Decoding Algorithms for MDS Array Codes
本文研究了一类基于Kronecker积构造的MDS阵列码的解码算法，针对擦除和错误信道提出了解决方法，并利用范德蒙矩阵的特殊结构优化了过程。
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A Scalable Digital Twin Framework for Energy Optimization in Data Centers
This study addresses the challenges of high energy consumption and the lack of real-time, scalable energy efficiency management in data centers by proposing a digital twin–based, scalable energy optimization framework. The framework integrates real-time IoT data acquisition, cloud computing infrastructure, and Long Short-Term Memory (LSTM) neural networks, representing the first integration of digital twin technology with LSTM for modeling and forecasting data center energy consumption. This synergy enables real-time monitoring, accurate prediction, and intelligent control of energy usage. Experimental results demonstrate that the proposed approach significantly reduces power consumption and improves Power Usage Effectiveness (PUE) in small-scale environments, while exhibiting strong scalability, cost-effectiveness, and practical potential for broader deployment.
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Decoding Algorithms for MDS Array Codes
本文研究了一类基于Kronecker积构造的MDS阵列码的解码算法，针对擦除和错误信道提出了解决方法，并利用范德蒙矩阵的特殊结构优化了过程。
0 citationsRead paper
A Scalable Digital Twin Framework for Energy Optimization in Data Centers
This study addresses the challenges of high energy consumption and the lack of real-time, scalable energy efficiency management in data centers by proposing a digital twin–based, scalable energy optimization framework. The framework integrates real-time IoT data acquisition, cloud computing infrastructure, and Long Short-Term Memory (LSTM) neural networks, representing the first integration of digital twin technology with LSTM for modeling and forecasting data center energy consumption. This synergy enables real-time monitoring, accurate prediction, and intelligent control of energy usage. Experimental results demonstrate that the proposed approach significantly reduces power consumption and improves Power Usage Effectiveness (PUE) in small-scale environments, while exhibiting strong scalability, cost-effectiveness, and practical potential for broader deployment.
0 citationsRead paper