With the gradual development of the 5G industry network and applications,each industry application has various network performance requirements,while customers hope to upgrade their industrial structures by leveraging...With the gradual development of the 5G industry network and applications,each industry application has various network performance requirements,while customers hope to upgrade their industrial structures by leveraging 5G technologies.The guarantee of service level agreement(SLA)requirements is becoming more and more important,especially SLA performance indicators,such as delay,jitter,bandwidth,etc.For network operators to fulfill customer’s requirements,emerging network technologies such as time-sensitive networking(TSN),edge computing(EC)and network slicing are introduced into the mobile network to improve network performance,which increase the complexity of the network operation and maintenance(O&M),as well as the network cost.As a result,operators urgently need new solutions to achieve low-cost and high-efficiency network SLA management.In this paper,a digital twin network(DTN)solution is innovatively proposed to achieve the mapping and full lifecycle management of the end-to-end physical network.All the network operation policies such as configuration and modification can be generated and verified inside the digital twin network first to make sure that the SLA requirements can be fulfilled without affecting the related network environment and the performance of the other network services,making network operation and maintenance more effective and accurate.展开更多
This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy...This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy singleton fetuses within 3 days of delivery. Three input variables were used to construct the ANN model: abdominal circumference (AC), ab-dominal diameter (AD), biparietal diameter (BPD). Then, a total of 121 twin fetuses were assessed sub-sequently as the validation group. In validation group, the mean absolute error and the mean absolute per-cent error between estimated fetal weight and actual fetal weight was 261.77 g and 7.81%, respectively. Results show that, twin estimation of birth weight by ultrasound correlates fairly well with the actual weights of twin fetuses.展开更多
Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the ima...Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.展开更多
基金This work was supported by the National Key Research and Development Program of China(2020YFB1806801,2020YFB1806800)the National Natural Science Foundation of China(61773382).
文摘With the gradual development of the 5G industry network and applications,each industry application has various network performance requirements,while customers hope to upgrade their industrial structures by leveraging 5G technologies.The guarantee of service level agreement(SLA)requirements is becoming more and more important,especially SLA performance indicators,such as delay,jitter,bandwidth,etc.For network operators to fulfill customer’s requirements,emerging network technologies such as time-sensitive networking(TSN),edge computing(EC)and network slicing are introduced into the mobile network to improve network performance,which increase the complexity of the network operation and maintenance(O&M),as well as the network cost.As a result,operators urgently need new solutions to achieve low-cost and high-efficiency network SLA management.In this paper,a digital twin network(DTN)solution is innovatively proposed to achieve the mapping and full lifecycle management of the end-to-end physical network.All the network operation policies such as configuration and modification can be generated and verified inside the digital twin network first to make sure that the SLA requirements can be fulfilled without affecting the related network environment and the performance of the other network services,making network operation and maintenance more effective and accurate.
文摘This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy singleton fetuses within 3 days of delivery. Three input variables were used to construct the ANN model: abdominal circumference (AC), ab-dominal diameter (AD), biparietal diameter (BPD). Then, a total of 121 twin fetuses were assessed sub-sequently as the validation group. In validation group, the mean absolute error and the mean absolute per-cent error between estimated fetal weight and actual fetal weight was 261.77 g and 7.81%, respectively. Results show that, twin estimation of birth weight by ultrasound correlates fairly well with the actual weights of twin fetuses.
基金This work was jointly supported by the Special Fund for Transformation and Upgrade of Jiangsu Industry and Information Industry-Key Core Technologies(Equipment)Key Industrialization Projects in 2022(No.CMHI-2022-RDG-004):“Key Technology Research for Development of Intelligent Wind Power Operation and Maintenance Mothership in Deep Sea”.
文摘Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.
