Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium alloy.By combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulate...Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium alloy.By combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulated to obtain the contour accurately.Therefore,an algorithm forαphase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed.Firstly,through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting,the information maps of theαphase contour of the TB6 titanium alloy at different scales are obtained;then the information map of the smallest scale contour is used as a benchmark,the neighborhood is constructed to judge the deviation of other scale contour information,and the corresponding weight value is calculated;finally,Gaussian function is used to weight and fuse the deviation information,and the contour detection result of TB6 titanium alloyαphase is obtained.In the Visual Studio 2013 environment,484 metallographic images with different temperatures,strain rates,and magnifications were tested.The results show that the performance evaluation F value of the proposed algorithm is 0.915,which can effectively improve the accuracy ofαphase contour detection of TB6 titanium alloy.展开更多
The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the reali...The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence(AI)-based analytics,empowering seamless integration across the entire Continuum(Edge,Fog,Core,Cloud).This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning(FL)-based anomaly detection in B5Gnetworks.By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm,which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior,the systemorchestrates network intelligence to detect and prevent cyber-attacks.Integrated into a B5G Zero-touch Service Management(ZSM)aligned Security Framework,the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors,enhancing reaction capabilities against cyber-attacks.The proposed FL architecture can be dynamically deployed across the B5G Continuum,utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling.Implementation includes FL enforcement operations for interoperability and extensibility,enabling dynamic deployment,configuration,and reconfiguration on demand.Performance validation of the proposed solution was conducted through dynamic orchestration,FL,and real-time anomaly detection processes using a practical test environment.Analysis of key performance metrics,leveraging the 5G-NIDD dataset,demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks,including real-time network monitoring and countermeasure implementation for mitigation.展开更多
Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging te...Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available.展开更多
In order to realize on-line quantitative detection on SF6 and effective control of nlnning state of SF6 high voltage power supply system, a concentrated SF6 quantitative ultrasonic on-line deteetion system has been de...In order to realize on-line quantitative detection on SF6 and effective control of nlnning state of SF6 high voltage power supply system, a concentrated SF6 quantitative ultrasonic on-line deteetion system has been developed based on the actual demand of electric power system consumers. There are four major characteristics in this system. Firstly, the gas of maximum 64 detection points is transferred through the specific air path to the detection devices to he detected and analyzed, thereby the electrical lines and the complicated installation of the collectors can be avoided; secondly, the differential technique is used to shield the influence of environmental factors, which effectively improves the accuracy of the acoustic detection; thirdly, the SF6 coneentration is determined by the speed and phase in the ultrasonic wave trans- mission process, therefore there is no secondary pollution for the purely physical means; finally, the ma- ture embedded technique is applied in this system to improve its intelligence and stability.展开更多
Objective: To investigate the clinical significance of serum tumor markers CA153, CA125, CA72-4 and FIB, IL-6 levels in the detection of ovarian cancer. Methods: A total of 40 patients with ovarian carcinoma treated i...Objective: To investigate the clinical significance of serum tumor markers CA153, CA125, CA72-4 and FIB, IL-6 levels in the detection of ovarian cancer. Methods: A total of 40 patients with ovarian carcinoma treated in our hospital from September 2015 to June 2017 were selected as the ovarian cancer group;40 patients with benign ovarian tumors in the same period were selected as the benign ovarian tumor group;40 healthy subjects were selected as the control group. The levels of tumor markers CA153, CA125, CA72-4 and FIB, IL-6 were compared between the three groups. Results: The CA153, CA125, CA72-4 and FIB, IL-6 levels of the ovarian cancer group were significantly higher than those in benign ovarian tumor group and the control group;these levels in the benign ovarian tumor group was not significantly different from those in the control group. With the increase of clinical phase, the levels of CA153, CA125 and CA72-4 in patients with ovarian cancer were gradually increased, and the levels in phase Ⅲ and Ⅳ were significantly higher than those in phase Ⅱ and in phase I;and the CA125 in phase Ⅱ was significantly higher than that in phase Ⅰ. The levels of FIB and IL-6 in phase Ⅱ and in phase Ⅲ, Ⅳ were significantly higher than those in phase I;the IL-6 level in phase Ⅲ, Ⅳ was significantly higher than that in phase Ⅱ. Conclusion: Serum tumor markers CA153, CA125, CA72-4 and FIB, IL-6 levels for ovarian cancer detection can be helpful for clinical diagnosis and worthy of promotion.展开更多
The next generation protocol IPv6 brings the new challenges to the information security. This paper presents the design and implementation of a network based intrusion detection system that support both IPv6 protocol ...The next generation protocol IPv6 brings the new challenges to the information security. This paper presents the design and implementation of a network based intrusion detection system that support both IPv6 protocol and IPv4 protocol. This system's architecture is focused on performance, simplicity, and scalability. There are four primary subsystems that make it up: the packet capture, the packet decoder, the detection engine, and the logging and alerting subsystem. This paper further describes a new approach to packet capture whose goal is to improve the performance of the capture process at high speeds. The evaluation shows that the system has a good performance to detect IPv6 attacks and IPv4 attacks, and achieves 61% correct detection rate with 20% false detection rate at the speed of 100 Mb·s^-1.展开更多
基金Supported by Hebei Provincial Key Laboratory for Software Engineering(Grant No.22567637H)the"Rail Vehicle Application Engineering"National International Science and Technology Cooperation Base Open Project Fund(Grant No.BMRV21KF09).
文摘Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium alloy.By combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulated to obtain the contour accurately.Therefore,an algorithm forαphase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed.Firstly,through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting,the information maps of theαphase contour of the TB6 titanium alloy at different scales are obtained;then the information map of the smallest scale contour is used as a benchmark,the neighborhood is constructed to judge the deviation of other scale contour information,and the corresponding weight value is calculated;finally,Gaussian function is used to weight and fuse the deviation information,and the contour detection result of TB6 titanium alloyαphase is obtained.In the Visual Studio 2013 environment,484 metallographic images with different temperatures,strain rates,and magnifications were tested.The results show that the performance evaluation F value of the proposed algorithm is 0.915,which can effectively improve the accuracy ofαphase contour detection of TB6 titanium alloy.
基金supported by the grants:PID2020-112675RBC44(ONOFRE-3),funded by MCIN/AEI/10.13039/501100011033Horizon Project RIGOUROUS funded by European Commission,GA:101095933TSI-063000-2021-{36,44,45,62}(Cerberus)funded by MAETD’s 2021 UNICO I+D Program.
文摘The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence(AI)-based analytics,empowering seamless integration across the entire Continuum(Edge,Fog,Core,Cloud).This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning(FL)-based anomaly detection in B5Gnetworks.By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm,which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior,the systemorchestrates network intelligence to detect and prevent cyber-attacks.Integrated into a B5G Zero-touch Service Management(ZSM)aligned Security Framework,the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors,enhancing reaction capabilities against cyber-attacks.The proposed FL architecture can be dynamically deployed across the B5G Continuum,utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling.Implementation includes FL enforcement operations for interoperability and extensibility,enabling dynamic deployment,configuration,and reconfiguration on demand.Performance validation of the proposed solution was conducted through dynamic orchestration,FL,and real-time anomaly detection processes using a practical test environment.Analysis of key performance metrics,leveraging the 5G-NIDD dataset,demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks,including real-time network monitoring and countermeasure implementation for mitigation.
基金supported by the National Key Research and Development Program of China(2020YFB1807500), the National Natural Science Foundation of China (62072360, 62001357, 62172438,61901367), the key research and development plan of Shaanxi province(2021ZDLGY02-09, 2020JQ-844)the Natural Science Foundation of Guangdong Province of China(2022A1515010988)+2 种基金Key Project on Artificial Intelligence of Xi'an Science and Technology Plan(2022JH-RGZN-0003)Xi'an Science and Technology Plan(20RGZN0005)the Xi'an Key Laboratory of Mobile Edge Computing and Security (201805052-ZD3CG36).
文摘Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available.
基金Supported by the National Natural Science Foundation of China (No. 10574038)
文摘In order to realize on-line quantitative detection on SF6 and effective control of nlnning state of SF6 high voltage power supply system, a concentrated SF6 quantitative ultrasonic on-line deteetion system has been developed based on the actual demand of electric power system consumers. There are four major characteristics in this system. Firstly, the gas of maximum 64 detection points is transferred through the specific air path to the detection devices to he detected and analyzed, thereby the electrical lines and the complicated installation of the collectors can be avoided; secondly, the differential technique is used to shield the influence of environmental factors, which effectively improves the accuracy of the acoustic detection; thirdly, the SF6 coneentration is determined by the speed and phase in the ultrasonic wave trans- mission process, therefore there is no secondary pollution for the purely physical means; finally, the ma- ture embedded technique is applied in this system to improve its intelligence and stability.
文摘Objective: To investigate the clinical significance of serum tumor markers CA153, CA125, CA72-4 and FIB, IL-6 levels in the detection of ovarian cancer. Methods: A total of 40 patients with ovarian carcinoma treated in our hospital from September 2015 to June 2017 were selected as the ovarian cancer group;40 patients with benign ovarian tumors in the same period were selected as the benign ovarian tumor group;40 healthy subjects were selected as the control group. The levels of tumor markers CA153, CA125, CA72-4 and FIB, IL-6 were compared between the three groups. Results: The CA153, CA125, CA72-4 and FIB, IL-6 levels of the ovarian cancer group were significantly higher than those in benign ovarian tumor group and the control group;these levels in the benign ovarian tumor group was not significantly different from those in the control group. With the increase of clinical phase, the levels of CA153, CA125 and CA72-4 in patients with ovarian cancer were gradually increased, and the levels in phase Ⅲ and Ⅳ were significantly higher than those in phase Ⅱ and in phase I;and the CA125 in phase Ⅱ was significantly higher than that in phase Ⅰ. The levels of FIB and IL-6 in phase Ⅱ and in phase Ⅲ, Ⅳ were significantly higher than those in phase I;the IL-6 level in phase Ⅲ, Ⅳ was significantly higher than that in phase Ⅱ. Conclusion: Serum tumor markers CA153, CA125, CA72-4 and FIB, IL-6 levels for ovarian cancer detection can be helpful for clinical diagnosis and worthy of promotion.
基金Supported by the National Natural Science Foun-dation of China (60572048)
文摘The next generation protocol IPv6 brings the new challenges to the information security. This paper presents the design and implementation of a network based intrusion detection system that support both IPv6 protocol and IPv4 protocol. This system's architecture is focused on performance, simplicity, and scalability. There are four primary subsystems that make it up: the packet capture, the packet decoder, the detection engine, and the logging and alerting subsystem. This paper further describes a new approach to packet capture whose goal is to improve the performance of the capture process at high speeds. The evaluation shows that the system has a good performance to detect IPv6 attacks and IPv4 attacks, and achieves 61% correct detection rate with 20% false detection rate at the speed of 100 Mb·s^-1.