To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A mac...To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.展开更多
Purpose Machine learning,as an advanced technology,has achieved remarkable success across various fields due to its powerful data processing and pattern recognition capabilities.When applied to particle accelerators,i...Purpose Machine learning,as an advanced technology,has achieved remarkable success across various fields due to its powerful data processing and pattern recognition capabilities.When applied to particle accelerators,it has the potential to optimize performance,enhance operational efficiency,and drive innovation in accelerator technology.However,the adoption of machine learning often necessitates extensive knowledge of algorithms and programming,which can be time-consuming and create barriers to accessibility.Methods To overcome these challenges,the development of the machine learning as a service for accelerators(MLaaS4ACC)system is proposed.This system is designed to simplify the use of machine learning tools for accelerator researchers,efficiently perform machine learning tasks,and continuously expand and optimize functionalities tailored to the unique requirements of accelerator systems.Results and Conclusion Currently,MLaaS4ACC can effectively perform several straightforward machine learning tasks.Compared to traditional methods,it reduces the time required for actual tasks and simplifies the model training process,while yielding results that are not significantly different.The model already meets the necessary requirements.Looking ahead,it is essential to enhance and expand the system in various aspects to address more complex demands.Improvements in both the performance and functionality of MLaaS4ACC are necessary to ensure it meets these evolving requirements.展开更多
基金partially supported by the National Key Technologies R&D Program of China under Grant No.2015BAK38B01the National Natural Science Foundation of China under Grant Nos.61174103 and 61272357the Fundamental Research Funds for the Central Universities under Grant No.06500025
文摘To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.
基金funded by National Natural Science Foundation of China No.12275294.
文摘Purpose Machine learning,as an advanced technology,has achieved remarkable success across various fields due to its powerful data processing and pattern recognition capabilities.When applied to particle accelerators,it has the potential to optimize performance,enhance operational efficiency,and drive innovation in accelerator technology.However,the adoption of machine learning often necessitates extensive knowledge of algorithms and programming,which can be time-consuming and create barriers to accessibility.Methods To overcome these challenges,the development of the machine learning as a service for accelerators(MLaaS4ACC)system is proposed.This system is designed to simplify the use of machine learning tools for accelerator researchers,efficiently perform machine learning tasks,and continuously expand and optimize functionalities tailored to the unique requirements of accelerator systems.Results and Conclusion Currently,MLaaS4ACC can effectively perform several straightforward machine learning tasks.Compared to traditional methods,it reduces the time required for actual tasks and simplifies the model training process,while yielding results that are not significantly different.The model already meets the necessary requirements.Looking ahead,it is essential to enhance and expand the system in various aspects to address more complex demands.Improvements in both the performance and functionality of MLaaS4ACC are necessary to ensure it meets these evolving requirements.
