Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a resu...Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.展开更多
This paper explores the integration of Standard Operating Procedures (SOPs) using virtual reality and smart glasses technology in food manufacturing. The study employs a thorough methodology, combining observational i...This paper explores the integration of Standard Operating Procedures (SOPs) using virtual reality and smart glasses technology in food manufacturing. The study employs a thorough methodology, combining observational insights to develop a comprehensive SOP. Implementation at different firms resulted in significant improvements, reducing product waste and enhancing overall efficiency. The use of virtual reality further augments SOP adoption. The findings underscore SOPs’ transformative influence, offering a tangible solution to challenges in the food production sector. Recommendations include regular SOP reviews and ongoing training for sustained success. Different firms exemplify SOPs as indispensable tools for operational excellence.展开更多
基金support provided by Thammasat University Research fund under the TSRI,Contract Nos.TUFF19/2564 and TUFF24/2565for the project of“AI Ready City Networking in RUN”,based on the RUN Digital Cluster collaboration scheme.This research project was also supported by the Thailand Science Research and Innovation fund,the University of Phayao(Grant No.FF65-RIM041)supported by National Science,Research and Innovation(NSRF),and King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-FF-66-07.
文摘Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.
文摘This paper explores the integration of Standard Operating Procedures (SOPs) using virtual reality and smart glasses technology in food manufacturing. The study employs a thorough methodology, combining observational insights to develop a comprehensive SOP. Implementation at different firms resulted in significant improvements, reducing product waste and enhancing overall efficiency. The use of virtual reality further augments SOP adoption. The findings underscore SOPs’ transformative influence, offering a tangible solution to challenges in the food production sector. Recommendations include regular SOP reviews and ongoing training for sustained success. Different firms exemplify SOPs as indispensable tools for operational excellence.
