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
In this study,unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring.Unsupervised recognition(k-means++)was used to label the spectral characteristics of...In this study,unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring.Unsupervised recognition(k-means++)was used to label the spectral characteristics of acoustic emission(AE)signals after completing the tensile tests at ambient temperature.Using in-plane tensile at 800 and 1000°C as implementing examples,supervised recognition(K-nearest neighbor(KNN))was used to identify damage mode in real time.According to the damage identification results,four main tensile damage modes of 2D C/SiC composites were identified:matrix cracking(122.6–201 kHz),interfacial debonding(201–294.4 kHz),interfacial sliding(20.6–122.6 kHz)and fiber breaking(294.4–1000 kHz).Additionally,the damage evolution mechanisms for the 2D C/SiC composites were analyzed based on the characteristics of AE energy accumulation curve during the in-plane tensile loading at ambient and elevated temperature with oxidation.Meanwhile,the energy of various damage modes was accurately calculated by harmonic wavelet packet and the damage degree of modes could be analyzed.The identification results show that compared with previous studies,using the AE analysis method,the method has higher sensitivity and accuracy.展开更多
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the National Natural Science Foundation of China(Grant No.12172304)the 111 Project(Grant No.BP0719007).
文摘In this study,unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring.Unsupervised recognition(k-means++)was used to label the spectral characteristics of acoustic emission(AE)signals after completing the tensile tests at ambient temperature.Using in-plane tensile at 800 and 1000°C as implementing examples,supervised recognition(K-nearest neighbor(KNN))was used to identify damage mode in real time.According to the damage identification results,four main tensile damage modes of 2D C/SiC composites were identified:matrix cracking(122.6–201 kHz),interfacial debonding(201–294.4 kHz),interfacial sliding(20.6–122.6 kHz)and fiber breaking(294.4–1000 kHz).Additionally,the damage evolution mechanisms for the 2D C/SiC composites were analyzed based on the characteristics of AE energy accumulation curve during the in-plane tensile loading at ambient and elevated temperature with oxidation.Meanwhile,the energy of various damage modes was accurately calculated by harmonic wavelet packet and the damage degree of modes could be analyzed.The identification results show that compared with previous studies,using the AE analysis method,the method has higher sensitivity and accuracy.
