In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utiliz...To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utilizing common knowledge.Knowledge transfer is the key to the effectiveness of EMT.Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance.There is plentiful knowledge in assistant tasks,and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks.EMT is thus confronted with a challenge to find appropriate knowledge.This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks.During the evolution process,better-performing candidates are supposed to have advantages in exploitation.Therefore,assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge.Specifically,the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population.Considering that target and assistant sub-populations have different characteristics,we use domain adaptation to reduce their distribution discrepancies.In this way,the trained classifier can classify assistant individuals more accurately,and truly useful knowledge can be selected for target tasks.The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.展开更多
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
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
Searching for the optimal cabin layout plan is an efective way to improve the efciency of the overall design and reduce a ship’s operation costs.The multitasking states of a ship involve several statuses when facing ...Searching for the optimal cabin layout plan is an efective way to improve the efciency of the overall design and reduce a ship’s operation costs.The multitasking states of a ship involve several statuses when facing diferent missions during a voyage,such as the status of the marine supply and emergency escape.The human fow and logistics between cabins will change as the state changes.An ideal cabin layout plan,which is directly impacted by the above-mentioned factors,can meet the diferent requirements of several statuses to a higher degree.Inevitable deviations exist in the quantifcation of human fow and logistics.Moreover,uncontrollability is present in the fow situation during actual operations.The coupling of these deviations and uncontrollability shows typical uncertainties,which must be considered in the design process.Thus,it is important to integrate the demands of the human fow and logistics in multiple states into an uncertainty parameter scheme.This research considers the uncertainties of adjacent and circulating strengths obtained after quantifying the human fow and logistics.Interval numbers are used to integrate them,a two-layer nested system of interval optimization is introduced,and diferent optimization algorithms are substituted for solving calculations.The comparison and analysis of the calculation results with deterministic optimization show that the conclusions obtained can provide feasible guidance for cabin layout scheme.展开更多
Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they prop...Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.展开更多
Developed a new program structure using in single chip computer system, which based on multitasking mechanism. Discussed the specific method for realization of the new structure. The applied sample is also provided.
This work is to observe the performance of PC based robot manipulator under general purpose （Windows）, Soft （Linux） and Hard （RT Linux） Real Time Operating Systems （OS）. The same open loop control system is ob...This work is to observe the performance of PC based robot manipulator under general purpose （Windows）, Soft （Linux） and Hard （RT Linux） Real Time Operating Systems （OS）. The same open loop control system is observed in different operating systems with and without multitasking environment. The Data Acquisition （DAQ, PLC-812PG） card is used as a hardware interface. From the experiment, it could be seen that in the non real time operating system （Windows）, the delay of the control system is larger than the Soft Real Time OS （Linux）. Further, the authors observed the same control system under Hard Real Time OS （RT-Linux）. At this point, the experiment showed that the real time error （jitter） is minimum in RT-Linux OS than the both of the previous OS. It is because the RT-Linux OS kernel can set the priority level and the control system was given the highest priority. The same experiment was observed under multitasking environment and the comparison of delay was similar to the preceding evaluation.展开更多
Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon...Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.展开更多
Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full...Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full information of the road surface including road lanes,road markings and their correspondences.Based on the prior knowledge of pavement information,we explore and use the deep progressive relationship between lane segmentation and pavement mark-ing detection.Then,different attention mechanisms are adapted for different tasks.A lane detection accuracy of 0.807 F1-score and a ground marking accuracy of 0.971 mean average precision at intersection over union(IOU)threshold 0.5 were achieved on the newly labeled see more on road plus(CeyMo+)dataset.Of course,we also validated it on two well-known datasets Berkeley Deep-Drive 100K(BDD100K)and CULane.In addition,a post-processing method for generating bird’s eye view lane(BEVLane)using lidar point cloud information is proposed,which is used for the construction of high-definition maps and subsequent decision-making planning.The code and data are available at https://github.com/mayberpf/RAIEnet.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
In the whole earth, people increased dramatically from generation to generation which had created a large scale of broken environment so that people are facing more various types of garbage. Most of garbages are not u...In the whole earth, people increased dramatically from generation to generation which had created a large scale of broken environment so that people are facing more various types of garbage. Most of garbages are not useful and as a matter of fact, they are used to be neglected. Furthermore, many efforts have been conducted to change it by many types of recycled methods. Here, a simple technique is proposed with and without using fires to transform the useless natural or man-made rubbish things to be a superfiber as well as thin film with multitasking applications in human daily life. Since most of earth environment is covered by oceans, here the authors show how the ocean related garbage such as the crab skins, broken coral reefs and beach stones were changed to be superfiber and a multitasking device prototype.展开更多
Background: Self-monitoring is important for recognizing the situations one is facing and assessing one’s own competence to respond appropriately to situations that require multitasking. Purpose: This study aimed to ...Background: Self-monitoring is important for recognizing the situations one is facing and assessing one’s own competence to respond appropriately to situations that require multitasking. Purpose: This study aimed to examine the surface and content validity of the Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking and refine the scale items accordingly. It is expected that the development of such scale will allow for reflection on advanced beginner nurses’ response to multitasking, leading to further capacity building. Methods: The surface validity of 96 items of the Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking was examined at a meeting with five expert researchers. Five researchers and five nurses examined the items’ content using an item-level content validity index through a questionnaire survey. Results and Conclusion: The Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking was organized into 73 items that were refined into scales with surface and content validity. Consequently, five sub-concepts were identified: recognizing the situation one’s facing, seeing one’s self from multiple perspectives, devising concrete strategies depending on the situation, considering a predictable time schedule, and being aware of the situation surrounding one’s self. In the future, it will be necessary to examine the reliability and validity of the scale.展开更多
Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer a...Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases.Motivated by this,a novel self-adjusting dualmode evolutionary framework,which integrates variable classification evolution and knowledge dynamic transfer strategies,is designed to compensate for this deficiency.First,a dual-mode evolutionary framework is designed to meet the needs of evolution in different states.Then,a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes.Second,a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes.Then,the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables.Third,an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge.Then,a dynamic weighting strategy is developed for efficient utilization of knowledge.Finally,by conducting experiments and comparing the designed method with several existing algorithms,the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.展开更多
Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown ...Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.展开更多
Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,whi...Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,which often struggle with the complexity and variability of indoor scenes.To address these limitations,we introduce a novel multi-robot collaborative framework based on multiplex interactive learning(MPIL)in which each robot specialises in a distinct visual task within a unified multitask architecture.During training,the framework employs task-specific decoders and cross-task feature sharing to enhance collaborative optimisation.At inference time,robots operate independently with optimised models,enabling scalable,asynchronous and efficient deployment in real-world scenarios.Specifically,MPIL employs specially designed modules that integrate RGB and depth data,refine feature representations and facilitate the simultaneous execution of multiple tasks,such as instance segmentation,scene classification and semantic segmentation.By leveraging these modules,distinct agents within multi-robot systems can effectively handle specialised tasks,thereby enhancing the overall system's flexibility and adaptability.This collaborative effort maximises the strengths of each robot,resulting in a more comprehensive understanding of environments.Extensive experiments on two public benchmark datasets demonstrate MPIL's competitive performance compared to state-of-the-art approaches,highlighting the effectiveness and robustness of our multi-robot system in complex indoor environments.展开更多
The advent of the internet-of-everything era has led to the increased use of mobile edge computing.The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of use...The advent of the internet-of-everything era has led to the increased use of mobile edge computing.The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of users,but existing technologies rigidly assume that there is only one task to be offloaded in each time slot at the terminal.In practical scenarios,there are often numerous computing tasks to be executed at the terminal,leading to a cumulative delay for subsequent task offloading.Therefore,the efficient processing of multiple computing tasks on the terminal has become highly challenging.To address the lowlatency offloading requirements for multiple computational tasks on terminal devices,we propose a terminal multitask parallel offloading algorithm based on deep reinforcement learning.Specifically,we first establish a mobile edge computing system model consisting of a single edge server and multiple terminal users.We then model the task offloading decision problem as a Markov decision process,and solve this problem using the Dueling Deep-Q Network algorithm to obtain the optimal offloading strategy.Experimental results demonstrate that,under the same constraints,our proposed algorithm reduces the average system latency.展开更多
Traditional deep learning methods pursue complex and single network architectures without considering the petrophysical relationship between different elastic parameters.The mathematical and statistical significance o...Traditional deep learning methods pursue complex and single network architectures without considering the petrophysical relationship between different elastic parameters.The mathematical and statistical significance of the inversion results may lead to model overfitting,especially when there are a limited number of well logs in a working area.Multitask learning provides an eff ective approach to addressing this issue.Simultaneously,learning multiple related tasks can improve a model’s generalization ability to a certain extent,thereby enhancing the performance of related tasks with an equal amount of labeled data.In this study,we propose an end-to-end multitask deep learning model that integrates a fully convolutional network and bidirectional gated recurrent unit for intelligent prestack inversion of“seismic data to elastic parameters.”The use of a Bayesian homoscedastic uncertainty-based loss function enables adaptive learning of the weight coeffi cients for diff erent elastic parameter inversion tasks,thereby reducing uncertainty during the inversion process.The proposed method combines the local feature perception of convolutional neural networks with the long-term memory of bidirectional gated recurrent networks.It maintains the rock physics constraint relationships among diff erent elastic parameters during the inversion process,demonstrating a high level of prediction accuracy.Numerical simulations and processing results of real seismic data validate the eff ectiveness and practicality of the proposed method.展开更多
Aim To achieve multitask data procssing in a wireless alarm system by computer. Methods The alarm system was composed of hardware and software. The hardware was composed of a master master computer and slave transmi...Aim To achieve multitask data procssing in a wireless alarm system by computer. Methods The alarm system was composed of hardware and software. The hardware was composed of a master master computer and slave transmitters. On urgent ugent occasion, one or more of the transmitters transmitted alarm signals and the master computer received the signals; interruption, residence, graph and word processing were utilized in software to achieve multitiask data processing . Results The main computer can conduct precise and quick multitask data procesing in any condition so long as alarm signals are received. The processing speed is higher than ordinary alarm System. Conclusion The master computer can conduct safe and quick multitask data processing by way of reliable design of software and hardware , so there is no need of special processor.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
基金supported in part by the National Natural Science Foundation of China(51775385)the Natural Science Foundation of Shanghai(23ZR1466000)+2 种基金the Shanghai Industrial Collaborative Science and Technology Innovation Project(2021-cyxt2-kj10)the Innovation Program of Shanghai Municipal Education Commission(202101070007E00098)Tongxiang Institute of Artificial General Intelligence(TAGI2-A-2024-0006).
文摘To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utilizing common knowledge.Knowledge transfer is the key to the effectiveness of EMT.Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance.There is plentiful knowledge in assistant tasks,and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks.EMT is thus confronted with a challenge to find appropriate knowledge.This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks.During the evolution process,better-performing candidates are supposed to have advantages in exploitation.Therefore,assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge.Specifically,the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population.Considering that target and assistant sub-populations have different characteristics,we use domain adaptation to reduce their distribution discrepancies.In this way,the trained classifier can classify assistant individuals more accurately,and truly useful knowledge can be selected for target tasks.The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.
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.
基金the National Natural Science Foundation of China under Grant No.51879023.
文摘Searching for the optimal cabin layout plan is an efective way to improve the efciency of the overall design and reduce a ship’s operation costs.The multitasking states of a ship involve several statuses when facing diferent missions during a voyage,such as the status of the marine supply and emergency escape.The human fow and logistics between cabins will change as the state changes.An ideal cabin layout plan,which is directly impacted by the above-mentioned factors,can meet the diferent requirements of several statuses to a higher degree.Inevitable deviations exist in the quantifcation of human fow and logistics.Moreover,uncontrollability is present in the fow situation during actual operations.The coupling of these deviations and uncontrollability shows typical uncertainties,which must be considered in the design process.Thus,it is important to integrate the demands of the human fow and logistics in multiple states into an uncertainty parameter scheme.This research considers the uncertainties of adjacent and circulating strengths obtained after quantifying the human fow and logistics.Interval numbers are used to integrate them,a two-layer nested system of interval optimization is introduced,and diferent optimization algorithms are substituted for solving calculations.The comparison and analysis of the calculation results with deterministic optimization show that the conclusions obtained can provide feasible guidance for cabin layout scheme.
基金supported in part by the National Key Research and Development Program of China(2022YFD2001200)the National Natural Science Foundation of China(62176238,61976237,62206251,62106230)+3 种基金China Postdoctoral Science Foundation(2021T140616,2021M692920)the Natural Science Foundation of Henan Province(222300420088)the Program for Science&Technology Innovation Talents in Universities of Henan Province(23HASTIT023)the Program for Science&Technology Innovation Teams in Universities of Henan Province(23IRTSTHN010).
文摘Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.
文摘Developed a new program structure using in single chip computer system, which based on multitasking mechanism. Discussed the specific method for realization of the new structure. The applied sample is also provided.
文摘This work is to observe the performance of PC based robot manipulator under general purpose （Windows）, Soft （Linux） and Hard （RT Linux） Real Time Operating Systems （OS）. The same open loop control system is observed in different operating systems with and without multitasking environment. The Data Acquisition （DAQ, PLC-812PG） card is used as a hardware interface. From the experiment, it could be seen that in the non real time operating system （Windows）, the delay of the control system is larger than the Soft Real Time OS （Linux）. Further, the authors observed the same control system under Hard Real Time OS （RT-Linux）. At this point, the experiment showed that the real time error （jitter） is minimum in RT-Linux OS than the both of the previous OS. It is because the RT-Linux OS kernel can set the priority level and the control system was given the highest priority. The same experiment was observed under multitasking environment and the comparison of delay was similar to the preceding evaluation.
基金supported by the National Natural Science Foundation of China(62073330)。
文摘Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.
基金supported by the Key R&D Program of Shandong Province,China(No.2020CXGC010118)Advanced Technology Research Institute,Beijing Institute of Technology(BITAI).
文摘Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full information of the road surface including road lanes,road markings and their correspondences.Based on the prior knowledge of pavement information,we explore and use the deep progressive relationship between lane segmentation and pavement mark-ing detection.Then,different attention mechanisms are adapted for different tasks.A lane detection accuracy of 0.807 F1-score and a ground marking accuracy of 0.971 mean average precision at intersection over union(IOU)threshold 0.5 were achieved on the newly labeled see more on road plus(CeyMo+)dataset.Of course,we also validated it on two well-known datasets Berkeley Deep-Drive 100K(BDD100K)and CULane.In addition,a post-processing method for generating bird’s eye view lane(BEVLane)using lidar point cloud information is proposed,which is used for the construction of high-definition maps and subsequent decision-making planning.The code and data are available at https://github.com/mayberpf/RAIEnet.
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.
文摘In the whole earth, people increased dramatically from generation to generation which had created a large scale of broken environment so that people are facing more various types of garbage. Most of garbages are not useful and as a matter of fact, they are used to be neglected. Furthermore, many efforts have been conducted to change it by many types of recycled methods. Here, a simple technique is proposed with and without using fires to transform the useless natural or man-made rubbish things to be a superfiber as well as thin film with multitasking applications in human daily life. Since most of earth environment is covered by oceans, here the authors show how the ocean related garbage such as the crab skins, broken coral reefs and beach stones were changed to be superfiber and a multitasking device prototype.
文摘Background: Self-monitoring is important for recognizing the situations one is facing and assessing one’s own competence to respond appropriately to situations that require multitasking. Purpose: This study aimed to examine the surface and content validity of the Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking and refine the scale items accordingly. It is expected that the development of such scale will allow for reflection on advanced beginner nurses’ response to multitasking, leading to further capacity building. Methods: The surface validity of 96 items of the Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking was examined at a meeting with five expert researchers. Five researchers and five nurses examined the items’ content using an item-level content validity index through a questionnaire survey. Results and Conclusion: The Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking was organized into 73 items that were refined into scales with surface and content validity. Consequently, five sub-concepts were identified: recognizing the situation one’s facing, seeing one’s self from multiple perspectives, devising concrete strategies depending on the situation, considering a predictable time schedule, and being aware of the situation surrounding one’s self. In the future, it will be necessary to examine the reliability and validity of the scale.
基金supported in part by the Plan of Key Scientific Research Projects of Colleges and Universities in Henan Province(25A413005,24A120005)the National Science and Technology Major Project(2021ZD0112302)the National Natural Science Foundation of China(62222301,61890930-5,62021003).
文摘Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases.Motivated by this,a novel self-adjusting dualmode evolutionary framework,which integrates variable classification evolution and knowledge dynamic transfer strategies,is designed to compensate for this deficiency.First,a dual-mode evolutionary framework is designed to meet the needs of evolution in different states.Then,a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes.Second,a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes.Then,the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables.Third,an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge.Then,a dynamic weighting strategy is developed for efficient utilization of knowledge.Finally,by conducting experiments and comparing the designed method with several existing algorithms,the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.
基金supported by National Natural Science Foundation of China(Grant No.62266028,62266027,U21B2027,and U24A20334)Major Science and Technology Programs in Yunnan Province(Grant No.202302AD080003,202402AG050007,and 202303AP140008)+1 种基金Yunnan Province Basic Research Program(Grant No.202301AS070047,202301AT070471,and 202401BC070021)Kunming University of Science and Technology's"Double First-rate"construction joint project(Grant No.202201BE070001-021).
文摘Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.
基金supported by the National Natural Science Foundation of China under Grant 62373009.
文摘Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,which often struggle with the complexity and variability of indoor scenes.To address these limitations,we introduce a novel multi-robot collaborative framework based on multiplex interactive learning(MPIL)in which each robot specialises in a distinct visual task within a unified multitask architecture.During training,the framework employs task-specific decoders and cross-task feature sharing to enhance collaborative optimisation.At inference time,robots operate independently with optimised models,enabling scalable,asynchronous and efficient deployment in real-world scenarios.Specifically,MPIL employs specially designed modules that integrate RGB and depth data,refine feature representations and facilitate the simultaneous execution of multiple tasks,such as instance segmentation,scene classification and semantic segmentation.By leveraging these modules,distinct agents within multi-robot systems can effectively handle specialised tasks,thereby enhancing the overall system's flexibility and adaptability.This collaborative effort maximises the strengths of each robot,resulting in a more comprehensive understanding of environments.Extensive experiments on two public benchmark datasets demonstrate MPIL's competitive performance compared to state-of-the-art approaches,highlighting the effectiveness and robustness of our multi-robot system in complex indoor environments.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+2 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067).
文摘The advent of the internet-of-everything era has led to the increased use of mobile edge computing.The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of users,but existing technologies rigidly assume that there is only one task to be offloaded in each time slot at the terminal.In practical scenarios,there are often numerous computing tasks to be executed at the terminal,leading to a cumulative delay for subsequent task offloading.Therefore,the efficient processing of multiple computing tasks on the terminal has become highly challenging.To address the lowlatency offloading requirements for multiple computational tasks on terminal devices,we propose a terminal multitask parallel offloading algorithm based on deep reinforcement learning.Specifically,we first establish a mobile edge computing system model consisting of a single edge server and multiple terminal users.We then model the task offloading decision problem as a Markov decision process,and solve this problem using the Dueling Deep-Q Network algorithm to obtain the optimal offloading strategy.Experimental results demonstrate that,under the same constraints,our proposed algorithm reduces the average system latency.
基金supported by National Key R&D Program of China(2018YFA0702501)National Natural Science Foundation of China (41974140)+1 种基金Science and Technology Management Department,China National Petroleum Corporation(2022DQ0604-01)China National Petroleum Corporation-China University of Petroleum (Beijing) Strategy。
文摘Traditional deep learning methods pursue complex and single network architectures without considering the petrophysical relationship between different elastic parameters.The mathematical and statistical significance of the inversion results may lead to model overfitting,especially when there are a limited number of well logs in a working area.Multitask learning provides an eff ective approach to addressing this issue.Simultaneously,learning multiple related tasks can improve a model’s generalization ability to a certain extent,thereby enhancing the performance of related tasks with an equal amount of labeled data.In this study,we propose an end-to-end multitask deep learning model that integrates a fully convolutional network and bidirectional gated recurrent unit for intelligent prestack inversion of“seismic data to elastic parameters.”The use of a Bayesian homoscedastic uncertainty-based loss function enables adaptive learning of the weight coeffi cients for diff erent elastic parameter inversion tasks,thereby reducing uncertainty during the inversion process.The proposed method combines the local feature perception of convolutional neural networks with the long-term memory of bidirectional gated recurrent networks.It maintains the rock physics constraint relationships among diff erent elastic parameters during the inversion process,demonstrating a high level of prediction accuracy.Numerical simulations and processing results of real seismic data validate the eff ectiveness and practicality of the proposed method.
文摘Aim To achieve multitask data procssing in a wireless alarm system by computer. Methods The alarm system was composed of hardware and software. The hardware was composed of a master master computer and slave transmitters. On urgent ugent occasion, one or more of the transmitters transmitted alarm signals and the master computer received the signals; interruption, residence, graph and word processing were utilized in software to achieve multitiask data processing . Results The main computer can conduct precise and quick multitask data procesing in any condition so long as alarm signals are received. The processing speed is higher than ordinary alarm System. Conclusion The master computer can conduct safe and quick multitask data processing by way of reliable design of software and hardware , so there is no need of special processor.
