The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enh...The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enhance productivity,safety,and ergonomics.However,achieving seamless collaboration requires robots to recognize the identity of individual human workers and perform appropriate collaborative operations.This paper presents a novel gait identity recognition method using Inertial Measurement Unit(IMU)data to enable personalized HRC in manufacturing settings,contributing to the human-centric vision of Industry 5.0.The hardware of the entire system consists of the IMU wearable device as the data source and a collaborative robot as the actuator,reflecting the interconnected nature of HCPS.The proposed method leverages wearable IMU sensors to capture motion data,including 3-axis acceleration,3-axis angular velocity.The two-tower Transformer architecture is employed to extract and analyze gait features.It consists of Temporal and Channel Modules,multi-head Auto-Correlation mechanism,and multi-scale convolutional neural network(CNN)layers.A series of optimization experiments were conducted to improve the performance of the model.The proposed model is compared with other state-of-the-art studies on two public datasets as well as one self-collected dataset.The experimental results demonstrate the better performance of our method in gait identity recognition.It is experimentally verified in the manufacturing environment involving four workers and one collaborative robot in an HRC assembly task,showcasing the practical applicability of this human-centric approach in the context of Industry 5.0.展开更多
By utilizing artificial intelligence and pattern rec ognition techniques, we propose an integrated mobile-customer identity recognition approach in this paper, based on cus tomer＇s behavior characteristics extracted ...By utilizing artificial intelligence and pattern rec ognition techniques, we propose an integrated mobile-customer identity recognition approach in this paper, based on cus tomer＇s behavior characteristics extracted from the customer information database. To verify the effectiveness of this approach, a test has been run on the dataset consisting of 1 000 customers in 3 consecutive months.The result is compared with the real dataset in the fourth month consisting of 162 customers, which has been set as the customers for recognition. The high correct rate of the test （96.30%）, together with 1. 87% of the judge-by-mistake rate and 7.82% of the leaving-out rate, demonstrates the effectiveness of this approach.展开更多
For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brin...For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brings trouble to the subsequent safety education of workers. Although, many scholars have devoted themselves to the study of person re-identification which neglected safety detection. The study of this paper mainly proposes a method based on deep learning, which is different from the previous study of helmet detection </span><span style="font-family:Verdana;">and human identity recognition and can carry out helmet detection and</span><span style="font-family:Verdana;"> identity recognition for construction workers. This paper proposes a computer vision-based worker identity recognition and helmet recognition method. We collected 3000 real-name channel images and constructed a neural network based on </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">You Only Look Once (YOLO) v3 model to extract the features of the construction worker’s face and helmet, respectively. Experiments show that the method has a high recognition accuracy rate, fast recognition speed, accurate recognition of workers and helmet detection, and solves the problem of poor supervision of real-name channels.展开更多
Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significa...Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.展开更多
A Streetcar Named Desire unfolds the tragedy of a southern lady.The conflict betwixt southern Blanche and northern Stanley is the main contradiction in the play.In the end,Stanley drives Blanche crazy and sends her to...A Streetcar Named Desire unfolds the tragedy of a southern lady.The conflict betwixt southern Blanche and northern Stanley is the main contradiction in the play.In the end,Stanley drives Blanche crazy and sends her to an asylum.This paper attempts to analyze Blanche’s identity crisis from identity negotiation theory in cross-cultural communication,including identity security,identity inclusion,and identity predictability.Thereby it figures out that one who suffers hostile gazes from others in a strange environment,is unable to carry out an intimate and effective communication,which will eventually lead to a sense of loss and despair.This paper puts forward the methods to obtain identity recognition:be honest with others,be sure of oneself,and try one’s best to fit in the new environment,aiming to help people avoid identity crisis in interpersonal communication and establish a positive identity.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52375031,52405038)Zhejiang Provincial Natural Science Foundation(Grant No.LRG25E050001)+4 种基金China Postdoctoral Science Foundation(Grant Nos.GZB20240654,2024M762812,2025T180371)the Priority-Funded Postdoctoral Research Project of Zhejiang Province(Grant No.ZJ2024013)the Dongfang Electric Corporation-Zhejiang University Joint Innovation Research Institutethe Bellwethers+X Research and Development Plan of Zhejiang Province(Grant Nos.2024C04057(CSJ),2025C01012)the Joint Research Project of Sci-Tech Innovation Community in Yangtze River Delta(Grant No.2023CSJGG1400)。
文摘The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enhance productivity,safety,and ergonomics.However,achieving seamless collaboration requires robots to recognize the identity of individual human workers and perform appropriate collaborative operations.This paper presents a novel gait identity recognition method using Inertial Measurement Unit(IMU)data to enable personalized HRC in manufacturing settings,contributing to the human-centric vision of Industry 5.0.The hardware of the entire system consists of the IMU wearable device as the data source and a collaborative robot as the actuator,reflecting the interconnected nature of HCPS.The proposed method leverages wearable IMU sensors to capture motion data,including 3-axis acceleration,3-axis angular velocity.The two-tower Transformer architecture is employed to extract and analyze gait features.It consists of Temporal and Channel Modules,multi-head Auto-Correlation mechanism,and multi-scale convolutional neural network(CNN)layers.A series of optimization experiments were conducted to improve the performance of the model.The proposed model is compared with other state-of-the-art studies on two public datasets as well as one self-collected dataset.The experimental results demonstrate the better performance of our method in gait identity recognition.It is experimentally verified in the manufacturing environment involving four workers and one collaborative robot in an HRC assembly task,showcasing the practical applicability of this human-centric approach in the context of Industry 5.0.
基金Supported by Guangdong Mobile CommunicationCompany Li mited Key Item(19984001)
文摘By utilizing artificial intelligence and pattern rec ognition techniques, we propose an integrated mobile-customer identity recognition approach in this paper, based on cus tomer＇s behavior characteristics extracted from the customer information database. To verify the effectiveness of this approach, a test has been run on the dataset consisting of 1 000 customers in 3 consecutive months.The result is compared with the real dataset in the fourth month consisting of 162 customers, which has been set as the customers for recognition. The high correct rate of the test （96.30%）, together with 1. 87% of the judge-by-mistake rate and 7.82% of the leaving-out rate, demonstrates the effectiveness of this approach.
文摘For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brings trouble to the subsequent safety education of workers. Although, many scholars have devoted themselves to the study of person re-identification which neglected safety detection. The study of this paper mainly proposes a method based on deep learning, which is different from the previous study of helmet detection </span><span style="font-family:Verdana;">and human identity recognition and can carry out helmet detection and</span><span style="font-family:Verdana;"> identity recognition for construction workers. This paper proposes a computer vision-based worker identity recognition and helmet recognition method. We collected 3000 real-name channel images and constructed a neural network based on </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">You Only Look Once (YOLO) v3 model to extract the features of the construction worker’s face and helmet, respectively. Experiments show that the method has a high recognition accuracy rate, fast recognition speed, accurate recognition of workers and helmet detection, and solves the problem of poor supervision of real-name channels.
基金supported by the grants from Yunnan Province(202305AH340006,202305AH340007)CAS Light of West China Program(xbzg-zdsys-202213)。
文摘Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.
文摘A Streetcar Named Desire unfolds the tragedy of a southern lady.The conflict betwixt southern Blanche and northern Stanley is the main contradiction in the play.In the end,Stanley drives Blanche crazy and sends her to an asylum.This paper attempts to analyze Blanche’s identity crisis from identity negotiation theory in cross-cultural communication,including identity security,identity inclusion,and identity predictability.Thereby it figures out that one who suffers hostile gazes from others in a strange environment,is unable to carry out an intimate and effective communication,which will eventually lead to a sense of loss and despair.This paper puts forward the methods to obtain identity recognition:be honest with others,be sure of oneself,and try one’s best to fit in the new environment,aiming to help people avoid identity crisis in interpersonal communication and establish a positive identity.
