As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology.Demand load management is central to energy systems wit...As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology.Demand load management is central to energy systems within digital twins,which significantly impacts operational costs.Peak demand loads can lead to substantial monthly utility expenses without proper management.AMPAMOD,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand loads.AMPAMOD leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load management.We provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive ratio.Extensive trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.展开更多
Medical big data with artificial intelligence are vital in advancing digital medicine.However,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a ...Medical big data with artificial intelligence are vital in advancing digital medicine.However,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous works.This paper aims to develop an easy-to-use time-series multimodal data extraction pipeline,Quick-MIMIC,for standardised data extraction from MIMIC datasets.Our method can fully integrate different data structures into a time-series table,including structured,semi-structured,and unstructured data.We also introduce two additional modules to Quick-MIMIC,a pipeline parallelization method and data analysis methods,for reducing the data extraction time and presenting the characteristics of the extracted data intuitively.The extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.展开更多
The increasing use of distributed energy resources changes the way to manage the electricity system.Unlike the traditional centralized powered utility,many homes and businesses with local electricity generators have e...The increasing use of distributed energy resources changes the way to manage the electricity system.Unlike the traditional centralized powered utility,many homes and businesses with local electricity generators have established their own microgrids,which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations,load demands,and dynamic electricity prices.To address this challenge,a rank-based multiple-choice secretary algorithm(RMSA)was proposed for microgrid management,to reduce the microgrid operating cost.Rather than relying on the complete information of future dynamic variables or accurate predictive approaches,a lightweight solution was used to make real-time decisions under uncertainties.The RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic pricing.Extensive experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.展开更多
Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts.These massive accounts can be divided into diverse categories,such as...Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts.These massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this paper.The benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities.Unfortunately,the exploration of blockchain account diversity remains scarce.Even the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are different.To this end,we present the first attempt to demystify the account diversity on Ethereum.The key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the premise.We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness.Furthermore,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions.As for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total transactions.The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.展开更多
文摘As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology.Demand load management is central to energy systems within digital twins,which significantly impacts operational costs.Peak demand loads can lead to substantial monthly utility expenses without proper management.AMPAMOD,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand loads.AMPAMOD leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load management.We provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive ratio.Extensive trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.
基金supported by the National Natural Science Foundation of China-Science and Technology Development Fund(No.62361166662)the National Key R&D Program of China(Nos.2023YFC3503400 and 2022YFC3400400)+3 种基金the Key R&D Program of Hunan Province(Nos.2023GK2004,2023SK2059,and 2023SK2060)the Top 10 Technical Key Project in Hunan Province(No.2023GK1010)the Key Technologies R&D Program of Guangdong Province(No.2023B1111030004)the Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics,the National Supercomputing Center in Changsha(http://nscc.hnu.edu.cn/),and Peng Cheng Lab.
文摘Medical big data with artificial intelligence are vital in advancing digital medicine.However,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous works.This paper aims to develop an easy-to-use time-series multimodal data extraction pipeline,Quick-MIMIC,for standardised data extraction from MIMIC datasets.Our method can fully integrate different data structures into a time-series table,including structured,semi-structured,and unstructured data.We also introduce two additional modules to Quick-MIMIC,a pipeline parallelization method and data analysis methods,for reducing the data extraction time and presenting the characteristics of the extracted data intuitively.The extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.
文摘The increasing use of distributed energy resources changes the way to manage the electricity system.Unlike the traditional centralized powered utility,many homes and businesses with local electricity generators have established their own microgrids,which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations,load demands,and dynamic electricity prices.To address this challenge,a rank-based multiple-choice secretary algorithm(RMSA)was proposed for microgrid management,to reduce the microgrid operating cost.Rather than relying on the complete information of future dynamic variables or accurate predictive approaches,a lightweight solution was used to make real-time decisions under uncertainties.The RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic pricing.Extensive experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.
基金supported by Key-Area Rescearch and Development Program of Guangdong Province(2020B010109005)the National Natural Science Foundation of China(Grant No.62072197)。
文摘Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts.These massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this paper.The benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities.Unfortunately,the exploration of blockchain account diversity remains scarce.Even the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are different.To this end,we present the first attempt to demystify the account diversity on Ethereum.The key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the premise.We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness.Furthermore,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions.As for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total transactions.The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.
