In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established...In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established to approximately describe the nonlinear and time-varying TC systems. According to this mathematical model, the feedback control theory is adopted to prove the system＇s stableness and zero steady state error. The experiments result shows that the error of deadline satisfied ratio in the system is kept within 4 of the desired value. And the number of classifiers can be dynamically adjusted by the system itself to save the computa tion resources. The proposed methodology enables the theo retical analysis and evaluation to the TC systems, leading to a high-quality and low cost implementation approach.展开更多
The simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite el...The simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite element method(FEM)are widely used to compute the deformations of soft tissue.However,it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties,geometries and interaction mechanisms.In this paper,a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback.Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth.The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth.The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths.The feasibility of the method is verified by investigating the adaptation to step variations.Compared with the ground truth of the finite element(FE)results,the maximum residual is less than 0.92 mm in the Y direction and 0.31 mm in the X direction.The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.展开更多
Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as ...Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.展开更多
We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronizat...We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & con-trolling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.展开更多
Aiming at the soft real-lime fault tolerant demand of critical webapplications at present, such as E-commerce, a new fault tolerant scheduling algorithm based onprobability is proposed. To achieve fault tolerant sched...Aiming at the soft real-lime fault tolerant demand of critical webapplications at present, such as E-commerce, a new fault tolerant scheduling algorithm based onprobability is proposed. To achieve fault tolerant scheduling, the primary/slave backuptechnology isapplied on the basis of task's self similar accessing characteristics, when the primary taskcompleted successfully, the resources allocated for the slave task are reclaimed, thus advancingsystem's efficiency. Experimental results demonstrate on the premise of satisfying system's certainfault tolerant probability, task's schcdulabi-listic probability is improved, especially, the highertask's self similar degree is, the more obviously the utilization of system resources is enhanced.展开更多
基金Supported by the National Natural Science Foun-dation of China (90104032) ,the National High-Tech Research andDevelopment Plan of China (2003AA1Z2090)
文摘In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established to approximately describe the nonlinear and time-varying TC systems. According to this mathematical model, the feedback control theory is adopted to prove the system＇s stableness and zero steady state error. The experiments result shows that the error of deadline satisfied ratio in the system is kept within 4 of the desired value. And the number of classifiers can be dynamically adjusted by the system itself to save the computa tion resources. The proposed methodology enables the theo retical analysis and evaluation to the TC systems, leading to a high-quality and low cost implementation approach.
基金National Major Scientific Research Instrument Development Project of China(Grant No.81827804)Zhejiang Provincial Natural Science Foundation of China(Grant No.LSD19H180004)+1 种基金Science Fund for Creative Group of NSFC(Grant No.51821903)National Natural Science Foundation of China(Grant No.51665049).
文摘The simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite element method(FEM)are widely used to compute the deformations of soft tissue.However,it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties,geometries and interaction mechanisms.In this paper,a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback.Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth.The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth.The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths.The feasibility of the method is verified by investigating the adaptation to step variations.Compared with the ground truth of the finite element(FE)results,the maximum residual is less than 0.92 mm in the Y direction and 0.31 mm in the X direction.The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.
基金This research was supported by the 2022 scientific promotion program funded by Jeju National University.
文摘Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.
基金Project supported by the MITT Intelligent Manufacturing Project of Chinathe Study of Interconnection Standard and Experimental Verification in the Intelligent Manufacturing Plant for Naval Architecture and Marine Engineeringthe Science and Technology Program of Jiangxi Province,China(No.20161BBE50062)
文摘We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & con-trolling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.
文摘Aiming at the soft real-lime fault tolerant demand of critical webapplications at present, such as E-commerce, a new fault tolerant scheduling algorithm based onprobability is proposed. To achieve fault tolerant scheduling, the primary/slave backuptechnology isapplied on the basis of task's self similar accessing characteristics, when the primary taskcompleted successfully, the resources allocated for the slave task are reclaimed, thus advancingsystem's efficiency. Experimental results demonstrate on the premise of satisfying system's certainfault tolerant probability, task's schcdulabi-listic probability is improved, especially, the highertask's self similar degree is, the more obviously the utilization of system resources is enhanced.
