The application of artificial intelligence technology in electrical automation engineering is of great help to improve work efficiency, which not only alleviates the problem of labor shortage, but also plays a role in...The application of artificial intelligence technology in electrical automation engineering is of great help to improve work efficiency, which not only alleviates the problem of labor shortage, but also plays a role in boosting the long-term development of electrical engineering industry. In order to further promote the development of electrical engineering and improve its work efficiency, power workers need to strengthen the application of artificial intelligence technology and constantly seek innovation and breakthrough.展开更多
The development of high and new technology also drives the development of electrical engineering and automation technology. At the same time, the technology has been widely used, which also provides strong support for...The development of high and new technology also drives the development of electrical engineering and automation technology. At the same time, the technology has been widely used, which also provides strong support for the development and innovation of electrical engineering. In the process of development, electrical engineering and automation technology are effectively integrated with electrical engineering, and further promote the informatization and modernization development of China's industry and motor field. After the integration of electrical engineering and automation technology, it can better meet people's requirements for electrical engineering and provide more quality services for people. In the process of use, we should also avoid the external influence in the application process of electrical engineering and automation technology, which will affect the overall development. Therefore, we should conduct in-depth analysis of the problems and carry out targeted treatment.展开更多
In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been auto...In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.展开更多
Modern approach to artificial intelligence(Al)aims to design algorithms that learn directly from data.This approach has achieved impressive results and has contributed significantly to the progress of Al,particularly ...Modern approach to artificial intelligence(Al)aims to design algorithms that learn directly from data.This approach has achieved impressive results and has contributed significantly to the progress of Al,particularly in the sphere of supervised deep learning.It has also simplified the design of machine learning systems as the learning process is highly automated.However,not all data processing tasks in conventional deep learning pipelines have been automated.In most cases data has to be manually collected,preprocessed and further extended through data augmentation before they can be effective for training.Recently,special techniques for automating these tasks have emerged.The automation of data processing tasks is driven by the need to utilize large volumes of complex,heterogeneous data for machine learning and big data applications.Today,end-to-end automated data processing systems based on automated machine learning(AutoML)techniques are capable of taking raw data and transforming them into useful features for big data tasks by automating all intermediate processing stages.In this work,we present a thorough review of approaches for automating data processing tasks in deep learning pipelines,including auto-mated data preprocessing-e.g.,data cleaning,labeling,missing data imputation,and categorical data encoding-as well as data augmentation(including synthetic data generation using gener-ative Al methods)and feature engineering-specifically,automated feature extraction,feature construction and feature selection.In addition to automating specific data processing tasks,we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine.展开更多
文摘The application of artificial intelligence technology in electrical automation engineering is of great help to improve work efficiency, which not only alleviates the problem of labor shortage, but also plays a role in boosting the long-term development of electrical engineering industry. In order to further promote the development of electrical engineering and improve its work efficiency, power workers need to strengthen the application of artificial intelligence technology and constantly seek innovation and breakthrough.
文摘The development of high and new technology also drives the development of electrical engineering and automation technology. At the same time, the technology has been widely used, which also provides strong support for the development and innovation of electrical engineering. In the process of development, electrical engineering and automation technology are effectively integrated with electrical engineering, and further promote the informatization and modernization development of China's industry and motor field. After the integration of electrical engineering and automation technology, it can better meet people's requirements for electrical engineering and provide more quality services for people. In the process of use, we should also avoid the external influence in the application process of electrical engineering and automation technology, which will affect the overall development. Therefore, we should conduct in-depth analysis of the problems and carry out targeted treatment.
文摘In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.
文摘Modern approach to artificial intelligence(Al)aims to design algorithms that learn directly from data.This approach has achieved impressive results and has contributed significantly to the progress of Al,particularly in the sphere of supervised deep learning.It has also simplified the design of machine learning systems as the learning process is highly automated.However,not all data processing tasks in conventional deep learning pipelines have been automated.In most cases data has to be manually collected,preprocessed and further extended through data augmentation before they can be effective for training.Recently,special techniques for automating these tasks have emerged.The automation of data processing tasks is driven by the need to utilize large volumes of complex,heterogeneous data for machine learning and big data applications.Today,end-to-end automated data processing systems based on automated machine learning(AutoML)techniques are capable of taking raw data and transforming them into useful features for big data tasks by automating all intermediate processing stages.In this work,we present a thorough review of approaches for automating data processing tasks in deep learning pipelines,including auto-mated data preprocessing-e.g.,data cleaning,labeling,missing data imputation,and categorical data encoding-as well as data augmentation(including synthetic data generation using gener-ative Al methods)and feature engineering-specifically,automated feature extraction,feature construction and feature selection.In addition to automating specific data processing tasks,we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine.
