To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was establis...To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.展开更多
Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,...Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,which may not capture all the features of the data.This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance measure.Therefore,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers.Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality.The paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.展开更多
Visual localization and object detection both play important roles in various tasks.In many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely together.Howeve...Visual localization and object detection both play important roles in various tasks.In many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely together.However,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such environments.In this paper,we explore multi-task network design and joint refinement of detection and localization.To address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic process.The dataset provides localization and detection information,and is publicly available at https://drive.google.com/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection tasks.Targeting this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding boxes.For dynamic environments,the detection branch also promotes the perception of dynamics.JLDNet includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud regression.Moreover,object-level bundle adjustment is used to further improve localization and detection accuracy.To test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn datasets.Our results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.展开更多
Because of the specific of underwater acoustic channel,spectrum sensing entails many difficulties in cognitive underwater acoustic communication( CUAC) networks, such as severe frequency-dependent attenuation and low ...Because of the specific of underwater acoustic channel,spectrum sensing entails many difficulties in cognitive underwater acoustic communication( CUAC) networks, such as severe frequency-dependent attenuation and low signal-to-noise ratios. To overcome these problems, two cooperative compressive spectrum sensing( CCSS) schemes are proposed for different scenarios( with and without channel state information). To strengthen collaboration among secondary users( SUs),cognitive central node( CCN) is provided to collect data from SUs. Thus,the proposed schemes can obtain spatial diversity gains and exploit joint sparse structure to improve the performance of spectrum sensing. Since the channel occupancy is sparse,we formulate the spectrum sensing problems into sparse vector recovery problems,and then present two CCSS algorithms based on path-wise coordinate optimization( PCO) and multi-task Bayesian compressive sensing( MT-BCS),respectively.Simulation results corroborate the effectiveness of the proposed methods in detecting the spectrum holes in underwater acoustic environment.展开更多
基金Project(2012B091100444)supported by the Production,Education and Research Cooperative Program of Guangdong Province and Ministry of Education,ChinaProject(2013ZM0091)supported by Fundamental Research Funds for the Central Universities of China
文摘To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.
基金supported by the Open Project of Xiangjiang Laboratory(No.22XJ02003)the National Natural Science Foundation of China(No.62122093).
文摘Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,which may not capture all the features of the data.This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance measure.Therefore,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers.Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality.The paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.
基金supported by the National Natural Science Foundation of China(No.62072020)Key-Area Research and the Leading Talents in Innovation and Entrepreneurship of Qingdao(No.19-3-2-21-zhc).
文摘Visual localization and object detection both play important roles in various tasks.In many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely together.However,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such environments.In this paper,we explore multi-task network design and joint refinement of detection and localization.To address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic process.The dataset provides localization and detection information,and is publicly available at https://drive.google.com/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection tasks.Targeting this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding boxes.For dynamic environments,the detection branch also promotes the perception of dynamics.JLDNet includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud regression.Moreover,object-level bundle adjustment is used to further improve localization and detection accuracy.To test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn datasets.Our results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.
基金National Natural Science Foundations of China(Nos.60872073,51075068,60975017,61301219)Doctoral Fund of Ministry of Education,China(No.20110092130004)
文摘Because of the specific of underwater acoustic channel,spectrum sensing entails many difficulties in cognitive underwater acoustic communication( CUAC) networks, such as severe frequency-dependent attenuation and low signal-to-noise ratios. To overcome these problems, two cooperative compressive spectrum sensing( CCSS) schemes are proposed for different scenarios( with and without channel state information). To strengthen collaboration among secondary users( SUs),cognitive central node( CCN) is provided to collect data from SUs. Thus,the proposed schemes can obtain spatial diversity gains and exploit joint sparse structure to improve the performance of spectrum sensing. Since the channel occupancy is sparse,we formulate the spectrum sensing problems into sparse vector recovery problems,and then present two CCSS algorithms based on path-wise coordinate optimization( PCO) and multi-task Bayesian compressive sensing( MT-BCS),respectively.Simulation results corroborate the effectiveness of the proposed methods in detecting the spectrum holes in underwater acoustic environment.
