The increasing penetration of PV power generation inevitably leads to the decline of system inertia,posing challenges to frequency stability.To this end,virtual inertia control has been proposed;however,it causes more...The increasing penetration of PV power generation inevitably leads to the decline of system inertia,posing challenges to frequency stability.To this end,virtual inertia control has been proposed;however,it causes more fluctuations of system inertia.To address this issue,a novel equivalent inertia evaluation method for multiple PV power generation under virtual inertia control is proposed.The total system inertia is first estimated based on historical or injected disturbance.Then,the total inertia of multiple PV power generation is directly calculated by subtracting the inertia of synchronous generators from the estimated system inertia.To improve practicality,a partition-based strategy is introduced,which divides the system into regions characterized by homogeneous frequency response behaviors.After partitioning,only the synchronous generator data within the region and inter-area transmission line power are required for evaluation,reducing the demand for PMU data compared to traditional methods requiring measurements at each PV connection point.Comprehensive simulation results in a 10-machine 39-bus system penetrated with multiple PV power generation validated the effectiveness of the proposed method.展开更多
Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of ...Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.展开更多
Because of explosive growth in Internet traffic and high complexity of heterogeneous networks, improving the routing and wavelength assignment （RWA） algorithm in underlying optical networks has become very important...Because of explosive growth in Internet traffic and high complexity of heterogeneous networks, improving the routing and wavelength assignment （RWA） algorithm in underlying optical networks has become very important. Where there are multiple links between different the node pairs, a traditional wavelength-assignment algorithm may be invalid for a wavelength-switched optical networks （WSON） that has directional blocking constraints. Also, impairments in network nodes and subsequent degradation of optical signals may cause modulation failure in the optical network. In this paper, we propose an RWA algorithm based on a novel evaluation model for a WSQN that has multiple constraints. The algorithm includes comprehensive evaluation model （CEM） and directional blocking constraint RWA based on CEM （DB-RWA）. Diverse constraints are abstracted into various constraint conditions in order to better assign routing and wavelength. We propose using the novel CEM to optimize routing according to an assessed value of constraints on transmission performance. This eliminates the effects of physical transmission impairments in a WSON. DB-RWA based on CEM abstracts directional blocking conditions in multiple links between network nodes into directional blocking constraints. It also satisfies rigorous network specifications and provides flexibility, scalability, and first-fit rate for the backbone, especially in multiple links between WSON nodes.展开更多
In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed i...In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.展开更多
Objective: To study the sonographic features of the primary site of papillary thyroid microcarcinoma(PTMC) for the prediction of cervical lymph node metastasis during preoperative diagnosis.Methods: A total of 710 PTM...Objective: To study the sonographic features of the primary site of papillary thyroid microcarcinoma(PTMC) for the prediction of cervical lymph node metastasis during preoperative diagnosis.Methods: A total of 710 PTMC patients between 2013 and 2016 with a diagnosis of cervical lymph node metastases were reviewed.We analyzed the sonographic features of the PTMC primary site to predict ipsilateral or central lymph node metastases in univariate and multivariate models.The ratio of abutment/perimeter of the PTMC primary site was utilized to evaluate cervical lymph node status.Results: Regarding clinical characteristics, multifocality and extrathyroidal extension were associated with cervical lymph node involvement.In the multivariate regression model, calcification and the abutment/perimeter ratio of lesions were evaluated as independent factors in level Ⅵ, ipsilateral or skip cervical lymph node metastases.The cut-off value of the ratio of abutment/perimeter of the PTMC primary site(25%) was significantly correlated with cervical lymph node metastases(P = 0.000).Conclusions: Independent sonographic features, including lesion size, lesion location, calcification, and the ratio of abutment/perimeter of the primary site, were associated with cervical lymph node metastases in PTMC patients.展开更多
Reliable vehicles are essential in vehicular networks for effective communication.Since vehicles in the network are dynamic,even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to...Reliable vehicles are essential in vehicular networks for effective communication.Since vehicles in the network are dynamic,even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences.In this paper,a Trust-Based Distributed DoS Misbehave Detection Approach(TBDDoSA-MD)is proposed to secure the Software-Defined Vehicular Network(SDVN).A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood.It uses the jamming technique by sending unnecessary signals in the network,as a result,the network performance degrades.Attacked vehicles in that network will no longer meet the service requests from other vehicles.Therefore,in this paper,we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles.Trust values are calculated based on direct trust and recommendations(indirect trust).These trust values help to decide whether a vehicle is legitimate or malicious.We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages.The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator,which uses OMNeT++and Simulation of Urban Mobility(SUMO).We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework(TBF)scheme using the following performance parameters such as detection accuracy,packet delivery ratio,detection time,and energy consumption.Simulation results show that the proposed work has a high detection accuracy of more than 90%while keeping the detection time as low as 30 s.展开更多
Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its ...Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.展开更多
基金supported by the science and technology project of State Grid Ningxia Electric Power Co.,Ltd.(5229DK23000C)the project of Ningxia Natural Science Foundation 2024AAC03745(B329DK24000S).
文摘The increasing penetration of PV power generation inevitably leads to the decline of system inertia,posing challenges to frequency stability.To this end,virtual inertia control has been proposed;however,it causes more fluctuations of system inertia.To address this issue,a novel equivalent inertia evaluation method for multiple PV power generation under virtual inertia control is proposed.The total system inertia is first estimated based on historical or injected disturbance.Then,the total inertia of multiple PV power generation is directly calculated by subtracting the inertia of synchronous generators from the estimated system inertia.To improve practicality,a partition-based strategy is introduced,which divides the system into regions characterized by homogeneous frequency response behaviors.After partitioning,only the synchronous generator data within the region and inter-area transmission line power are required for evaluation,reducing the demand for PMU data compared to traditional methods requiring measurements at each PV connection point.Comprehensive simulation results in a 10-machine 39-bus system penetrated with multiple PV power generation validated the effectiveness of the proposed method.
基金the Science and Technology Funding Project of Hunan Province,China(2023JJ50410)(HX)Key Laboratory of Tumor Precision Medicine,Hunan colleges and Universities Project(2019-379)(QL).
文摘Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.
基金supported in part by 973 Program(2010CB328204)NSFC project(60932004)RFDP Project(20090005110013)
文摘Because of explosive growth in Internet traffic and high complexity of heterogeneous networks, improving the routing and wavelength assignment （RWA） algorithm in underlying optical networks has become very important. Where there are multiple links between different the node pairs, a traditional wavelength-assignment algorithm may be invalid for a wavelength-switched optical networks （WSON） that has directional blocking constraints. Also, impairments in network nodes and subsequent degradation of optical signals may cause modulation failure in the optical network. In this paper, we propose an RWA algorithm based on a novel evaluation model for a WSQN that has multiple constraints. The algorithm includes comprehensive evaluation model （CEM） and directional blocking constraint RWA based on CEM （DB-RWA）. Diverse constraints are abstracted into various constraint conditions in order to better assign routing and wavelength. We propose using the novel CEM to optimize routing according to an assessed value of constraints on transmission performance. This eliminates the effects of physical transmission impairments in a WSON. DB-RWA based on CEM abstracts directional blocking conditions in multiple links between network nodes into directional blocking constraints. It also satisfies rigorous network specifications and provides flexibility, scalability, and first-fit rate for the backbone, especially in multiple links between WSON nodes.
基金Research Committee,National Technical University of Athens。
文摘In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.
基金supported by grants from the National Natural Science Foundation of China(Grant No.81771852)
文摘Objective: To study the sonographic features of the primary site of papillary thyroid microcarcinoma(PTMC) for the prediction of cervical lymph node metastasis during preoperative diagnosis.Methods: A total of 710 PTMC patients between 2013 and 2016 with a diagnosis of cervical lymph node metastases were reviewed.We analyzed the sonographic features of the PTMC primary site to predict ipsilateral or central lymph node metastases in univariate and multivariate models.The ratio of abutment/perimeter of the PTMC primary site was utilized to evaluate cervical lymph node status.Results: Regarding clinical characteristics, multifocality and extrathyroidal extension were associated with cervical lymph node involvement.In the multivariate regression model, calcification and the abutment/perimeter ratio of lesions were evaluated as independent factors in level Ⅵ, ipsilateral or skip cervical lymph node metastases.The cut-off value of the ratio of abutment/perimeter of the PTMC primary site(25%) was significantly correlated with cervical lymph node metastases(P = 0.000).Conclusions: Independent sonographic features, including lesion size, lesion location, calcification, and the ratio of abutment/perimeter of the primary site, were associated with cervical lymph node metastases in PTMC patients.
文摘Reliable vehicles are essential in vehicular networks for effective communication.Since vehicles in the network are dynamic,even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences.In this paper,a Trust-Based Distributed DoS Misbehave Detection Approach(TBDDoSA-MD)is proposed to secure the Software-Defined Vehicular Network(SDVN).A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood.It uses the jamming technique by sending unnecessary signals in the network,as a result,the network performance degrades.Attacked vehicles in that network will no longer meet the service requests from other vehicles.Therefore,in this paper,we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles.Trust values are calculated based on direct trust and recommendations(indirect trust).These trust values help to decide whether a vehicle is legitimate or malicious.We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages.The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator,which uses OMNeT++and Simulation of Urban Mobility(SUMO).We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework(TBF)scheme using the following performance parameters such as detection accuracy,packet delivery ratio,detection time,and energy consumption.Simulation results show that the proposed work has a high detection accuracy of more than 90%while keeping the detection time as low as 30 s.
基金supported in part by the National Science Foundation Project of P.R.China (No.61931001)the Fundamental Research Funds for the Central Universities under Grant (No.FRFAT-19-010)the Scientific and Technological Innovation Foundation of Foshan,USTB (No.BK20AF003)。
文摘Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.
