Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accura...Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
Objective:To perform a meta-analysis to evaluate the diagnostic performance of computed tomography(CT) and transthoracic echocardiography(TTE) in complex congenital heart diseases(CHD) in China.Methods:MEDLINE,Cochran...Objective:To perform a meta-analysis to evaluate the diagnostic performance of computed tomography(CT) and transthoracic echocardiography(TTE) in complex congenital heart diseases(CHD) in China.Methods:MEDLINE,Cochrane library and China National Knowledge Infrastructure(CNKI) database from January 1966 to October 2010,were searched for initial studies in China.All the studies,published in English or Chinese,used TTE,CT,or both as diagnostic tests for CHD and reported the rate of true-positive,true-negative,false-positive and false-negative diagnoses of CHD from TTE and CT findings with the surgical results as the 'gold-standard'(15 studies,XX patients) were collected.The statistic software package,'Meta-Disc 1.4',was used to conduct data analysis.A covariate analysis was used to evaluate the influence of patient or study-related factors on sensitivity.Results:Pooled sensitivity for diagnosis of CHD were 95% [95% confidence interval(CI):94%~96%] for CT studies and 87%(95% CI:85%~88%) for TTE studies.The difference between the pooled sensitivity of CT and that of TTE was statistically significant(P<0.001).TTE had higher sensitivity [0.96(95% CI:0.94~0.97)] for cardiac malformation but lower sensitivity [0.78(95% CI:0.76~0.81)] for extracardiac malformation than CT.Conclusion:CT can provide added diagnostic information compared with TTE in patients with CHD in China,especially for patients suspected of extracardiac malformation.展开更多
A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d...A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d<0.075 mm,0.075 mm≤d<0.1 mm,0.1 mm≤d<0.2 mm,0.2 mm≤d<0.5 mm and 0.5 mm≤d<1.0 mm)to study the structures and particle contacts of granite residual soil.The X-ray micro computed tomography method was used to reconstruct the microstructure of granite residual soil.The particle was identified and regularized using principal component analysis(PCA).The particle contacts and geometrical characteristics in 3D space were analyzed and summarized using statistical analyses.The results demonstrate that the main types of contact among the particles are face-face,face-angle,face-edge,edge-edge,edge-angle and angle-angle contacts for particle sizes less than 0.2 mm.When the particle sizes are greater than 0.2 mm,the contacts are effectively summarized as face-face,face-angle,face-edge,edge-edge,edge-angle,angle-angle,sphere-sphere,sphere-face,sphere-edge and sphere-angle contacts.The differences in porosity among the original sample,reconstructed sample and regularized sample are closely related to the water-swelling and water-disintegrable characteristics of granite residual soil.展开更多
Micro computed tomography (Micro-CT) was applied to obtain three-dimensional images of the microstructure of cement paste (water-to-cement mass ratio of 0.5) at different ages. By using the Amira software, component p...Micro computed tomography (Micro-CT) was applied to obtain three-dimensional images of the microstructure of cement paste (water-to-cement mass ratio of 0.5) at different ages. By using the Amira software, component phases of the cement paste such as pores, hydration products, and unhydrated clinker particles were segmented from each other based on their 3D image grey levels; their relative contents were also calculated with the software, and the data are 61.2%, 0% and 38.8% at the beginning of hydration and 11.8%, 78.5% and 9.7% at 28 d age, respectively. The hydration degree of cement paste at different ages was compared with the experimental data acquired by loss on ignition (LOI) tests. The results show that the calculated and measured data reasonably agree with each other, which indicates that micro-CT is a useful and reliable approach to characterize the micro structure evolution of hydrating cement paste.展开更多
Aptamers are a type of single-chain oligonucleotide that can combine with a specific target.Due to their simple preparation,easy modification,stable structure and reusability,aptamers have been widely applied as bioch...Aptamers are a type of single-chain oligonucleotide that can combine with a specific target.Due to their simple preparation,easy modification,stable structure and reusability,aptamers have been widely applied as biochemical sensors for medicine,food safety and environmental monitoring.However,there is little research on aptamer-target binding mechanisms,which limits their application and development.Computational simulation has gained much attention for revealing aptamer-target binding mechanisms at the atomic level.This work summarizes the main simulation methods used in the mechanistic analysis of aptamer-target complexes,the characteristics of binding between aptamers and different targets(metal ions,small organic molecules,biomacromolecules,cells,bacteria and viruses),the types of aptamer-target interactions and the factors influencing their strength.It provides a reference for further use of simulations in understanding aptamer-target binding mechanisms.展开更多
By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal serv...By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.展开更多
A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process u...A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-f...Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.展开更多
文摘Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
文摘Objective:To perform a meta-analysis to evaluate the diagnostic performance of computed tomography(CT) and transthoracic echocardiography(TTE) in complex congenital heart diseases(CHD) in China.Methods:MEDLINE,Cochrane library and China National Knowledge Infrastructure(CNKI) database from January 1966 to October 2010,were searched for initial studies in China.All the studies,published in English or Chinese,used TTE,CT,or both as diagnostic tests for CHD and reported the rate of true-positive,true-negative,false-positive and false-negative diagnoses of CHD from TTE and CT findings with the surgical results as the 'gold-standard'(15 studies,XX patients) were collected.The statistic software package,'Meta-Disc 1.4',was used to conduct data analysis.A covariate analysis was used to evaluate the influence of patient or study-related factors on sensitivity.Results:Pooled sensitivity for diagnosis of CHD were 95% [95% confidence interval(CI):94%~96%] for CT studies and 87%(95% CI:85%~88%) for TTE studies.The difference between the pooled sensitivity of CT and that of TTE was statistically significant(P<0.001).TTE had higher sensitivity [0.96(95% CI:0.94~0.97)] for cardiac malformation but lower sensitivity [0.78(95% CI:0.76~0.81)] for extracardiac malformation than CT.Conclusion:CT can provide added diagnostic information compared with TTE in patients with CHD in China,especially for patients suspected of extracardiac malformation.
基金Projects(41572277,41877229)supported by the National Natural Science Foundation of ChinaProject(2015A030313118)supported by the Natural Science Foundation of Guangdong Province,ChinaProject(201607010023)supported by the Science and Technology Program of Guangzhou,China
文摘A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d<0.075 mm,0.075 mm≤d<0.1 mm,0.1 mm≤d<0.2 mm,0.2 mm≤d<0.5 mm and 0.5 mm≤d<1.0 mm)to study the structures and particle contacts of granite residual soil.The X-ray micro computed tomography method was used to reconstruct the microstructure of granite residual soil.The particle was identified and regularized using principal component analysis(PCA).The particle contacts and geometrical characteristics in 3D space were analyzed and summarized using statistical analyses.The results demonstrate that the main types of contact among the particles are face-face,face-angle,face-edge,edge-edge,edge-angle and angle-angle contacts for particle sizes less than 0.2 mm.When the particle sizes are greater than 0.2 mm,the contacts are effectively summarized as face-face,face-angle,face-edge,edge-edge,edge-angle,angle-angle,sphere-sphere,sphere-face,sphere-edge and sphere-angle contacts.The differences in porosity among the original sample,reconstructed sample and regularized sample are closely related to the water-swelling and water-disintegrable characteristics of granite residual soil.
基金Project(2009CB623201) supported by the National Basic Research Program of ChinaProjects(50902106, 51272193) supported by the National Natural Science Foundation of ChinaProject(NCET-10-0660) supported by New Century Excellent Talents in Universities of China
文摘Micro computed tomography (Micro-CT) was applied to obtain three-dimensional images of the microstructure of cement paste (water-to-cement mass ratio of 0.5) at different ages. By using the Amira software, component phases of the cement paste such as pores, hydration products, and unhydrated clinker particles were segmented from each other based on their 3D image grey levels; their relative contents were also calculated with the software, and the data are 61.2%, 0% and 38.8% at the beginning of hydration and 11.8%, 78.5% and 9.7% at 28 d age, respectively. The hydration degree of cement paste at different ages was compared with the experimental data acquired by loss on ignition (LOI) tests. The results show that the calculated and measured data reasonably agree with each other, which indicates that micro-CT is a useful and reliable approach to characterize the micro structure evolution of hydrating cement paste.
文摘Aptamers are a type of single-chain oligonucleotide that can combine with a specific target.Due to their simple preparation,easy modification,stable structure and reusability,aptamers have been widely applied as biochemical sensors for medicine,food safety and environmental monitoring.However,there is little research on aptamer-target binding mechanisms,which limits their application and development.Computational simulation has gained much attention for revealing aptamer-target binding mechanisms at the atomic level.This work summarizes the main simulation methods used in the mechanistic analysis of aptamer-target complexes,the characteristics of binding between aptamers and different targets(metal ions,small organic molecules,biomacromolecules,cells,bacteria and viruses),the types of aptamer-target interactions and the factors influencing their strength.It provides a reference for further use of simulations in understanding aptamer-target binding mechanisms.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900600)the National Natural Science Foundation of China(61971041+2 种基金62001027)the Beijing Natural Science Foundation(M22001)the Technological Innovation Program of Beijing Institute of Technology(2022CX01027).
文摘By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900603)the National Natural Science Foundation of China(61831008).
文摘A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
文摘Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
