Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misc...Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misclassifications and safety risks.This study introduces a proactive framework to detect early signs of domain-specific concept drift by leveraging domain analysis and natural language processing techniques.This method is designed to help maintain the relevance of domain knowledge and prevent potential failures in AI systems due to evolving concept definitions.Design/methodology/approach:The proposed framework integrates natural language processing and image analysis to continuously update and monitor key domain concepts against evolving external data sources,such as social media and news.By identifying terms and features closely associated with core concepts,the system anticipates and flags significant changes.This was tested in the automotive domain on the pedestrian concept,where the framework was evaluated for its capacity to detect shifts in the recognition of pedestrians,particularly during events like Halloween and specific car accidents.Findings:The framework demonstrated an ability to detect shifts in the domain concept of pedestrians,as evidenced by contextual changes around major events.While it successfully identified pedestrian-related drift,the system’s accuracy varied when overlapping with larger social events.The results indicate the model’s potential to foresee relevant shifts before they impact autonomous systems,although further refinement is needed to handle high-impact concurrent events.Research limitations:This study focused on detecting concept drift in the pedestrian domain within autonomous vehicles,with results varying across domains.To assess generalizability,we tested the framework for airplane-related incidents and demonstrated adaptability.However,unpredictable events and data biases from social media and news may obscure domain-specific drifts.Further evaluation across diverse applications is needed to enhance robustness in evolving AI environments.Practical implications:The proactive detection of concept drift has significant implications for AI-driven domains,especially in safety-critical applications like autonomous driving.By identifying early signs of drift,this framework provides actionable insights for AI system updates,potentially reducing misclassification risks and enhancing public safety.Moreover,it enables timely interventions,reducing costly and labor-intensive retraining requirements by focusing only on the relevant aspects of evolving concepts.This method offers a streamlined approach for maintaining AI system performance in environments where domain knowledge rapidly changes.Originality/value:This study contributes a novel domain-agnostic framework that combines natural language processing with image analysis to predict concept drift early.This unique approach,which is focused on real-time data sources,offers an effective and scalable solution for addressing the evolving nature of domain-specific concepts in AI applications.展开更多
Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendou...Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.展开更多
In this paper, we propose a novel AIenabled space-air-ground integrated networks(SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called &...In this paper, we propose a novel AIenabled space-air-ground integrated networks(SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called "civil aircraft assisted SAGIN(CAA-SAGIN)". The assistance of civil aircrafts can reduce the stress of satellite networks, improve the performance of SAGIN, decrease the construction cost and save space resources. Taking the Chinese mainland as an example, this paper has analyzed the distribution of civil aircrafts, and obtained the coverage characteristics of civil aircraft assisted networks(CAAN). Taking Starlink as the benchmark, this paper has calculated the service gap of CAAN, and designed the joint coverage constellation. The simulation results prove that the number of satellites in CAASAGIN can be greatly reduced with the assistance of civil aircrafts at the same data rate.展开更多
基金supported by U.S.Office of Naval Research(ONR)Grant number G2A62826.
文摘Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misclassifications and safety risks.This study introduces a proactive framework to detect early signs of domain-specific concept drift by leveraging domain analysis and natural language processing techniques.This method is designed to help maintain the relevance of domain knowledge and prevent potential failures in AI systems due to evolving concept definitions.Design/methodology/approach:The proposed framework integrates natural language processing and image analysis to continuously update and monitor key domain concepts against evolving external data sources,such as social media and news.By identifying terms and features closely associated with core concepts,the system anticipates and flags significant changes.This was tested in the automotive domain on the pedestrian concept,where the framework was evaluated for its capacity to detect shifts in the recognition of pedestrians,particularly during events like Halloween and specific car accidents.Findings:The framework demonstrated an ability to detect shifts in the domain concept of pedestrians,as evidenced by contextual changes around major events.While it successfully identified pedestrian-related drift,the system’s accuracy varied when overlapping with larger social events.The results indicate the model’s potential to foresee relevant shifts before they impact autonomous systems,although further refinement is needed to handle high-impact concurrent events.Research limitations:This study focused on detecting concept drift in the pedestrian domain within autonomous vehicles,with results varying across domains.To assess generalizability,we tested the framework for airplane-related incidents and demonstrated adaptability.However,unpredictable events and data biases from social media and news may obscure domain-specific drifts.Further evaluation across diverse applications is needed to enhance robustness in evolving AI environments.Practical implications:The proactive detection of concept drift has significant implications for AI-driven domains,especially in safety-critical applications like autonomous driving.By identifying early signs of drift,this framework provides actionable insights for AI system updates,potentially reducing misclassification risks and enhancing public safety.Moreover,it enables timely interventions,reducing costly and labor-intensive retraining requirements by focusing only on the relevant aspects of evolving concepts.This method offers a streamlined approach for maintaining AI system performance in environments where domain knowledge rapidly changes.Originality/value:This study contributes a novel domain-agnostic framework that combines natural language processing with image analysis to predict concept drift early.This unique approach,which is focused on real-time data sources,offers an effective and scalable solution for addressing the evolving nature of domain-specific concepts in AI applications.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0118700)the National Natural Science Foundation of China(Grant No.62375101)the Fundamental Research Funds for the Central Universities(Grant No.YCJJ20230216).
文摘Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.
基金supported by National Nature Science Foundation of China (No. 61871155)。
文摘In this paper, we propose a novel AIenabled space-air-ground integrated networks(SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called "civil aircraft assisted SAGIN(CAA-SAGIN)". The assistance of civil aircrafts can reduce the stress of satellite networks, improve the performance of SAGIN, decrease the construction cost and save space resources. Taking the Chinese mainland as an example, this paper has analyzed the distribution of civil aircrafts, and obtained the coverage characteristics of civil aircraft assisted networks(CAAN). Taking Starlink as the benchmark, this paper has calculated the service gap of CAAN, and designed the joint coverage constellation. The simulation results prove that the number of satellites in CAASAGIN can be greatly reduced with the assistance of civil aircrafts at the same data rate.
