Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-obj...Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.展开更多
Building Information Modelling (BIM) is a methodology focused on the centralization and sharing of the project information among all professionals involved, supported on the generation and manipulation of a three-dime...Building Information Modelling (BIM) is a methodology focused on the centralization and sharing of the project information among all professionals involved, supported on the generation and manipulation of a three-dimensional (3D) digital BIM model. This methodology allows a close collaboration between the architect and the structural engineer and an adequate manipulation of the structural BIM model database, on the definition of multitasks. The collaboration allowed between all disciplines, avoid the detection of conflicts and data omission after in the construction place. Two BIM structural design cases were developed, using Revit as the modelling system and Robot as the structural software. Concerning the structural project the interoperability capacity between the software is still a limitation that engineers must be warned of. In the present study, the benefits and limitations identified within the communication and integration of distinct disciplines and on the development of most frequent multitasks normally related with a structural project, were considered.展开更多
Because of its strong ability to solve problems,evolutionary multitask optimization(EMTO)algorithms have been widely studied recently.Evolutionary algorithms have the advantage of fast searching for the optimal soluti...Because of its strong ability to solve problems,evolutionary multitask optimization(EMTO)algorithms have been widely studied recently.Evolutionary algorithms have the advantage of fast searching for the optimal solution,but it is easy to fall into local optimum and difficult to generalize.Combining evolutionary multitask algorithms with evolutionary optimization algorithms can be an effective method for solving these problems.Through the implicit parallelism of tasks themselves and the knowledge transfer between tasks,more promising individual algorithms can be generated in the evolution process,which can jump out of the local optimum.How to better combine the two has also been studied more and more.This paper explores the existing evolutionary multitasking theory and improvement scheme in detail.Then,it summarizes the application of EMTO in different scenarios.Finally,according to the existing research,the future research trends and potential exploration directions are revealed.展开更多
Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topograp...Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topographical map,and an improved adaptive differential evolution(IADE)algorithm is proposed for single UAV multitasking.As an optimized problem,the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem.Therefore,the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions,which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value,letting the algorithm exploration and development more reasonable.The experimental results implicate that the IADE algorithm has better performance,higher convergence and efficiency to solve the multitasking problem compared with other algorithms.展开更多
The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning(SESRL)on perceptions of academic performance and views in university students during the COVID-19 pan-demic...The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning(SESRL)on perceptions of academic performance and views in university students during the COVID-19 pan-demic in Mexico.264 university students fulfilled an online questionnaire.It was observed that multitasking beha-vior negatively influences SESRL(-0.203),while SESRL showed a positive influence of 0.537 on perceptions of academic performance,and multitasking behavior had an influence of-0.097 on the perception of academic per-formance.Cronbach’s alpha and Average Variance Extracted values were 0.809 and 0.577(multitasking behavior),0.819 and 0.626(SESRL),0.873 and 0.725(perceptions of academic performance),respectively.The results of the bootstrapping test showed that the path coefficients were significant.The study outcomes can support new plans in universities to ensure the best academic outcomes.Our study showed evidence of the COVID-19 impact on education behavior.This study’s novelty is based on using the partial least square structural equation modeling(PLS-SEM)technique to evaluate these variables.展开更多
With the sharp increase of China's in-orbit spacecraft and the constraint TT&C resources,a mathe- matical model for optimal TT&C resource allocation is proposed,and the TT&C facility remote monitoring ...With the sharp increase of China's in-orbit spacecraft and the constraint TT&C resources,a mathe- matical model for optimal TT&C resource allocation is proposed,and the TT&C facility remote monitoring function is designed to achieve the multitask operation pattern under the unified management of the network management center.With this pattern,the TT&C network management and the spacecraft management are separated,which is quite different from the previous pattern.Further,a novel spacecraft TT&C technique based on spacecraft control language is developed,and the telecommanding pattern is designed to address the spacecraft operation problems. The engineering application shows that this pattern fundamentally improves the TT&C network capability,increases the resource efficiency,and satisfies the efficient,accurate,and flexible operation of spacecraft.展开更多
Aiming at scheduling problems of networked control system (NCS) used to fulfill motion synthesis and cooperation control of the distributed multi-mechatronic systems, the differences of network scheduling and task sch...Aiming at scheduling problems of networked control system (NCS) used to fulfill motion synthesis and cooperation control of the distributed multi-mechatronic systems, the differences of network scheduling and task scheduling are compared, and the mathematic description of task scheduling is presented. A performance index function of task scheduling of NCS according to task balance and traffic load matching principles is defined. According to this index, a static scheduling method is designed and implemented to controlling task set simulation of the DCY100 transportation vehicle. The simulation results are applied successfully to practical engineering in this case so as to validate the effectiveness of the proposed performance index and scheduling algorithm.展开更多
Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a mult...Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a multitask multiscale residual attention network(MMRAN)to simultaneously solve the problem of accurately segmenting and classifying brain tumors.The proposed MMRAN is based on U-Net,and a parallel branch is added at the end of the encoder as the classification network.First,we propose a novel multiscale residual attention module(MRAM)that can aggregate contextual features and combine channel attention and spatial attention better and add it to the shared parameter layer of MMRAN.Second,we propose a method of dynamic weight training that can improve model performance while minimizing the need for multiple experiments to determine the optimal weights for each task.Finally,prior knowledge of brain tumors is added to the postprocessing of segmented images to further improve the segmentation accuracy.We evaluated MMRAN on a brain tumor data set containing meningioma,glioma,and pituitary tumors.In terms of segmentation performance,our method achieves Dice,Hausdorff distance(HD),mean intersection over union(MIoU),and mean pixel accuracy(MPA)values of 80.03%,6.649 mm,84.38%,and 89.41%,respectively.In terms of classification performance,our method achieves accuracy,recall,precision,and F1-score of 89.87%,90.44%,88.56%,and 89.49%,respectively.Compared with other networks,MMRAN performs better in segmentation and classification,which significantly aids medical professionals in brain tumor management.The code and data set are available at https://github.com/linkenfaqiu/MMRAN.展开更多
Predicting entities in knowledge graphs is a crucial research area,and convolutional neural networks(CNNs)have exhibited significant performance due to their ability to generate expressive feature embeddings.However,se...Predicting entities in knowledge graphs is a crucial research area,and convolutional neural networks(CNNs)have exhibited significant performance due to their ability to generate expressive feature embeddings.However,sev-eral existing methods in thisfield tend to disrupt entities and relational embed-dings,disregarding the original translation characteristics in triples,leading to incomplete feature extraction.To address this issue and preserve the translation characteristics of triples,the present study introduces a novel representation tech-nique,termed MultiGNN.The suggested approach uses a graph convolutional neural network for encoding and implements a parameter sharing technique.It employs a convolutional neural network and a translation model as decoders.The model’s parameter space is expanded to effectively integrate translation charac-teristics into the convolutional neural network,which allows it to capture these characteristics and enhance the model’s performance.The proposed method in this paper has demonstrated significant enhancements in several metrics on the public benchmark dataset when compared to the baseline method.展开更多
Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on...Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.展开更多
The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies...The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network(MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.展开更多
基金supported in part by the Shanghai Aerospace Science and Technology Innovation Foundation(No.SAST 2021-026)the Fund of Prospec⁃tive Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics(NUAA).
文摘随着空间技术的飞速发展,空间态势感知能力需求不断增加。与传统光学传感器相比,逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)具有全天候、远距离高分辨率成像的能力,且成像不受光照条件的影响。此外,空间态势感知系统需要对周围航天器进行准确的评估,因此对空间目标部件识别能力的需求日益迫切。本文提出了一种基于YOLOv5结构的Multitask⁃YOLO网络,用于卫星ISAR图像中卫星帆板的识别和分割。首先,本文添加了分割解耦头来实现网络的分割功能。然后用空间金字塔池快速算法(Spatial pyramid pooling fast,SPPF)和距离交并比算法(Distance intersection over union,DIoU)代替原有结构,避免图像失真,加快收敛速度。通过在通道中引入注意机制,提高了分割和识别的准确性。最后使用模拟卫星的ISAR图像进行实验。结果表明,所提出的Multitask⁃YOLO网络高效、准确地实现了部件的识别和分割。与其他的识别和分割网络相比,该网络的平均精度(mean Average precision,mAP)和平均交并比(mean Intersection over union,mIoU)提高了约5%。此外,该网络的运行速度高达16.4 GFLOP,优于传统的多任务网络的性能。
基金supported in part by the National Natural Science Fund for Outstanding Young Scholars of China (61922072)the National Natural Science Foundation of China (62176238, 61806179, 61876169, 61976237)+2 种基金China Postdoctoral Science Foundation (2020M682347)the Training Program of Young Backbone Teachers in Colleges and Universities in Henan Province (2020GGJS006)Henan Provincial Young Talents Lifting Project (2021HYTP007)。
文摘Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.
文摘Building Information Modelling (BIM) is a methodology focused on the centralization and sharing of the project information among all professionals involved, supported on the generation and manipulation of a three-dimensional (3D) digital BIM model. This methodology allows a close collaboration between the architect and the structural engineer and an adequate manipulation of the structural BIM model database, on the definition of multitasks. The collaboration allowed between all disciplines, avoid the detection of conflicts and data omission after in the construction place. Two BIM structural design cases were developed, using Revit as the modelling system and Robot as the structural software. Concerning the structural project the interoperability capacity between the software is still a limitation that engineers must be warned of. In the present study, the benefits and limitations identified within the communication and integration of distinct disciplines and on the development of most frequent multitasks normally related with a structural project, were considered.
基金Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2022JM-327 and in part by the CAAI-Huawei MindSpore Academic Open Fund.
文摘Because of its strong ability to solve problems,evolutionary multitask optimization(EMTO)algorithms have been widely studied recently.Evolutionary algorithms have the advantage of fast searching for the optimal solution,but it is easy to fall into local optimum and difficult to generalize.Combining evolutionary multitask algorithms with evolutionary optimization algorithms can be an effective method for solving these problems.Through the implicit parallelism of tasks themselves and the knowledge transfer between tasks,more promising individual algorithms can be generated in the evolution process,which can jump out of the local optimum.How to better combine the two has also been studied more and more.This paper explores the existing evolutionary multitasking theory and improvement scheme in detail.Then,it summarizes the application of EMTO in different scenarios.Finally,according to the existing research,the future research trends and potential exploration directions are revealed.
文摘Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topographical map,and an improved adaptive differential evolution(IADE)algorithm is proposed for single UAV multitasking.As an optimized problem,the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem.Therefore,the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions,which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value,letting the algorithm exploration and development more reasonable.The experimental results implicate that the IADE algorithm has better performance,higher convergence and efficiency to solve the multitasking problem compared with other algorithms.
文摘The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning(SESRL)on perceptions of academic performance and views in university students during the COVID-19 pan-demic in Mexico.264 university students fulfilled an online questionnaire.It was observed that multitasking beha-vior negatively influences SESRL(-0.203),while SESRL showed a positive influence of 0.537 on perceptions of academic performance,and multitasking behavior had an influence of-0.097 on the perception of academic per-formance.Cronbach’s alpha and Average Variance Extracted values were 0.809 and 0.577(multitasking behavior),0.819 and 0.626(SESRL),0.873 and 0.725(perceptions of academic performance),respectively.The results of the bootstrapping test showed that the path coefficients were significant.The study outcomes can support new plans in universities to ensure the best academic outcomes.Our study showed evidence of the COVID-19 impact on education behavior.This study’s novelty is based on using the partial least square structural equation modeling(PLS-SEM)technique to evaluate these variables.
基金supported by the China Postdotoral Science Foundation (20060401004).
文摘With the sharp increase of China's in-orbit spacecraft and the constraint TT&C resources,a mathe- matical model for optimal TT&C resource allocation is proposed,and the TT&C facility remote monitoring function is designed to achieve the multitask operation pattern under the unified management of the network management center.With this pattern,the TT&C network management and the spacecraft management are separated,which is quite different from the previous pattern.Further,a novel spacecraft TT&C technique based on spacecraft control language is developed,and the telecommanding pattern is designed to address the spacecraft operation problems. The engineering application shows that this pattern fundamentally improves the TT&C network capability,increases the resource efficiency,and satisfies the efficient,accurate,and flexible operation of spacecraft.
基金This project is supported by National Natural Science Foundation of China (No. 50575013)
文摘Aiming at scheduling problems of networked control system (NCS) used to fulfill motion synthesis and cooperation control of the distributed multi-mechatronic systems, the differences of network scheduling and task scheduling are compared, and the mathematic description of task scheduling is presented. A performance index function of task scheduling of NCS according to task balance and traffic load matching principles is defined. According to this index, a static scheduling method is designed and implemented to controlling task set simulation of the DCY100 transportation vehicle. The simulation results are applied successfully to practical engineering in this case so as to validate the effectiveness of the proposed performance index and scheduling algorithm.
基金This paper was supported by National Natural Science Foundation of China(No.61977063 and 61872020).The authors thank all the patients for providing their MRI images and School of Biomedical Engineering at Southern Medical University,China for providing the brain tumor data set.We appreciate Dr.Fenfen Li,Wenzhou Eye Hospital,Wenzhou Medical University,China,for her support with clinical consulting and language editing.
文摘Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a multitask multiscale residual attention network(MMRAN)to simultaneously solve the problem of accurately segmenting and classifying brain tumors.The proposed MMRAN is based on U-Net,and a parallel branch is added at the end of the encoder as the classification network.First,we propose a novel multiscale residual attention module(MRAM)that can aggregate contextual features and combine channel attention and spatial attention better and add it to the shared parameter layer of MMRAN.Second,we propose a method of dynamic weight training that can improve model performance while minimizing the need for multiple experiments to determine the optimal weights for each task.Finally,prior knowledge of brain tumors is added to the postprocessing of segmented images to further improve the segmentation accuracy.We evaluated MMRAN on a brain tumor data set containing meningioma,glioma,and pituitary tumors.In terms of segmentation performance,our method achieves Dice,Hausdorff distance(HD),mean intersection over union(MIoU),and mean pixel accuracy(MPA)values of 80.03%,6.649 mm,84.38%,and 89.41%,respectively.In terms of classification performance,our method achieves accuracy,recall,precision,and F1-score of 89.87%,90.44%,88.56%,and 89.49%,respectively.Compared with other networks,MMRAN performs better in segmentation and classification,which significantly aids medical professionals in brain tumor management.The code and data set are available at https://github.com/linkenfaqiu/MMRAN.
基金This work was supported by the National Key R&D Program of China under Grant No.2020YFB1710200.
文摘Predicting entities in knowledge graphs is a crucial research area,and convolutional neural networks(CNNs)have exhibited significant performance due to their ability to generate expressive feature embeddings.However,sev-eral existing methods in thisfield tend to disrupt entities and relational embed-dings,disregarding the original translation characteristics in triples,leading to incomplete feature extraction.To address this issue and preserve the translation characteristics of triples,the present study introduces a novel representation tech-nique,termed MultiGNN.The suggested approach uses a graph convolutional neural network for encoding and implements a parameter sharing technique.It employs a convolutional neural network and a translation model as decoders.The model’s parameter space is expanded to effectively integrate translation charac-teristics into the convolutional neural network,which allows it to capture these characteristics and enhance the model’s performance.The proposed method in this paper has demonstrated significant enhancements in several metrics on the public benchmark dataset when compared to the baseline method.
基金supported by MRC,UK (MC_PC_17171)Royal Society,UK (RP202G0230)+8 种基金BHF,UK (AA/18/3/34220)Hope Foundation for Cancer Research,UK (RM60G0680)GCRF,UK (P202PF11)Sino-UK Industrial Fund,UK (RP202G0289)LIAS,UK (P202ED10,P202RE969)Data Science Enhancement Fund,UK (P202RE237)Fight for Sight,UK (24NN201)Sino-UK Education Fund,UK (OP202006)BBSRC,UK (RM32G0178B8).
文摘Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.
基金supported by the National Natural Science Foundation of China(No.61976247)
文摘The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network(MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.
