The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,parti...The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.展开更多
To achieve accurate classification and recognition of ship target types,it is necessary to establish a sample library of ship targets to be identified.On the basis of exploring the principles of building a ship target...To achieve accurate classification and recognition of ship target types,it is necessary to establish a sample library of ship targets to be identified.On the basis of exploring the principles of building a ship target image library,the paper determines the sample set.Using 3DS MAX software as the platform,combined with the accurate 3D model of the ship in an offline state,the software fully utilizes its own rendering and animation functions to achieve the automatic generation of multi-view and multi-scale views of ship targets.To reduce the storage capacity of the image database,a construction method of the ship target image database based on the AP algorithm is presented.The algorithm can obtain the optimal cluster number,reduce the data storage capacity of the image database,and save the calculation amount for the subsequent matching calculation.展开更多
The variety of encryption mechanism and algorithms which were conventionally used have some limitations.The kernel operator library based on Cryptographic algorithm is put forward. Owing to the impenetrability of algo...The variety of encryption mechanism and algorithms which were conventionally used have some limitations.The kernel operator library based on Cryptographic algorithm is put forward. Owing to the impenetrability of algorithm, the data transfer system with the cryptographic algorithm library has many remarkable advantages in algorithm rebuilding and optimization,easily adding and deleting algorithm, and improving the security power over the traditional algorithm. The user can choose any one in all algorithms with the method against any attack because the cryptographic algorithm library is extensible.展开更多
This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often lim...This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.展开更多
基金the funding supported by China Scholarship Council(Nos.202008440524 and 202006370006)partially supported by the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)+1 种基金the Innovation Driven Project of Central South University(No.2020CX040)Shenzhen Science and Technology Plan(No.JCYJ20190808123013260).
文摘The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.
文摘To achieve accurate classification and recognition of ship target types,it is necessary to establish a sample library of ship targets to be identified.On the basis of exploring the principles of building a ship target image library,the paper determines the sample set.Using 3DS MAX software as the platform,combined with the accurate 3D model of the ship in an offline state,the software fully utilizes its own rendering and animation functions to achieve the automatic generation of multi-view and multi-scale views of ship targets.To reduce the storage capacity of the image database,a construction method of the ship target image database based on the AP algorithm is presented.The algorithm can obtain the optimal cluster number,reduce the data storage capacity of the image database,and save the calculation amount for the subsequent matching calculation.
文摘The variety of encryption mechanism and algorithms which were conventionally used have some limitations.The kernel operator library based on Cryptographic algorithm is put forward. Owing to the impenetrability of algorithm, the data transfer system with the cryptographic algorithm library has many remarkable advantages in algorithm rebuilding and optimization,easily adding and deleting algorithm, and improving the security power over the traditional algorithm. The user can choose any one in all algorithms with the method against any attack because the cryptographic algorithm library is extensible.
文摘This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.
