In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthca...In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthcare WBANs are the black hole and sink hole attacks.Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path.Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness.This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score(PCS)and an MK-Means algorithm,which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory issues.First,the gathered training data feature count is reduced through data pre-processing in the PCS.Second,the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification.Third,certain attack detection measures given by the intrusion detection system,such as the number of data packages trans-received,are identified by the MK-Means algorithm.This study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate,low communication overhead,and reduced end-to-end delay in the network and improves the accuracy of biomedical data.展开更多
Noninvasive detection of body composition plays a significant role in the improvement of life quality and reduction in complications of the patients,and the near-infrared(NIR)spectroscopy,with the advantages of painle...Noninvasive detection of body composition plays a significant role in the improvement of life quality and reduction in complications of the patients,and the near-infrared(NIR)spectroscopy,with the advantages of painlessness and convenience,is considered as the most promising tool for the online noninvasive monitoring of body composition.However,quite different from other fields of online detection using NIR spectroscopy,such as food safety and environment monitoring,noninvasive detection of body composit ion demands higher precision of the instruments as well as more rigor-ousness of measurement conditions.Therefore,new challenges emerge when NIR spectroscopy is applied to the noninvasive detection of body composition,which,in this paper,are first concluded from the aspects of measurement methods,measurement conditions,instrument precision,multi-component influence,individual difference and novel weak signal extraction method based on our previous research in the cutting edge field of NIR noninvasive blood glucose detection.Moreover,novel ideas and approaches of our group to solve these problems are introduced,which may provide evidence for the future development of noninvasive blood glucose detection,and further contibute to the noninvasive detection of other body compositions using NIR spectroscopy.展开更多
This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro bo...This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro body gestures,which will be mapped to emotions and appropriate engagement states.The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames.We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset.The adopted C3D model was used based on two different approaches;as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pretrained model.Our model was tested and its performance was evaluated and compared to the existing models.It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%.The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’engagement levels.展开更多
Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method o...Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to the peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.展开更多
Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for...Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.展开更多
The welding fixtures are the most important devices for an auto body welding assembly line. The current special fixtures used by many automotive manufactures are only fit for one or several specific welding processes,...The welding fixtures are the most important devices for an auto body welding assembly line. The current special fixtures used by many automotive manufactures are only fit for one or several specific welding processes, and the dimensional problem in the circle due to several variation sources accumulation has no adjustment. The active error compensating welding fixture system for auto body is designed and manufactured. The detecting model, coordinate transformation model, and adjusting model based on auto body coordinate system are presented. The dowel pin modular design is adopted in the structure of the fixture to suit different workpieces with some similar characteristics. The online detection and adaptive control system using eddy current sensors and adaptive adjusting devices is analyzed. Three kinds of the left rear wheel covers SGM60 are selected to test workpieces of the developed system, and the active error compensating experiments are performed in the lab for many times. Test results show the validity of mechanism reconfigurations, on-line detections and error compensations of the developed welding fixture.展开更多
针对无线体域网(wireless body area network,WBAN)异常数据检测方法忽视人体异常数据的连续性,缺乏异常数据集检测等问题,提出一种基于Hampel滤波器和DBSCAN分层的WBAN异常数据检测方法。根据时间相关性利用Hampel滤波器检测异常数据点...针对无线体域网(wireless body area network,WBAN)异常数据检测方法忽视人体异常数据的连续性,缺乏异常数据集检测等问题,提出一种基于Hampel滤波器和DBSCAN分层的WBAN异常数据检测方法。根据时间相关性利用Hampel滤波器检测异常数据点,保证数据的连续性,使用改进的基于滑动时间窗的DBSCAN算法,检测异常数据集。实验结果表明,所提方法和其它方法相比,实现了分层的异常数据检测,在保证检测精度的同时准确标注出了异常数据集,具有空间复杂度小的优势。展开更多
基金funded by Stefan cel Mare University of Suceava,Romania.
文摘In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthcare WBANs are the black hole and sink hole attacks.Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path.Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness.This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score(PCS)and an MK-Means algorithm,which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory issues.First,the gathered training data feature count is reduced through data pre-processing in the PCS.Second,the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification.Third,certain attack detection measures given by the intrusion detection system,such as the number of data packages trans-received,are identified by the MK-Means algorithm.This study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate,low communication overhead,and reduced end-to-end delay in the network and improves the accuracy of biomedical data.
文摘Noninvasive detection of body composition plays a significant role in the improvement of life quality and reduction in complications of the patients,and the near-infrared(NIR)spectroscopy,with the advantages of painlessness and convenience,is considered as the most promising tool for the online noninvasive monitoring of body composition.However,quite different from other fields of online detection using NIR spectroscopy,such as food safety and environment monitoring,noninvasive detection of body composit ion demands higher precision of the instruments as well as more rigor-ousness of measurement conditions.Therefore,new challenges emerge when NIR spectroscopy is applied to the noninvasive detection of body composition,which,in this paper,are first concluded from the aspects of measurement methods,measurement conditions,instrument precision,multi-component influence,individual difference and novel weak signal extraction method based on our previous research in the cutting edge field of NIR noninvasive blood glucose detection.Moreover,novel ideas and approaches of our group to solve these problems are introduced,which may provide evidence for the future development of noninvasive blood glucose detection,and further contibute to the noninvasive detection of other body compositions using NIR spectroscopy.
基金Makkah Digital Gate Initiatives funded this research work under Grant Number(MDP-IRI-8-2020).Emirate of Makkah Province and King Abdulaziz University,Jeddah,Saudi Arabia.https://science.makkah.kau.edu.sa/Default-101888-AR.
文摘This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro body gestures,which will be mapped to emotions and appropriate engagement states.The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames.We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset.The adopted C3D model was used based on two different approaches;as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pretrained model.Our model was tested and its performance was evaluated and compared to the existing models.It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%.The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’engagement levels.
基金This work was supported by the natural science foundation of Henan province(004061000)
文摘Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to the peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.
基金supported in part by the National Key Research and Development Program of China(No. 2018YFC0309104)the Construction System Science and Technology Project of Jiangsu Province (No.2021JH03)。
文摘Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.
基金Shanghai Leading Academic Discipline Project,China(No.B602)Patent Second Development Project of Science and Technology Commission of Shanghai Municipality,China(No.05dz52038)
文摘The welding fixtures are the most important devices for an auto body welding assembly line. The current special fixtures used by many automotive manufactures are only fit for one or several specific welding processes, and the dimensional problem in the circle due to several variation sources accumulation has no adjustment. The active error compensating welding fixture system for auto body is designed and manufactured. The detecting model, coordinate transformation model, and adjusting model based on auto body coordinate system are presented. The dowel pin modular design is adopted in the structure of the fixture to suit different workpieces with some similar characteristics. The online detection and adaptive control system using eddy current sensors and adaptive adjusting devices is analyzed. Three kinds of the left rear wheel covers SGM60 are selected to test workpieces of the developed system, and the active error compensating experiments are performed in the lab for many times. Test results show the validity of mechanism reconfigurations, on-line detections and error compensations of the developed welding fixture.
文摘针对无线体域网(wireless body area network,WBAN)异常数据检测方法忽视人体异常数据的连续性,缺乏异常数据集检测等问题,提出一种基于Hampel滤波器和DBSCAN分层的WBAN异常数据检测方法。根据时间相关性利用Hampel滤波器检测异常数据点,保证数据的连续性,使用改进的基于滑动时间窗的DBSCAN算法,检测异常数据集。实验结果表明,所提方法和其它方法相比,实现了分层的异常数据检测,在保证检测精度的同时准确标注出了异常数据集,具有空间复杂度小的优势。
