This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather event...This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.展开更多
In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In thi...In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.展开更多
Objective:To explore the application effect of“Internet+”nursing in patients discharged from hospital after liver cancer operation.Methods:A total of 90 patients who underwent hepatocellular carcinoma resection in S...Objective:To explore the application effect of“Internet+”nursing in patients discharged from hospital after liver cancer operation.Methods:A total of 90 patients who underwent hepatocellular carcinoma resection in Shaanxi Provincial People’s Hospital from November 2019 to August 2020 were divided into an observation group(n=45)and a control group(n=45)randomly by drawing lots.The control group received routine discharge health guidance while the observation group underwent an“Internet+”nursing through hospital Internet information platforms,online consultation,and so on.Patients of both groups returned to the hospital one month after discharge and filled in the questionnaire.The incidence of adverse reactions,quality of life,and level of hope were compared between the two groups 1 month after discharge.Results:The incidence of adverse reactions in the observation group was significantly lower than that of the control group,and the difference was statistically significant(P<0.05).The quality of life and level of hope of patients in the observation group were significantly higher than those in control group and the difference was statistically significant(P<0.05).Conclusion:“Internet+”nursing service plays a positive role in improving the patients’about their disease and their quality of life after discharge from hospital after liver cancer operation,which is worthy of popularization in clinical practice.展开更多
文摘This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.
基金supported in part by the National Key R&D Program of China under Grant 2018YFA0701601part by the National Natural Science Foundation of China(Grant No.U22A2002,61941104,62201605)part by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.
文摘Objective:To explore the application effect of“Internet+”nursing in patients discharged from hospital after liver cancer operation.Methods:A total of 90 patients who underwent hepatocellular carcinoma resection in Shaanxi Provincial People’s Hospital from November 2019 to August 2020 were divided into an observation group(n=45)and a control group(n=45)randomly by drawing lots.The control group received routine discharge health guidance while the observation group underwent an“Internet+”nursing through hospital Internet information platforms,online consultation,and so on.Patients of both groups returned to the hospital one month after discharge and filled in the questionnaire.The incidence of adverse reactions,quality of life,and level of hope were compared between the two groups 1 month after discharge.Results:The incidence of adverse reactions in the observation group was significantly lower than that of the control group,and the difference was statistically significant(P<0.05).The quality of life and level of hope of patients in the observation group were significantly higher than those in control group and the difference was statistically significant(P<0.05).Conclusion:“Internet+”nursing service plays a positive role in improving the patients’about their disease and their quality of life after discharge from hospital after liver cancer operation,which is worthy of popularization in clinical practice.
