Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge.In this work,a multi⁃hop D2D⁃enabled MEC scenario w...Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge.In this work,a multi⁃hop D2D⁃enabled MEC scenario was studied,where mobile devices at network edge connect and share resources with each other via multi⁃hop D2D.The research focus was on the micro⁃task scheduling problem in the multi⁃hop D2D⁃enabled MEC system,where each task was divided into multiple sequential micro⁃tasks,such as data downloading micro⁃task,data processing micro⁃task,and data uploading micro⁃task,according to their functionalities as well as resource requirements.A joint Task Failure Probability and Energy Consumption Minimization(TFP⁃ECM)problem was proposed,aiming at minimizing the task failure probability and the energy consumption jointly.To solve the problem,several linearization methods were proposed to relax the constraints and convert the original problem into an integer linear programming(ILP).Simulation results show that the proposed solution outperformed the existing solutions(with indivisible tasks or without resource sharing)in terms of both total cost and task failure probability.展开更多
Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always...Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.展开更多
Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation.This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and intro...Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation.This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and introducing CO_(2)nanobubble technology to improve the performance of cement-fly ash-based backfill materials(CFB).The properties including fluidity,setting time,uniaxial compressive strength,elastic modulus,porosity,microstructure and CO_(2)storage performance were systematically studied through methods such as fluidity evaluation,time test,uniaxial compression test,mercury intrusion porosimetry(MIP),scanning electron microscopy-energy dispersive spectroscopy analysis(SEM-EDS),and thermogravimetric-differential thermogravimetric analysis(TG-DTG).The experimental results show that the density and strength of the material are significantly improved under the synergistic effect of fractal dimension and CO_(2)nanobubbles.When the fractal dimension reaches 2.65,the mass ratio of coarse and fine aggregates reaches the optimal balance,and the structural density is greatly improved at the same time.At this time,the uniaxial compressive strength and elastic modulus reach their peak values,with increases of up to 13.46%and 27.47%,respectively.CO_(2)nanobubbles enhance the material properties by promoting hydration reaction and carbonization.At the microscopic level,CO_(2)nanobubble water promotes the formation of C-S-H(hydrated calcium silicate),C-A-S-H(hydrated calcium aluminium silicate)gel and CaCO_(3),which is the main way to enhance the performance.Thermogravimetric studies have shown that when the fractal dimension is 2.65,the dehydration of hydration products and the decarbonization process of CaCO_(3)are most obvious,and CO_(2)nanobubble water promotes the carbonization reaction,making it surpass the natural state.The CO_(2)sequestration quality of cement-fly ash-based materials treated with CO_(2)nanobubble water at different fractal dimensions increased by 12.4wt%to 99.8wt%.The results not only provide scientific insights for the design and implementation of low-carbon filling materials,but also provide a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.展开更多
An automated approach is proposed for a microassembly task, which is to insert a 10 μm diameter glass tube into a 12 μm diameter hole in a silicon substrate, and bond them together with ultraviolet （UV） curable ad...An automated approach is proposed for a microassembly task, which is to insert a 10 μm diameter glass tube into a 12 μm diameter hole in a silicon substrate, and bond them together with ultraviolet （UV） curable adhesive. Two three-degree-of-freedom micromanipulators axe used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. On-line monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
The mechanical behavior of cohesive soil is sensitized to drying-wetting cycles under confinements.However,the hydromechanical coupling effect has not been considered in current constitutive models.A macro-micro analy...The mechanical behavior of cohesive soil is sensitized to drying-wetting cycles under confinements.However,the hydromechanical coupling effect has not been considered in current constitutive models.A macro-micro analysis scheme is proposed in this paper to investigate the soil deformation behavior under the coupling of stress and drying-wetting cycles.A new device is developed based on CT(computerized tomography)workstation to apply certain normal and shear stresses on a soil specimen during drying-wetting cycles.A series of tests are conducted on a type of loess with various coupling of stress paths and drying-wetting cycles.At macroscopic level,stress sensor and laser sensor are used to acquire stress and strain,respectively.The shear and volumetric strain increase during the first few drying-wetting cycles and then become stable.The increase of the shear stress level or confining pressure would cause higher increase rate and the value of shear strain in the process of drying-wetting cycles.At microscopic level,the grayscale value(GSV)of CT scanning image is characterized as the proportion of soil particles to voids.A fabric state parameter is proposed to characterize soil microstructures under the influence of stress and drying-wetting cycle.Test results indicate that the macroand micro-responses show high consistence and relevance.The stress and drying-wetting cycles would both induce collapse of the soil microstructure,which dominants degradation of the soil mechanical properties.The evolution of the macro-mechanical property of soil exhibits a positive linear relationship with the micro-evolution of the fabric state parameter.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
基金National Natural Science Foundation of China(Grant Nos.61831008,61525103 and 61972113)the Guangdong Science and Tech⁃nology Planning Project(Grant No.2018B030322004)the Basic Research Project of Shenzhen Science and Technology Program(Grant No.JCYJ20190806112215116).
文摘Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge.In this work,a multi⁃hop D2D⁃enabled MEC scenario was studied,where mobile devices at network edge connect and share resources with each other via multi⁃hop D2D.The research focus was on the micro⁃task scheduling problem in the multi⁃hop D2D⁃enabled MEC system,where each task was divided into multiple sequential micro⁃tasks,such as data downloading micro⁃task,data processing micro⁃task,and data uploading micro⁃task,according to their functionalities as well as resource requirements.A joint Task Failure Probability and Energy Consumption Minimization(TFP⁃ECM)problem was proposed,aiming at minimizing the task failure probability and the energy consumption jointly.To solve the problem,several linearization methods were proposed to relax the constraints and convert the original problem into an integer linear programming(ILP).Simulation results show that the proposed solution outperformed the existing solutions(with indivisible tasks or without resource sharing)in terms of both total cost and task failure probability.
基金supported by Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2022QNRC001)the National Natural Science Foundation of China(No.52273053)the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.21CGA41)。
文摘Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.
基金financially supported by the China Scholarship Council(CSC)。
文摘Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation.This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and introducing CO_(2)nanobubble technology to improve the performance of cement-fly ash-based backfill materials(CFB).The properties including fluidity,setting time,uniaxial compressive strength,elastic modulus,porosity,microstructure and CO_(2)storage performance were systematically studied through methods such as fluidity evaluation,time test,uniaxial compression test,mercury intrusion porosimetry(MIP),scanning electron microscopy-energy dispersive spectroscopy analysis(SEM-EDS),and thermogravimetric-differential thermogravimetric analysis(TG-DTG).The experimental results show that the density and strength of the material are significantly improved under the synergistic effect of fractal dimension and CO_(2)nanobubbles.When the fractal dimension reaches 2.65,the mass ratio of coarse and fine aggregates reaches the optimal balance,and the structural density is greatly improved at the same time.At this time,the uniaxial compressive strength and elastic modulus reach their peak values,with increases of up to 13.46%and 27.47%,respectively.CO_(2)nanobubbles enhance the material properties by promoting hydration reaction and carbonization.At the microscopic level,CO_(2)nanobubble water promotes the formation of C-S-H(hydrated calcium silicate),C-A-S-H(hydrated calcium aluminium silicate)gel and CaCO_(3),which is the main way to enhance the performance.Thermogravimetric studies have shown that when the fractal dimension is 2.65,the dehydration of hydration products and the decarbonization process of CaCO_(3)are most obvious,and CO_(2)nanobubble water promotes the carbonization reaction,making it surpass the natural state.The CO_(2)sequestration quality of cement-fly ash-based materials treated with CO_(2)nanobubble water at different fractal dimensions increased by 12.4wt%to 99.8wt%.The results not only provide scientific insights for the design and implementation of low-carbon filling materials,but also provide a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.
基金supported by National Natural Science Foundation of China under(Nos.61227804 and 61105036)
文摘An automated approach is proposed for a microassembly task, which is to insert a 10 μm diameter glass tube into a 12 μm diameter hole in a silicon substrate, and bond them together with ultraviolet （UV） curable adhesive. Two three-degree-of-freedom micromanipulators axe used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. On-line monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
基金funded by National Key R&D Program of China(Grant No.2023YFC3007001)Beijing Natural Science Foundation(Grant No.8244053)China Postdoctoral Science Foundation(Grant No.2024M754065).
文摘The mechanical behavior of cohesive soil is sensitized to drying-wetting cycles under confinements.However,the hydromechanical coupling effect has not been considered in current constitutive models.A macro-micro analysis scheme is proposed in this paper to investigate the soil deformation behavior under the coupling of stress and drying-wetting cycles.A new device is developed based on CT(computerized tomography)workstation to apply certain normal and shear stresses on a soil specimen during drying-wetting cycles.A series of tests are conducted on a type of loess with various coupling of stress paths and drying-wetting cycles.At macroscopic level,stress sensor and laser sensor are used to acquire stress and strain,respectively.The shear and volumetric strain increase during the first few drying-wetting cycles and then become stable.The increase of the shear stress level or confining pressure would cause higher increase rate and the value of shear strain in the process of drying-wetting cycles.At microscopic level,the grayscale value(GSV)of CT scanning image is characterized as the proportion of soil particles to voids.A fabric state parameter is proposed to characterize soil microstructures under the influence of stress and drying-wetting cycle.Test results indicate that the macroand micro-responses show high consistence and relevance.The stress and drying-wetting cycles would both induce collapse of the soil microstructure,which dominants degradation of the soil mechanical properties.The evolution of the macro-mechanical property of soil exhibits a positive linear relationship with the micro-evolution of the fabric state parameter.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
