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
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
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
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
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
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
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
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
Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,su...Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.展开更多
Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite cons...Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
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 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.
基金the National Key Research and Development Program of China(Grant No.2023YFC3009400).
文摘Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.
文摘Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
