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
It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This stu...It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.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
Challenges arise in automate design with building information modeling(BIM)in underground space.Industry foundation classes(IFC)standard lacks detailed entity objects for describing excavation retaining structures and...Challenges arise in automate design with building information modeling(BIM)in underground space.Industry foundation classes(IFC)standard lacks detailed entity objects for describing excavation retaining structures and geological information,and automated design based on BIM models is not yet for practical application.This study presents a novel automated framework.It integrates the extended IFC standard with mechanical analysis and BIM modeling,significantly advancing structural optimization and rebar detailing.Direct 3D model generation streamlines complex excavation projects,aligning with the trend towards automated,precision-driven design.Key contributions include:(1)the extension of the IFC standard to support excavation retaining structures with objects like IfcBracedPit and IfcPitWall,improving interoperability between geotechnical models and BIM systems;(2)the integration of heuristic algorithms for automated optimization of deformation control parameters,reducing manual intervention;and(3)the promotion of design methodology that bypasses two-dimensional modeling and directly generates three-dimensional models,enhancing efficiency and allowing engineers to focus on high-level decision-making.However,the framework is primarily suited for standard cross-section projects like subway stations and tunnels.Future work will focus on refining the framework for more complex geotechnical projects,addressing software independence and improving design robustness and independence.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.
基金supported by the DH2025-TN07-07 project conducted at the Thai Nguyen University of Information and Communication Technology,Thai Nguyen,Vietnam,with additional support from the AI in Software Engineering Lab.
文摘It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.
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.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3009400)National Natural Science Foundation of China(Grant Nos.52238009,52208344,and 52278350)+1 种基金Natural Science Foundation of Jiangxi Province(Grant No.20223BBG71018)the Innovation Fund of Jiangxi Province for Postgraduate(Grant No.YC2024-B196).
文摘Challenges arise in automate design with building information modeling(BIM)in underground space.Industry foundation classes(IFC)standard lacks detailed entity objects for describing excavation retaining structures and geological information,and automated design based on BIM models is not yet for practical application.This study presents a novel automated framework.It integrates the extended IFC standard with mechanical analysis and BIM modeling,significantly advancing structural optimization and rebar detailing.Direct 3D model generation streamlines complex excavation projects,aligning with the trend towards automated,precision-driven design.Key contributions include:(1)the extension of the IFC standard to support excavation retaining structures with objects like IfcBracedPit and IfcPitWall,improving interoperability between geotechnical models and BIM systems;(2)the integration of heuristic algorithms for automated optimization of deformation control parameters,reducing manual intervention;and(3)the promotion of design methodology that bypasses two-dimensional modeling and directly generates three-dimensional models,enhancing efficiency and allowing engineers to focus on high-level decision-making.However,the framework is primarily suited for standard cross-section projects like subway stations and tunnels.Future work will focus on refining the framework for more complex geotechnical projects,addressing software independence and improving design robustness and independence.
