This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an ...This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).展开更多
Nanoparticles,distinguished by their unique chemical and physical properties,have emerged as focal points within the realm of materials science.Traditional theoretical approaches for atomic simulations mainly include ...Nanoparticles,distinguished by their unique chemical and physical properties,have emerged as focal points within the realm of materials science.Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations,with the former offering efficiency but limited reliability,and the latter providing accuracy but restricted to systems of relatively small sizes.Herein,we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset.This includes small nanoclusters,nanoparticles,as well as surface and bulk systems with periodic boundary conditions.The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes.Through rigorous validation,we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range,especially within the nanoscale regime.Remarkably,this is achieved while preserving the accuracy typically associated with ab initio methods.展开更多
The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insigh...The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insights, aid in optimization, and allow us to explore various design parameters efficiently. However, existing empirical models often fall short in facilitating an optimal design because of their limited accuracy, sensitivity to assumption, and context dependency. In this scenario, the use of Machine and Deep Learning (ML and DL) methods can enhance accuracy, manage nonlinearity, adjust to varying conditions, decrease dependence on assumptions, automatically extract pertinent features, and provide scalability. Indeed, ML and DL techniques can derive valuable insights from datasets, contributing to a comprehensive understanding. By means of multiple ML and DL methods, this paper addresses the challenge of estimating key parameters in micro-finned tube heat exchangers such as the heat transfer coefficient (HTC) and frictional pressure drop (FPD). The methods have been trained and tested using an experimental dataset consisting of over a thousand data points associated with flow condensation, involving various tube geometries. In this context, the Artificial Neural Network (ANN) demonstrates superior performance in accurately estimating parameters with MAEs in the range below 4.5% for both HTC and FPD. Finally, recognizing the importance of comprehending the internal mechanisms of the black-box ANN model, the paper explores its interpretability aspects.展开更多
为了提高网络入侵的检测率,以降低误检率,提出一种基于均值聚分析和多层核心集凝聚算法相融合的网络入侵检的网络入侵检测模型。利用K-means算法对多层核心集凝聚算法的核心集,用其替代原粗化过程得到的顶层核心集,实现了顶层核心集的...为了提高网络入侵的检测率,以降低误检率,提出一种基于均值聚分析和多层核心集凝聚算法相融合的网络入侵检的网络入侵检测模型。利用K-means算法对多层核心集凝聚算法的核心集,用其替代原粗化过程得到的顶层核心集,实现了顶层核心集的快速准确定位,简化了算法的计算复杂性。然后,将KM-Mul CA算法应用到入侵检测模型,最后采用KDD Cup 99数据集进行仿真实验。结果表明,本模型可以获得理想的网络入侵检测率和误检率。展开更多
基金Supported by Program of Science and Technology of Hunan Province(2007FJ2006)Project the Program of Science and Tech-nology of Hunan Province(2007TP4030)Hunan Provincial Natural Science Foundation of China(08JJ3093)
文摘This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).
基金supported by the National Science Fund for Distinguished Young Scholars(22225302)the National Natural Science Foundation of China(92161113,21991151,21991150 and 22021001)+2 种基金the Fundamental Research Funds for the Central Universities(20720220008,20720220009 and 20720220010)the Laboratory of AI for Electrochemistry(AI4EC)IKKEM(RD2023100101 and RD2022070501)
文摘Nanoparticles,distinguished by their unique chemical and physical properties,have emerged as focal points within the realm of materials science.Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations,with the former offering efficiency but limited reliability,and the latter providing accuracy but restricted to systems of relatively small sizes.Herein,we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset.This includes small nanoclusters,nanoparticles,as well as surface and bulk systems with periodic boundary conditions.The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes.Through rigorous validation,we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range,especially within the nanoscale regime.Remarkably,this is achieved while preserving the accuracy typically associated with ab initio methods.
文摘The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insights, aid in optimization, and allow us to explore various design parameters efficiently. However, existing empirical models often fall short in facilitating an optimal design because of their limited accuracy, sensitivity to assumption, and context dependency. In this scenario, the use of Machine and Deep Learning (ML and DL) methods can enhance accuracy, manage nonlinearity, adjust to varying conditions, decrease dependence on assumptions, automatically extract pertinent features, and provide scalability. Indeed, ML and DL techniques can derive valuable insights from datasets, contributing to a comprehensive understanding. By means of multiple ML and DL methods, this paper addresses the challenge of estimating key parameters in micro-finned tube heat exchangers such as the heat transfer coefficient (HTC) and frictional pressure drop (FPD). The methods have been trained and tested using an experimental dataset consisting of over a thousand data points associated with flow condensation, involving various tube geometries. In this context, the Artificial Neural Network (ANN) demonstrates superior performance in accurately estimating parameters with MAEs in the range below 4.5% for both HTC and FPD. Finally, recognizing the importance of comprehending the internal mechanisms of the black-box ANN model, the paper explores its interpretability aspects.
文摘为了提高网络入侵的检测率,以降低误检率,提出一种基于均值聚分析和多层核心集凝聚算法相融合的网络入侵检的网络入侵检测模型。利用K-means算法对多层核心集凝聚算法的核心集,用其替代原粗化过程得到的顶层核心集,实现了顶层核心集的快速准确定位,简化了算法的计算复杂性。然后,将KM-Mul CA算法应用到入侵检测模型,最后采用KDD Cup 99数据集进行仿真实验。结果表明,本模型可以获得理想的网络入侵检测率和误检率。
