method of repairing a dysfunctional limb in a stroke patient by using low-intensity electrical stimulation.Currently,it is widely used in smart medical treatment for limb rehabil...Functional electrical stimulation is a method of repairing a dysfunctional limb in a stroke patient by using low-intensity electrical stimulation.Currently,it is widely used in smart medical treatment for limb rehabilitation in stroke patients.In this paper,the development of FES systems is sorted out and analyzed in a time order.Then,the progress of functional electrical stimulation in the field of rehabilitation is reviewed in details in two aspects,i.e.,system development and algorithm progress.In the system aspect,the development of the first FES control and stimulation system,the core of the lower limb-based neuroprosthesis system and the system based on brain-computer interface are introduced.The algorithm optimization for control strategy is introduced in the algorithm.Asynchronous stimulation to prolong the function time of the lower limbs and a method to improve the robustness of knee joint modeling using neural networks.Representative applications in each of these aspects have been investigated and analyzed.展开更多
Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes.“Straggling”tasks,however,have a serious impact on task allocation and scheduling in a Hadoop system.Speculat...Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes.“Straggling”tasks,however,have a serious impact on task allocation and scheduling in a Hadoop system.Speculative Execution(SE)is an efficient method of processing“Straggling”Tasks by monitoring real-time running status of tasks and then selectively backing up“Stragglers”in another node to increase the chance to complete the entire mission early.Present speculative execution strategies meet challenges on misjudgement of“Straggling”tasks and improper selection of backup nodes,which leads to inefficient implementation of speculative executive processes.This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution(ORSE)by introducing non-cooperative game schemes.The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem,where the tasks are regarded as game participants,whilst total task execution time of the entire cluster as the utility function.In that case,the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point,i.e.,the final resource scheduling scheme to be obtained.The strategy has been implemented in Hadoop-2.x.Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load,Busy Load and Busy Load with Skewed Data.展开更多
基金This work has received funding from 5150 Spring Specialists(05492018012)the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.701697,Major Program of the National Social Science Fund of China(Grant No.17ZDA092)+1 种基金Basic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20180794)333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)and the PAPD fund.
文摘MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application prospects.However,the low accuracy limits its range of applications.To ensure data accuracy and safety we need to calibrate MEMS accelerometers.Many authors have improved accelerometer accuracy by calculating calibration parameters,and a large number of published calibration methods have been confusing.In this context,this paper introduces these techniques and methods,analyzes and summarizes the main error models and calibration procedures,and provides useful suggestions.Finally,the content of the accelerometer calibration method needs to be overcome.
基金This work has received funding from the European Union Horizon 2020 research and innovation programmer under the Marie Sklodowska-Curie grant agreement No.701697,Major Program of the National Social Science Fund of China(Grant No.17ZDA092)Basic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20180794)+1 种基金333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)the PAPD fund.
文摘Functional electrical stimulation is a method of repairing a dysfunctional limb in a stroke patient by using low-intensity electrical stimulation.Currently,it is widely used in smart medical treatment for limb rehabilitation in stroke patients.In this paper,the development of FES systems is sorted out and analyzed in a time order.Then,the progress of functional electrical stimulation in the field of rehabilitation is reviewed in details in two aspects,i.e.,system development and algorithm progress.In the system aspect,the development of the first FES control and stimulation system,the core of the lower limb-based neuroprosthesis system and the system based on brain-computer interface are introduced.The algorithm optimization for control strategy is introduced in the algorithm.Asynchronous stimulation to prolong the function time of the lower limbs and a method to improve the robustness of knee joint modeling using neural networks.Representative applications in each of these aspects have been investigated and analyzed.
基金This work has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no.701697Major Program of the National Social Science Fund of China(Grant No.17ZDA092)+2 种基金Basic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20180794)333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)the PAPD fund.
文摘Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes.“Straggling”tasks,however,have a serious impact on task allocation and scheduling in a Hadoop system.Speculative Execution(SE)is an efficient method of processing“Straggling”Tasks by monitoring real-time running status of tasks and then selectively backing up“Stragglers”in another node to increase the chance to complete the entire mission early.Present speculative execution strategies meet challenges on misjudgement of“Straggling”tasks and improper selection of backup nodes,which leads to inefficient implementation of speculative executive processes.This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution(ORSE)by introducing non-cooperative game schemes.The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem,where the tasks are regarded as game participants,whilst total task execution time of the entire cluster as the utility function.In that case,the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point,i.e.,the final resource scheduling scheme to be obtained.The strategy has been implemented in Hadoop-2.x.Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load,Busy Load and Busy Load with Skewed Data.
