Some properties of a class of quasi-differentiable functions（the difference of two finite convex functions） are considered in this paper. And the convergence of the steepest descent algorithm for unconstrained and c...Some properties of a class of quasi-differentiable functions（the difference of two finite convex functions） are considered in this paper. And the convergence of the steepest descent algorithm for unconstrained and constrained quasi-differentiable programming is proved.展开更多
This paper derives first order necessary and sufficient conditions for unconstrained coned.c. Programming problems where the underlined space is partially ordered with respect to acone. These conditions are given in t...This paper derives first order necessary and sufficient conditions for unconstrained coned.c. Programming problems where the underlined space is partially ordered with respect to acone. These conditions are given in terms of directional derivatives and subdifferentials of thecomponent functions. Moreover, conjugate duality for cone d.c. Optimization is discussed andweak duality theorem is proved in a more general partially ordered linear topological vectorspace (generalizing the results in [11]).展开更多
Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Me...Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.展开更多
The SafeAmpCase is an innovative 3D-printed solution developed to address critical challenges in transporting and storing fragile glass drug ampoules during emergencies.This study employs a multidisciplinary approach...The SafeAmpCase is an innovative 3D-printed solution developed to address critical challenges in transporting and storing fragile glass drug ampoules during emergencies.This study employs a multidisciplinary approach—integrating biomedical engineering,advanced materials science,and emergency medicine expertise—to develop a compact,durable,and user-friendly ampoule case.A key innovation lies in the strategic selection of thermoplastic polyurethane(TPU)as the material,leveraging its superior impact resistance,flexibility,and noise-damping characteristics to ensure reliability under performance in demanding real-world conditions.To optimize the 3D printing process,key parameters,including printing temperature(220-250℃),volumetric flow rate(3-20 mm^(3)/s),retraction speed(30-90 mm/s),and retraction length(0.4-1.2 mm),were systematically adjusted using calibration models.The final optimized parameters(245℃,7 mm^(3)/s,90 mm/s,and 1.2 mm)reduced production time by 43%while preserving structural integrity.American Society for Testing and Materials(ASTM)international standard drop tests confirmed the case’s exceptional impact resistance,demonstrating a 90%reduction in ampoule breakage compared to polylactic acid plus.Further refinements,guided by feedback from 25 emergency professionals,resulted in medicationspecific color coding and an enhanced locking mechanism for usability in high-pressure situations.The final SafeAmpCase model withstood 18 consecutive drop trials without ampoule breakage,confirming its robustness in field conditions.This research underscores the transformative potential of additive manufacturing in developing customized,high-performance solutions for critical healthcare applications,setting a new benchmark for biomedical device design and rapid prototyping.展开更多
本文是D.C.隶属函数模糊集及其应用系列研究的第二部分。指出在实际问题中普遍选用的三角形、半三角形、梯形、半梯形、高斯型、柯西型、S形、Z形、π形隶属函数模糊集等均为D.C.隶属函数模糊集,建立了D.C.隶属函数模糊集对模糊集的万...本文是D.C.隶属函数模糊集及其应用系列研究的第二部分。指出在实际问题中普遍选用的三角形、半三角形、梯形、半梯形、高斯型、柯西型、S形、Z形、π形隶属函数模糊集等均为D.C.隶属函数模糊集,建立了D.C.隶属函数模糊集对模糊集的万有逼近性。探讨了D.C.隶属函数模糊集与模糊数之间的关系,给出了用D.C.隶属函数模糊集逼近模糊数的-εC e llina逼近形式,得到模糊数与D.C.函数之间的一个对应算子,指出了用模糊数表示D.C.函数的问题。展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
This paper presents a new non-linear formulation of the classical Vortex Lattice Method （VLM） approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity...This paper presents a new non-linear formulation of the classical Vortex Lattice Method （VLM） approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.展开更多
Topology optimization is an effective method to obtain a lightweight structure that meets the requirements of structural strength.Whether the optimization results meet the actual needs mainly depends on the accuracy o...Topology optimization is an effective method to obtain a lightweight structure that meets the requirements of structural strength.Whether the optimization results meet the actual needs mainly depends on the accuracy of the material properties and the boundary conditions,especially for a tiny Flapping-wing Micro Aerial Vehicle(FMAV)transmission system manufactured by 3D printing.In this paper,experimental and numerical computation efforts were undertaken to gain a reliable topology optimization method for the bottom of the transmission system.First,the constitutive behavior of the ultraviolet(UV)curable resin used in fabrication was evaluated.Second,a numerical computation model describing further verified via experiments.Topology optimization modeling considering nonlinear factors,e.g.contact,friction and collision,was presented,and the optimization results were verified by both dynamic simulation and experiments.Finally,detailed discussions on different load cases and constraints were presented to clarify their effect on the optimization.Our methods and results presented in this paper may shed light on the lightweight design of a FMAV.展开更多
This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities...This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.展开更多
Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design r...Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.展开更多
This paper presents an optimization methodology for the geometric configuration of a room–and–pillar mining project,considering safety and operational restrictions while maximizing ore recovery.An underground mangan...This paper presents an optimization methodology for the geometric configuration of a room–and–pillar mining project,considering safety and operational restrictions while maximizing ore recovery.An underground manganese mine was chosen as a case study to investigate the capabilities of the presented methodology.A software package(OPTIMINE)was implemented to address the computational demand in an automated manner.Three–dimensional finite difference analyses were performed in FLAC3D and used as implicit functions to consider safety in terms of the factor of safety and room convergence.The obtained results showed that recovery could be increased from 44%to more than 80%in a safe manner.展开更多
基金Supported by the State Foundations of Ph.D.Units(20020141013)Supported by the NSF of China(10001007)
文摘Some properties of a class of quasi-differentiable functions（the difference of two finite convex functions） are considered in this paper. And the convergence of the steepest descent algorithm for unconstrained and constrained quasi-differentiable programming is proved.
文摘This paper derives first order necessary and sufficient conditions for unconstrained coned.c. Programming problems where the underlined space is partially ordered with respect to acone. These conditions are given in terms of directional derivatives and subdifferentials of thecomponent functions. Moreover, conjugate duality for cone d.c. Optimization is discussed andweak duality theorem is proved in a more general partially ordered linear topological vectorspace (generalizing the results in [11]).
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4604800,2021YFA1202300)the Natural and Science Foundation of China(Grant Nos.52201041,52275331,52205358)+1 种基金the Key Research and Development Program of Hubei Province(Nos.2024BCB091,2022CFA031)the Hong Kong Scholars Program(No.XJ2022014)。
文摘Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.
基金Open access funding provided by Ben-Gurion University.
文摘The SafeAmpCase is an innovative 3D-printed solution developed to address critical challenges in transporting and storing fragile glass drug ampoules during emergencies.This study employs a multidisciplinary approach—integrating biomedical engineering,advanced materials science,and emergency medicine expertise—to develop a compact,durable,and user-friendly ampoule case.A key innovation lies in the strategic selection of thermoplastic polyurethane(TPU)as the material,leveraging its superior impact resistance,flexibility,and noise-damping characteristics to ensure reliability under performance in demanding real-world conditions.To optimize the 3D printing process,key parameters,including printing temperature(220-250℃),volumetric flow rate(3-20 mm^(3)/s),retraction speed(30-90 mm/s),and retraction length(0.4-1.2 mm),were systematically adjusted using calibration models.The final optimized parameters(245℃,7 mm^(3)/s,90 mm/s,and 1.2 mm)reduced production time by 43%while preserving structural integrity.American Society for Testing and Materials(ASTM)international standard drop tests confirmed the case’s exceptional impact resistance,demonstrating a 90%reduction in ampoule breakage compared to polylactic acid plus.Further refinements,guided by feedback from 25 emergency professionals,resulted in medicationspecific color coding and an enhanced locking mechanism for usability in high-pressure situations.The final SafeAmpCase model withstood 18 consecutive drop trials without ampoule breakage,confirming its robustness in field conditions.This research underscores the transformative potential of additive manufacturing in developing customized,high-performance solutions for critical healthcare applications,setting a new benchmark for biomedical device design and rapid prototyping.
文摘本文是D.C.隶属函数模糊集及其应用系列研究的第二部分。指出在实际问题中普遍选用的三角形、半三角形、梯形、半梯形、高斯型、柯西型、S形、Z形、π形隶属函数模糊集等均为D.C.隶属函数模糊集,建立了D.C.隶属函数模糊集对模糊集的万有逼近性。探讨了D.C.隶属函数模糊集与模糊数之间的关系,给出了用D.C.隶属函数模糊集逼近模糊数的-εC e llina逼近形式,得到模糊数与D.C.函数之间的一个对应算子,指出了用模糊数表示D.C.函数的问题。
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 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)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金the Natural Sciences and Engineering Research Council of Canada (NSERC) for the funding of the Canada Research Chair in Aircraft Modeling and Simulation Technologiesthe Canada Foundation of Innovation (CFI), the Ministerèdu Développement économique, de l’Innovation et de l’Exportation (MDEIE) and Hydra Technologies for the acquisition of the UAS-S4 using the Leaders Opportunity Funds+2 种基金the financial support obtained in the framework of the CRIAQ MDO-505 projectthe implication of our industrial partners Bombardier Aerospace and Thales CanadaNSERC for their support
文摘This paper presents a new non-linear formulation of the classical Vortex Lattice Method （VLM） approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.
基金supported by the National Natural Science Foundation of China(No.11672022)。
文摘Topology optimization is an effective method to obtain a lightweight structure that meets the requirements of structural strength.Whether the optimization results meet the actual needs mainly depends on the accuracy of the material properties and the boundary conditions,especially for a tiny Flapping-wing Micro Aerial Vehicle(FMAV)transmission system manufactured by 3D printing.In this paper,experimental and numerical computation efforts were undertaken to gain a reliable topology optimization method for the bottom of the transmission system.First,the constitutive behavior of the ultraviolet(UV)curable resin used in fabrication was evaluated.Second,a numerical computation model describing further verified via experiments.Topology optimization modeling considering nonlinear factors,e.g.contact,friction and collision,was presented,and the optimization results were verified by both dynamic simulation and experiments.Finally,detailed discussions on different load cases and constraints were presented to clarify their effect on the optimization.Our methods and results presented in this paper may shed light on the lightweight design of a FMAV.
基金the supports from the National Natural Science Foundation of China (61571156)Basic Research Project of Shenzhen (JCYJ20170413110004682 and JCYJ20150403161923521)。
文摘This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.
基金financially sponsored by National Natural Science Foundation of China(No.50975121)Changchun Science and Technology Plan Projects(No.10KZ03)the Plan for Scientific and Technology Development of Jilin Province(No.20150520106JH)
文摘Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.
文摘This paper presents an optimization methodology for the geometric configuration of a room–and–pillar mining project,considering safety and operational restrictions while maximizing ore recovery.An underground manganese mine was chosen as a case study to investigate the capabilities of the presented methodology.A software package(OPTIMINE)was implemented to address the computational demand in an automated manner.Three–dimensional finite difference analyses were performed in FLAC3D and used as implicit functions to consider safety in terms of the factor of safety and room convergence.The obtained results showed that recovery could be increased from 44%to more than 80%in a safe manner.
