Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ...Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.展开更多
Members of TMAS-the Swedish textile machinery association-are providing crucial manufacturing and automation services to the filtration sector which is an often invisible but very significant part of the global textil...Members of TMAS-the Swedish textile machinery association-are providing crucial manufacturing and automation services to the filtration sector which is an often invisible but very significant part of the global textile industry.Technical woven and nonwoven fabrics are used in a wide variety of products in filtration systems for air,gas and liquid filtration,touching on almost every facet of life in the 21st Century.展开更多
Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual...Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual Reality technologies will revolutionise training,patient education,and simulation-based care planning.Clinical pharmacists need to be ready and upskill to prepare for emerging technologies.The ethical,regulatory,and educational frameworks surrounding artificial intelligence and precision medicine will require constant attention,but the potential benefits for patient outcomes are unprecedented.Clinical pharmacists are in a prime position to design a new era in precision medicine,where technology works hand in hand with humans to transform healthcare.展开更多
With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation a...With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation across various aspects of human living.Over the past decade,AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures.These technologies not only increase productivity but also enable more scalable and adaptable solutions,proving particularly beneficial in industries such as healthcare,finance,and customer service.However,the absence of a unified review for categorization,benchmarking,and ethical risk assessment hinders the AI-driven automation progress.To bridge this gap,in this survey,we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements.We present a comparative analysis of performance metrics between production environments and industrial applications,along with an examination of cutting-edge developments.Specifically,we present a comparative analysis of the performance across various aspects in different industries,offering valuable insights for researchers to select the most suitable approaches for specific applications.Additionally,we also review multiple existing mainstream AI-driven automation applications in detail,highlighting their strengths and limitations.Finally,we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.展开更多
The rapid advancement of Artificial Intelligence(AI)and automation has significantly transformed the accounting profession,shifting the role of accountants from routine data processors to strategic decision makers and...The rapid advancement of Artificial Intelligence(AI)and automation has significantly transformed the accounting profession,shifting the role of accountants from routine data processors to strategic decision makers and ethical stewards of technology.This conceptual study explores how AI and automation are reshaping accounting tasks,transforming required competencies,and redefining professional responsibilities.By analyzing relevant literature and theoretical frameworks,this paper identifies the evolving skill sets,both technical such as data analytics and AI literacy,and nontechnical such as critical thinking and ethical judgment,that are essential for modern accountants.The study also emphasizes the importance of continuous education,ethical integrity,and adaptive learning in navigating the digital transformation of accounting.Ultimately,this paper contributes to a deeper understanding of how accountants can maintain relevance and add value in an increasingly automated and data driven environment.展开更多
The integrated innovation of artificial intelligence and electrical automation technology not only represents a further innovation of traditional models but also promotes the innovative development of both artificial ...The integrated innovation of artificial intelligence and electrical automation technology not only represents a further innovation of traditional models but also promotes the innovative development of both artificial intelligence and electrical automation technology.This paper delves into the significance of the integrated innovative applications of artificial intelligence and electrical automation technology,as well as the strategies for such applications,aiming to better achieve the intelligent development of electrical automation technology.展开更多
With the rapid development of the new energy industry,lithium batteries as key energy storage devices have an increasing demand for automated production and manufacturing.The automated guided vehicle(AGV),as a key equ...With the rapid development of the new energy industry,lithium batteries as key energy storage devices have an increasing demand for automated production and manufacturing.The automated guided vehicle(AGV),as a key equipment for achieving automation and intelligence in lithium battery production,has been widely applied in the lithium battery industry.This paper deeply explores the application of AGV in the analyzes its functions,advantages,and challenges in lithium battery automation equipment,various production processes,and looks ahead to its future development.Through research,it is found that AGV can effectively improve the production efficiency,reduce the costs,enhance the product quality,and the improve the production safety of the lithium batteries.Despite facing some challenges,with the continuous advancement of technology and the accumulation of application experience,AGV will have a broader development prospect in the lithium battery industry.展开更多
As the demand for intelligent and flexible production in the automotive manufacturing industry continues to intensify,industrial automation enterprises are gaining ever-greater market opportunities and competitive adv...As the demand for intelligent and flexible production in the automotive manufacturing industry continues to intensify,industrial automation enterprises are gaining ever-greater market opportunities and competitive advantages in this field.Based on a literature review and representative case studies,this paper constructs a theoretical framework for growth strategies and systematically analyzes the current application status and growth paths of automation enterprises in both complete vehicle and component production.The research finds that different growth strategies(such as vertical integration,horizontal diversification,and digital service transformation)exhibit varying applicability across upstream and downstream segments of automotive manufacturing,while simultaneously facing challenges related to technology integration,business models,and organizational change.In response to these issues,this paper proposes countermeasures such as optimizing R&D and customer relationship management,improving branding and after-sales service systems,and strengthening policy and industry environment support,thereby offering guidance for sustainable growth of industrial automation enterprises in the automotive manufacturing sector.展开更多
The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive ...The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive tasks on assembly lines,are now increasingly powered by advanced technologies such as Artificial Intelligence(AI),machine learning,and the Internet of Things(IoT),enabling them to perform complex,adaptive tasks in real-time.This paper explores the technological advancements that have transformed industrial robots,highlighting the integration of AI,smart sensors,and autonomous systems.Furthermore,it examines the implications of this paradigm shift for industries,human-robot collaboration,and the workforce.While intelligent robots promise greater efficiency,flexibility,and safety in manufacturing,challenges regarding implementation,economic impact,and ethical considerations remain significant.The paper concludes by looking at the future trends in robotics and their potential to reshape the global industrial landscape.展开更多
With the swift advancement of industrial automation,robots have emerged as an essential component in emerging industries and high-end equipment,thereby propelling industrial production towards greater intelligence and...With the swift advancement of industrial automation,robots have emerged as an essential component in emerging industries and high-end equipment,thereby propelling industrial production towards greater intelligence and efficiency.This paper reviews the pivotal technologies for motion planning of robots engaged in contact tasks within industrial automation contexts,encompassing environmental recognition,trajectory generation strategies,and sim-to-real transfer.Environmental recognition technology empowers robots to accurately discern objects and obstacles in their operational environment.Trajectory generation strategies formulate optimal motion paths based on environmental data and task specifications.Sim-to-real transfer is committed to effectively translating strategies from simulated environments to actual production,thereby diminishing the discrepancies between simulation and reality.The article also delves into the application of artificial intelligence in robot motion planning and how embodied intelligence models catalyze the evolution of robotics technology towards enhanced intelligence and automation.The paper concludes with a synthesis of the methodologies addressing this challenge and a perspective on the myriad challenges that warrant attention.展开更多
CT:As one of the exhibition owners,what were the key factors that led CEMATEX to decide to host ITMA ASIA 2025 in Singapore?Alex Zucchi:In response to requests from our members for an exhibition in Asia outside of Chi...CT:As one of the exhibition owners,what were the key factors that led CEMATEX to decide to host ITMA ASIA 2025 in Singapore?Alex Zucchi:In response to requests from our members for an exhibition in Asia outside of China,we decided to hold a combined exhibition in a second Asian location to support our members.It will also provide a reputable sourcing platform to help textile and garment manufacturers in the region modernize their operations.CT:Exhibition booths sold out very quickly.What motivates companies to participate in the exhibition?Alex Zucchi:The Singapore edition targets the South and Southeast Asia markets,as well as the Middle East.These are key textile and garment producing hubs.Hence,machinery makers are keen to reach out to buyers in the region.展开更多
Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artifici...Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artificial intelligence is designed.This design integrates expert control,fuzzy control,and neural network control technologies,and builds a hierarchical distributed architecture.Fault warning adopts threshold judgment and dynamic time warping pattern recognition technologies,and state monitoring realizes accurate analysis through multi-source data fusion and Kalman filtering algorithm.Practical applications show that this system can reduce the equipment failure rate by more than 30%.With the help of intelligent scheduling optimization,it can significantly improve production efficiency and reduce energy consumption,providing a reliable technical solution and practical path for the intelligent upgrade of the mechanical automation field.展开更多
In recent years,automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market.The integration of artificial intelligence(AI)tools,particularly those us...In recent years,automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market.The integration of artificial intelligence(AI)tools,particularly those using natural language processing(NLP)like ChatGPT,has opened new possibilities for automating various stages of the development lifecycle.The primary objective of this study is to evaluate the effectiveness of ChatGPT in automating various phases of software development.An artificial intelligence(AI)tool was developed using the OpenAI—Application Programming Interface(API),incorporating two key functionalities:1)generating user stories based on case or process inputs,and 2)estimating the effort required to execute each user story.Additionally,ChatGPT was employed to generate application code.The AI tool was tested in three case studies,each explored under two different development strategies:a semi-automated process utilizing the AI tools and a traditional manual approach.The results demonstrated a significant reduction in total development time,ranging from 40%to 51%.However,it was observed that the generated content could be inaccurate and incomplete,necessitating review and debugging before being applied to projects.In conclusion,given the increasing shift towards automation in software engineering,further research is critical to enhance the efficiency and reliability of AI tools,particularly those that leverage natural language processing(NLP)technologies.展开更多
As time swiftly passes,we find ourselves welcoming another spring with renewed hope and energy.On this occasion of bidding farewell to the old and embracing the new,the editorial team of Journal of Automation and Inte...As time swiftly passes,we find ourselves welcoming another spring with renewed hope and energy.On this occasion of bidding farewell to the old and embracing the new,the editorial team of Journal of Automation and Intelligence(JAI)extends heartfelt gratitude and sincere wishes to all our editorial board members,peer reviewers,authors,readers,and friends from various fields who have supported the journal’s development!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
GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable...GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable visual bugs,especially those that are context-dependent or graphical in nature.As a result,many issues go unnoticed during manual QA,which reduces overall game quality,degrades the user experience,and creates inefficiencies throughout the development cycle.This study proposes two approaches to address these challenges.The first leverages a Large Language Model(LLM)to directly analyze gameplay videos,detect visual bugs,and automatically generate QA reports in natural language.The second approach introduces a pipeline method:first generating textual descriptions of visual bugs in game videos using the ClipCap model,then using those descriptions as input for the LLM to synthesize QA reports.Through these two multi-faceted approaches,this study evaluates the feasibility of automated game QA systems.To implement this system,we constructed a visual bug database derived from real-world game cases and fine-tuned the ClipCap model for the game video domain.Our proposed approach aims to enhance both efficiency and quality in game development by reducing the burden of manual QA while improving the accuracy of visual bug detection and ensuring consistent,reliable report generation.展开更多
To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
基金supported by European Union’s Horizon Europe research and innovation programme,project AGILEHAND(Smart Grading,Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines)(101092043).
文摘Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.
文摘Members of TMAS-the Swedish textile machinery association-are providing crucial manufacturing and automation services to the filtration sector which is an often invisible but very significant part of the global textile industry.Technical woven and nonwoven fabrics are used in a wide variety of products in filtration systems for air,gas and liquid filtration,touching on almost every facet of life in the 21st Century.
文摘Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual Reality technologies will revolutionise training,patient education,and simulation-based care planning.Clinical pharmacists need to be ready and upskill to prepare for emerging technologies.The ethical,regulatory,and educational frameworks surrounding artificial intelligence and precision medicine will require constant attention,but the potential benefits for patient outcomes are unprecedented.Clinical pharmacists are in a prime position to design a new era in precision medicine,where technology works hand in hand with humans to transform healthcare.
文摘With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation across various aspects of human living.Over the past decade,AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures.These technologies not only increase productivity but also enable more scalable and adaptable solutions,proving particularly beneficial in industries such as healthcare,finance,and customer service.However,the absence of a unified review for categorization,benchmarking,and ethical risk assessment hinders the AI-driven automation progress.To bridge this gap,in this survey,we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements.We present a comparative analysis of performance metrics between production environments and industrial applications,along with an examination of cutting-edge developments.Specifically,we present a comparative analysis of the performance across various aspects in different industries,offering valuable insights for researchers to select the most suitable approaches for specific applications.Additionally,we also review multiple existing mainstream AI-driven automation applications in detail,highlighting their strengths and limitations.Finally,we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.
文摘The rapid advancement of Artificial Intelligence(AI)and automation has significantly transformed the accounting profession,shifting the role of accountants from routine data processors to strategic decision makers and ethical stewards of technology.This conceptual study explores how AI and automation are reshaping accounting tasks,transforming required competencies,and redefining professional responsibilities.By analyzing relevant literature and theoretical frameworks,this paper identifies the evolving skill sets,both technical such as data analytics and AI literacy,and nontechnical such as critical thinking and ethical judgment,that are essential for modern accountants.The study also emphasizes the importance of continuous education,ethical integrity,and adaptive learning in navigating the digital transformation of accounting.Ultimately,this paper contributes to a deeper understanding of how accountants can maintain relevance and add value in an increasingly automated and data driven environment.
文摘The integrated innovation of artificial intelligence and electrical automation technology not only represents a further innovation of traditional models but also promotes the innovative development of both artificial intelligence and electrical automation technology.This paper delves into the significance of the integrated innovative applications of artificial intelligence and electrical automation technology,as well as the strategies for such applications,aiming to better achieve the intelligent development of electrical automation technology.
文摘With the rapid development of the new energy industry,lithium batteries as key energy storage devices have an increasing demand for automated production and manufacturing.The automated guided vehicle(AGV),as a key equipment for achieving automation and intelligence in lithium battery production,has been widely applied in the lithium battery industry.This paper deeply explores the application of AGV in the analyzes its functions,advantages,and challenges in lithium battery automation equipment,various production processes,and looks ahead to its future development.Through research,it is found that AGV can effectively improve the production efficiency,reduce the costs,enhance the product quality,and the improve the production safety of the lithium batteries.Despite facing some challenges,with the continuous advancement of technology and the accumulation of application experience,AGV will have a broader development prospect in the lithium battery industry.
文摘As the demand for intelligent and flexible production in the automotive manufacturing industry continues to intensify,industrial automation enterprises are gaining ever-greater market opportunities and competitive advantages in this field.Based on a literature review and representative case studies,this paper constructs a theoretical framework for growth strategies and systematically analyzes the current application status and growth paths of automation enterprises in both complete vehicle and component production.The research finds that different growth strategies(such as vertical integration,horizontal diversification,and digital service transformation)exhibit varying applicability across upstream and downstream segments of automotive manufacturing,while simultaneously facing challenges related to technology integration,business models,and organizational change.In response to these issues,this paper proposes countermeasures such as optimizing R&D and customer relationship management,improving branding and after-sales service systems,and strengthening policy and industry environment support,thereby offering guidance for sustainable growth of industrial automation enterprises in the automotive manufacturing sector.
文摘The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive tasks on assembly lines,are now increasingly powered by advanced technologies such as Artificial Intelligence(AI),machine learning,and the Internet of Things(IoT),enabling them to perform complex,adaptive tasks in real-time.This paper explores the technological advancements that have transformed industrial robots,highlighting the integration of AI,smart sensors,and autonomous systems.Furthermore,it examines the implications of this paradigm shift for industries,human-robot collaboration,and the workforce.While intelligent robots promise greater efficiency,flexibility,and safety in manufacturing,challenges regarding implementation,economic impact,and ethical considerations remain significant.The paper concludes by looking at the future trends in robotics and their potential to reshape the global industrial landscape.
基金Supported by National Natural Science Foundation of China(Grant Nos.52575091,U2341231)。
文摘With the swift advancement of industrial automation,robots have emerged as an essential component in emerging industries and high-end equipment,thereby propelling industrial production towards greater intelligence and efficiency.This paper reviews the pivotal technologies for motion planning of robots engaged in contact tasks within industrial automation contexts,encompassing environmental recognition,trajectory generation strategies,and sim-to-real transfer.Environmental recognition technology empowers robots to accurately discern objects and obstacles in their operational environment.Trajectory generation strategies formulate optimal motion paths based on environmental data and task specifications.Sim-to-real transfer is committed to effectively translating strategies from simulated environments to actual production,thereby diminishing the discrepancies between simulation and reality.The article also delves into the application of artificial intelligence in robot motion planning and how embodied intelligence models catalyze the evolution of robotics technology towards enhanced intelligence and automation.The paper concludes with a synthesis of the methodologies addressing this challenge and a perspective on the myriad challenges that warrant attention.
文摘CT:As one of the exhibition owners,what were the key factors that led CEMATEX to decide to host ITMA ASIA 2025 in Singapore?Alex Zucchi:In response to requests from our members for an exhibition in Asia outside of China,we decided to hold a combined exhibition in a second Asian location to support our members.It will also provide a reputable sourcing platform to help textile and garment manufacturers in the region modernize their operations.CT:Exhibition booths sold out very quickly.What motivates companies to participate in the exhibition?Alex Zucchi:The Singapore edition targets the South and Southeast Asia markets,as well as the Middle East.These are key textile and garment producing hubs.Hence,machinery makers are keen to reach out to buyers in the region.
文摘Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artificial intelligence is designed.This design integrates expert control,fuzzy control,and neural network control technologies,and builds a hierarchical distributed architecture.Fault warning adopts threshold judgment and dynamic time warping pattern recognition technologies,and state monitoring realizes accurate analysis through multi-source data fusion and Kalman filtering algorithm.Practical applications show that this system can reduce the equipment failure rate by more than 30%.With the help of intelligent scheduling optimization,it can significantly improve production efficiency and reduce energy consumption,providing a reliable technical solution and practical path for the intelligent upgrade of the mechanical automation field.
文摘In recent years,automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market.The integration of artificial intelligence(AI)tools,particularly those using natural language processing(NLP)like ChatGPT,has opened new possibilities for automating various stages of the development lifecycle.The primary objective of this study is to evaluate the effectiveness of ChatGPT in automating various phases of software development.An artificial intelligence(AI)tool was developed using the OpenAI—Application Programming Interface(API),incorporating two key functionalities:1)generating user stories based on case or process inputs,and 2)estimating the effort required to execute each user story.Additionally,ChatGPT was employed to generate application code.The AI tool was tested in three case studies,each explored under two different development strategies:a semi-automated process utilizing the AI tools and a traditional manual approach.The results demonstrated a significant reduction in total development time,ranging from 40%to 51%.However,it was observed that the generated content could be inaccurate and incomplete,necessitating review and debugging before being applied to projects.In conclusion,given the increasing shift towards automation in software engineering,further research is critical to enhance the efficiency and reliability of AI tools,particularly those that leverage natural language processing(NLP)technologies.
文摘As time swiftly passes,we find ourselves welcoming another spring with renewed hope and energy.On this occasion of bidding farewell to the old and embracing the new,the editorial team of Journal of Automation and Intelligence(JAI)extends heartfelt gratitude and sincere wishes to all our editorial board members,peer reviewers,authors,readers,and friends from various fields who have supported the journal’s development!
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.
基金supported by a grant from the Korea Creative Content Agency,funded by the Ministry of Culture,Sports and Tourism of the Republic of Korea in 2025,for the project,“Development of AI-based large-scale automatic game verification technology to improve game production verification efficiency for small and medium-sized game companies”(RS 2024-00393500).
文摘GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable visual bugs,especially those that are context-dependent or graphical in nature.As a result,many issues go unnoticed during manual QA,which reduces overall game quality,degrades the user experience,and creates inefficiencies throughout the development cycle.This study proposes two approaches to address these challenges.The first leverages a Large Language Model(LLM)to directly analyze gameplay videos,detect visual bugs,and automatically generate QA reports in natural language.The second approach introduces a pipeline method:first generating textual descriptions of visual bugs in game videos using the ClipCap model,then using those descriptions as input for the LLM to synthesize QA reports.Through these two multi-faceted approaches,this study evaluates the feasibility of automated game QA systems.To implement this system,we constructed a visual bug database derived from real-world game cases and fine-tuned the ClipCap model for the game video domain.Our proposed approach aims to enhance both efficiency and quality in game development by reducing the burden of manual QA while improving the accuracy of visual bug detection and ensuring consistent,reliable report generation.
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
