Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and pro...Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.展开更多
In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionall...In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.展开更多
The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on th...The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on the characteristics of composite field multiplication in symmetric cipher algorithms and the realization principle of its reconfigurable architectures, this paper describes the reconfigurable composite field multiplication over GF（（2^8）k） （k=1,2,3,4） in RISC （reduced instruction set computer） processor and VLIW （very long instruction word） processor architecture, respectively. Through configuration, the architectures can realize the composite field multiplication over GF（2^8）, GF （（2^8）2）, GF（（28）3） and GF（（28）4） flexibly and efficiently. We simulated the function of circuits and synthesized the reconfigurable design based on the 0.18 μm CMOS （complementary metal oxide semiconductor） standard cell library and the comparison with other same kind designs. The result shows that the reconfigurable design proposed in the paper can provide higher efficiency under the premise of flexibility.展开更多
Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and...Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and a novel scalable and unified architecture for Montgomery inverse hardware in finite fields GF(p) and GF(2n) is proposed. Furthermore, this architecture based on the new modular inversion algorithm has been verified by modeling it in Verilog-HDL, and accomplished it under 0.18 μm CMOS technology. The result indicates that our work has better performance and flexibility than other works.展开更多
Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 20...Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.展开更多
Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index ...Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.展开更多
To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method ...To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.展开更多
This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low ...This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.展开更多
Bipolar plate is one of the key components of Proton Exchange Membrane Fuel Cell(PEMFC)and a reasonable flow field design for bipolar plate will improve cell performance.Herein,we have reviewed conventional and bionic...Bipolar plate is one of the key components of Proton Exchange Membrane Fuel Cell(PEMFC)and a reasonable flow field design for bipolar plate will improve cell performance.Herein,we have reviewed conventional and bionic flow field designs in recent literature with a focus on bionic flow fields.In particular,the bionic flow fields are summarized into two types:plant-inspired and animal-inspired.The conventional methodology for flow field design takes more time to find the optimum since it is based on experience and trial-and-error methods.In recent years,machine learning has been used to optimize flow field structures of bipolar plates owing to the advantages of excellent prediction and optimization capability.Artificial Intelligence(AI)-assisted flow field design has been summarized into two categories in this review:single-objective optimization and multi-objective optimization.Furthermore,a Threats-Opportunities-Weaknesses-Strengths(TOWS)analysis has been conducted for AI-assisted flow field design.It has been envisioned that AI can afford a powerful tool to solve the complex problem of bionic flow field design and significantly enhance the performance of PEMFC with bionic flow fields.展开更多
Background Advancements in computer science and knowledge have made the incorporation of control theory,graphics processing,and mathematical models increasingly important for urban design planning.However,challenges r...Background Advancements in computer science and knowledge have made the incorporation of control theory,graphics processing,and mathematical models increasingly important for urban design planning.However,challenges remain in aligning virtual reality(VR)environments with real-world spatial and preparation requirements,particularly in indoor urban spaces.Methods This study investigates the application of VR technology to urban design,focusing on the growth and assessment of the redirection of the space-tree sorter algorithm(STSA).It outlines various assessment indicators,organization of the VR-based system architecture,and construction of 3D urban models and databases.This research also examined methods for the interactive adjustment of indoor space layout plans within a VR environment.Results This research study involved developing and demonstrating the creation and simulation of urban indoor spaces and cityscapes in VR and implementing an experimental setup to test layout modifications and system interactivity.The results indicated enhanced alignment between the virtual and physical spatial configurations.The analysis highlights the strengths and limitations of current VR systems for urban design and identifies key areas for optimization and refinement.Conclusion High congruence between virtual simulations and real-world urban spaces is necessary for effective VR-driven urban planning.This study contributes to a clearer understanding of how 3D modeling,interactive layout design,and reproduction technology can be efficiently employed to support urban increase initiatives.展开更多
This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D n...This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.展开更多
This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is propo...This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experiments on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.展开更多
Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorit...Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a directed random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches may be relatively inefficient. Differential evolution uses information from a distance and azimuth between individuals of a population to search the parameter space, the initial search is effective, but the search speed decreases quickly because differential information between the individuals of population vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to the matched field inversion with synthetic data. Optimal values of the parameters, the final values of object function and inversion time is presented and compared.展开更多
This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injecti...This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injection-molded parts.At its core,the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles,leading to improvements in both mechanical strength and material efficiency.The design optimization is validated through a series of rigorous experimental tests,including three-point bending and torsion tests performed on key-socket frames,ensuring that the optimized designs meet practical performance requirements.A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm(AETDPA),which refines the prediction of mechanical failure and strength fitting.This algorithm has been shown to deliver mesh-independent accuracy,thereby enhancing the reliability of simulation results across various design iterations.The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part,thus accelerating the overall design process while ensuring struc-tural integrity.In addition to its immediate applications in injection molding,the study explores the potential extension of this framework to metal additive manufacturing,opening new avenues for its use in advanced manufacturing technologies.Numerical simulations,including finite element analysis,support the experimental findings and confirm that the optimized designs provide a balanced combination of strength,durability,and efficiency.Furthermore,the integration challenges with existing injection molding practices are addressed,underscoring the framework’s scalability and industrial relevance.Overall,this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications,promising significant improvements in design efficiency,cost-effectiveness,and product performance.Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.展开更多
The karst geothermal reservoir in Xiong'an New Area is a representative example of an ancient buried hill geothermal system.However,published heat flow data are predominantly derived from the Cenozoic sedimentary ...The karst geothermal reservoir in Xiong'an New Area is a representative example of an ancient buried hill geothermal system.However,published heat flow data are predominantly derived from the Cenozoic sedimentary cap.Due to the limited depth of borehole exploration,heat flow measurements and analyses of the Archean crystalline base-ment in the study area are rare.Further investigation of the heat flow and temperature field characteristics within the Archean crystalline basement beneath the karst geothermal reservoir is necessary to understand the vertical distribution of heat flow and improve the geothermal genetic mechanism in the area.The D01 deep geothermal scientific drilling param-eter well was implemented in the Niutuozhen geothermal field of Xiong'an New Area.The well exposed the entire Gaoyuzhaung Formation karst geotheremal reservoir of the Jixian system and drilled 1,723.67 m into the Archean crys-talline basement,providing the necessary conditions for determining its heat flow.This study involved borehole tempera-ture measurements and thermophysical property testing of core samples from the D01 well to analyze the vertical distri-bution of heat flow.The findings revealed distinct segmentation in the geothermal gradient and rock thermophysical prop-erties.The geothermal reservoir of Gaoyuzhuang Formation is dominated by convection,with significant temperature inversions corresponding to karst fracture developments.In contrast,the Archean crystalline basement exhibits conduc-tive heat transfer.After 233 days of static equilibrium,the average geothermal gradients of the Gaoyuzhuang Formation and the Archean crystalline basement were determined to be 1.5°C/km and 18.3°C/km,respectively.These values adjusted to-0.8°C/km and 18.2°C/km after 551 days,with the longer static time curve approaching steady-state condi-tions.The average thermal conductivity of dolomite in Gaoyuzhuang Formation was measured as 4.37±0.82 W/(K·m),3 and that of Archean gneiss as 2.41±0.40 W/(K·m).The average radioactive heat generation rate were 0.30±0.32μW/m 3 for dolomite and 1.32±0.69μW/m for gneiss.Using the temperature curve after 551 days and thermal conductivity data,the Archean heat flow at the D01 well was calculated as(43.9±7.0)mW/m2,While the heat flow for the Neogene sedi-mentary cap was estimated at 88.6mW/m2.The heat flow of Neogene sedimentary caprock is significantly higher than 2 that of Archean crystalline basement at the D01 well,with an excess of 44.7 mW/m accounting for approximately 50%of the total heat flow in the Neogene sedimentary caprock.This is primarily attributed to lateral thermal convection within the high-porosity and high-permeability karst dolomite layer,and vertical thermal convection facilitated by the Niudong fault,which collectively contribute to the heat supply of the Neogene sedimentary caprock.Thermal convection in karst fissure and fault zone contribute approximately 50%of the heat flow in the Neogene sedimentary caprock.This study quantitatively revealed the vertical distribution of heat flow,providing empirical evidence for the genetic mechanism of the convection-conduction geothermal system in sedimentary basins.展开更多
We present the Fourier lightfield multiview stereoscope(FiLM-Scope).This imaging device combines concepts from Fourier lightfield microscopy and multiview stereo imaging to capture high-resolution 3D videos over large...We present the Fourier lightfield multiview stereoscope(FiLM-Scope).This imaging device combines concepts from Fourier lightfield microscopy and multiview stereo imaging to capture high-resolution 3D videos over large fields of view.The FiLM-Scope optical hardware consists of a multicamera array,with 48 individual microcameras,placed behind a high-throughput primary lens.This allows the FiLM-Scope to simultaneously capture 48 unique 12.8 megapixel images of a 28×37 mm field-of-view,from unique angular perspectives over a 21 deg×29 deg range,with down to 22μm lateral resolution.We also describe a self-supervised algorithm to reconstruct 3D height maps from these images.Our approach demonstrates height accuracy down to 11μm.To showcase the utility of our system,we perform tool tracking over the surface of an ex vivo rat skull and visualize the 3D deformation in stretching human skin,with videos captured at up to 100 frames per second.The FiLM-Scope has the potential to improve 3D visualization in a range of microsurgical settings.展开更多
Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial ...Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.展开更多
Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to dedu...Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to deduce the boundary forces and investigate controlling factors. It is suggested that the continent continent collision is the dominant factor controlling the Chinese tectonic stress field. The ocean continent convergence along the subduction zone is an important factor. There exists tensile boundary force along the marginal sea.展开更多
Self-consistent field theory（SCFT）, as a state-of-the-art technique for studying the self-assembly of block copolymers, is attracting continuous efforts to improve its accuracy and efficiency. Here we present a four...Self-consistent field theory（SCFT）, as a state-of-the-art technique for studying the self-assembly of block copolymers, is attracting continuous efforts to improve its accuracy and efficiency. Here we present a fourth-order exponential time differencing Runge-Kutta algorithm（ETDRK4） to solve the modified diffusion equation（MDE） which is the most time-consuming part of a SCFT calculation. By making a careful comparison with currently most efficient and popular algorithms, we demonstrate that the ETDRK4 algorithm significantly reduces the number of chain contour steps in solving the MDE, resulting in a boost of the overall computation efficiency, while it shares the same spatial accuracy with other algorithms. In addition, to demonstrate the power of our ETDRK4 algorithm, we apply it to compute the phase boundaries of the bicontinuous gyroid phase in the strong segregation regime and to verify the existence of the triple point of the O70 phase, the lamellar phase and the cylindrical phase.展开更多
The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock mater...The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock materials.In this study,we present a novel approach that introduces a 3D numerical manifold method(3D-NMM)with a geometric kernel to enhance computational efficiency.Specifically,the maximum tensile stress criterion is adopted as a crack growth criterion to achieve strong discontinuous crack growth,and a local crack tracking algorithm and an angle correction technique are incorporated to address minor limitations of the algorithm in a 3D model.The implementation of the program is carried out in Python,using object-oriented programming in two independent modules:a calculation module and a crack module.Furthermore,we propose feasible improvements to enhance the performance of the algorithm.Finally,we demonstrate the feasibility and effectiveness of the enhanced algorithm in the 3D-NMM using four numerical examples.This study establishes the potential of the 3DNMM,combined with the local tracking algorithm,for accurately modeling 3D crack propagation in brittle rock materials.展开更多
基金The National Natural Science Foundation of China(Grant No.72274052)the National Natural Science Foundation of China(Grant No.72174173).
文摘Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.
基金the support of EPIC - Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by Equinor Brazil and FAPESP - Sao Paulo Research Foundation (2021/04878- 7 and 2017/15736-3)financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior Brasil (CAPES) - Financing Code 001
文摘In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.
基金Supported by the National Natural Science Foundation of China(61202492,61309022,61309008)the Natural Science Foundation for Young of Shaanxi Province(2013JQ8013)
文摘The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on the characteristics of composite field multiplication in symmetric cipher algorithms and the realization principle of its reconfigurable architectures, this paper describes the reconfigurable composite field multiplication over GF（（2^8）k） （k=1,2,3,4） in RISC （reduced instruction set computer） processor and VLIW （very long instruction word） processor architecture, respectively. Through configuration, the architectures can realize the composite field multiplication over GF（2^8）, GF （（2^8）2）, GF（（28）3） and GF（（28）4） flexibly and efficiently. We simulated the function of circuits and synthesized the reconfigurable design based on the 0.18 μm CMOS （complementary metal oxide semiconductor） standard cell library and the comparison with other same kind designs. The result shows that the reconfigurable design proposed in the paper can provide higher efficiency under the premise of flexibility.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (No. 2008AA01Z103)
文摘Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and a novel scalable and unified architecture for Montgomery inverse hardware in finite fields GF(p) and GF(2n) is proposed. Furthermore, this architecture based on the new modular inversion algorithm has been verified by modeling it in Verilog-HDL, and accomplished it under 0.18 μm CMOS technology. The result indicates that our work has better performance and flexibility than other works.
基金funded by Deanship of Scientific Research,King Saud University,through the Vice Deanship of Scientific Research.
文摘Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.
基金Project(61273187)supported by the National Natural Science Foundation of ChinaProject(61321003)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.
基金Supported by the National High Technology Research and Development Programme of China（ No. 2006AA04Z245 ） and China Postdoctoral Science Foundation （ No. 200904500988 ）.
文摘To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.
基金Support by the Fundamental Research Funds for the Central Universities(2024300443)the National Natural Science Foundation of China(NSFC)Young Scientists Fund(62405131)。
文摘This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.
基金supported by the National Natural Science Foundation of China(52075214and 51975245)the National Key R&D Program of China(No.2022YFE0138500)+3 种基金Jilin Provincial Science&Technology Department(20220201115GX)Key Science and Technology R&D Projects of Jilin Province(2020C023-3)Program of Jilin University Science and Technology Innovative Research Team(2020TD-03)the Fundamental Research Funds for the Central Universities.
文摘Bipolar plate is one of the key components of Proton Exchange Membrane Fuel Cell(PEMFC)and a reasonable flow field design for bipolar plate will improve cell performance.Herein,we have reviewed conventional and bionic flow field designs in recent literature with a focus on bionic flow fields.In particular,the bionic flow fields are summarized into two types:plant-inspired and animal-inspired.The conventional methodology for flow field design takes more time to find the optimum since it is based on experience and trial-and-error methods.In recent years,machine learning has been used to optimize flow field structures of bipolar plates owing to the advantages of excellent prediction and optimization capability.Artificial Intelligence(AI)-assisted flow field design has been summarized into two categories in this review:single-objective optimization and multi-objective optimization.Furthermore,a Threats-Opportunities-Weaknesses-Strengths(TOWS)analysis has been conducted for AI-assisted flow field design.It has been envisioned that AI can afford a powerful tool to solve the complex problem of bionic flow field design and significantly enhance the performance of PEMFC with bionic flow fields.
文摘Background Advancements in computer science and knowledge have made the incorporation of control theory,graphics processing,and mathematical models increasingly important for urban design planning.However,challenges remain in aligning virtual reality(VR)environments with real-world spatial and preparation requirements,particularly in indoor urban spaces.Methods This study investigates the application of VR technology to urban design,focusing on the growth and assessment of the redirection of the space-tree sorter algorithm(STSA).It outlines various assessment indicators,organization of the VR-based system architecture,and construction of 3D urban models and databases.This research also examined methods for the interactive adjustment of indoor space layout plans within a VR environment.Results This research study involved developing and demonstrating the creation and simulation of urban indoor spaces and cityscapes in VR and implementing an experimental setup to test layout modifications and system interactivity.The results indicated enhanced alignment between the virtual and physical spatial configurations.The analysis highlights the strengths and limitations of current VR systems for urban design and identifies key areas for optimization and refinement.Conclusion High congruence between virtual simulations and real-world urban spaces is necessary for effective VR-driven urban planning.This study contributes to a clearer understanding of how 3D modeling,interactive layout design,and reproduction technology can be efficiently employed to support urban increase initiatives.
文摘This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.
文摘This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experiments on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.
文摘Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a directed random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches may be relatively inefficient. Differential evolution uses information from a distance and azimuth between individuals of a population to search the parameter space, the initial search is effective, but the search speed decreases quickly because differential information between the individuals of population vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to the matched field inversion with synthetic data. Optimal values of the parameters, the final values of object function and inversion time is presented and compared.
文摘This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injection-molded parts.At its core,the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles,leading to improvements in both mechanical strength and material efficiency.The design optimization is validated through a series of rigorous experimental tests,including three-point bending and torsion tests performed on key-socket frames,ensuring that the optimized designs meet practical performance requirements.A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm(AETDPA),which refines the prediction of mechanical failure and strength fitting.This algorithm has been shown to deliver mesh-independent accuracy,thereby enhancing the reliability of simulation results across various design iterations.The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part,thus accelerating the overall design process while ensuring struc-tural integrity.In addition to its immediate applications in injection molding,the study explores the potential extension of this framework to metal additive manufacturing,opening new avenues for its use in advanced manufacturing technologies.Numerical simulations,including finite element analysis,support the experimental findings and confirm that the optimized designs provide a balanced combination of strength,durability,and efficiency.Furthermore,the integration challenges with existing injection molding practices are addressed,underscoring the framework’s scalability and industrial relevance.Overall,this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications,promising significant improvements in design efficiency,cost-effectiveness,and product performance.Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.
基金funded by the National Key Research and Development Program(Grant Nos.2021YFB1507404 and 2018YFC0604305)the Project of China Geological Survey(Grant Nos.DD20221680,DD20189113,and DD20190127).
文摘The karst geothermal reservoir in Xiong'an New Area is a representative example of an ancient buried hill geothermal system.However,published heat flow data are predominantly derived from the Cenozoic sedimentary cap.Due to the limited depth of borehole exploration,heat flow measurements and analyses of the Archean crystalline base-ment in the study area are rare.Further investigation of the heat flow and temperature field characteristics within the Archean crystalline basement beneath the karst geothermal reservoir is necessary to understand the vertical distribution of heat flow and improve the geothermal genetic mechanism in the area.The D01 deep geothermal scientific drilling param-eter well was implemented in the Niutuozhen geothermal field of Xiong'an New Area.The well exposed the entire Gaoyuzhaung Formation karst geotheremal reservoir of the Jixian system and drilled 1,723.67 m into the Archean crys-talline basement,providing the necessary conditions for determining its heat flow.This study involved borehole tempera-ture measurements and thermophysical property testing of core samples from the D01 well to analyze the vertical distri-bution of heat flow.The findings revealed distinct segmentation in the geothermal gradient and rock thermophysical prop-erties.The geothermal reservoir of Gaoyuzhuang Formation is dominated by convection,with significant temperature inversions corresponding to karst fracture developments.In contrast,the Archean crystalline basement exhibits conduc-tive heat transfer.After 233 days of static equilibrium,the average geothermal gradients of the Gaoyuzhuang Formation and the Archean crystalline basement were determined to be 1.5°C/km and 18.3°C/km,respectively.These values adjusted to-0.8°C/km and 18.2°C/km after 551 days,with the longer static time curve approaching steady-state condi-tions.The average thermal conductivity of dolomite in Gaoyuzhuang Formation was measured as 4.37±0.82 W/(K·m),3 and that of Archean gneiss as 2.41±0.40 W/(K·m).The average radioactive heat generation rate were 0.30±0.32μW/m 3 for dolomite and 1.32±0.69μW/m for gneiss.Using the temperature curve after 551 days and thermal conductivity data,the Archean heat flow at the D01 well was calculated as(43.9±7.0)mW/m2,While the heat flow for the Neogene sedi-mentary cap was estimated at 88.6mW/m2.The heat flow of Neogene sedimentary caprock is significantly higher than 2 that of Archean crystalline basement at the D01 well,with an excess of 44.7 mW/m accounting for approximately 50%of the total heat flow in the Neogene sedimentary caprock.This is primarily attributed to lateral thermal convection within the high-porosity and high-permeability karst dolomite layer,and vertical thermal convection facilitated by the Niudong fault,which collectively contribute to the heat supply of the Neogene sedimentary caprock.Thermal convection in karst fissure and fault zone contribute approximately 50%of the heat flow in the Neogene sedimentary caprock.This study quantitatively revealed the vertical distribution of heat flow,providing empirical evidence for the genetic mechanism of the convection-conduction geothermal system in sedimentary basins.
基金supported by the National Cancer Institute(NCI)of the National Institutes of Health(Grant No.R44CA250877)the Office of Research Infrastructure Programs(ORIP),Office of the Director,National Institutes of Health,and the National Institute of Environmental Health Sciences(NIEHS)of the National Institutes of Health(Grant No.R44OD024879)+2 种基金the National Institute of Biomedical Imaging and Bioengineering(NIBIB)of the National Institutes of Health(Grant No.R43EB030979)the National Science Foundation(Grant Nos.2036439 and 2238845)the Duke Coulter Translational Part-nership Award,the Fitzpatrick Institute at the Duke University.
文摘We present the Fourier lightfield multiview stereoscope(FiLM-Scope).This imaging device combines concepts from Fourier lightfield microscopy and multiview stereo imaging to capture high-resolution 3D videos over large fields of view.The FiLM-Scope optical hardware consists of a multicamera array,with 48 individual microcameras,placed behind a high-throughput primary lens.This allows the FiLM-Scope to simultaneously capture 48 unique 12.8 megapixel images of a 28×37 mm field-of-view,from unique angular perspectives over a 21 deg×29 deg range,with down to 22μm lateral resolution.We also describe a self-supervised algorithm to reconstruct 3D height maps from these images.Our approach demonstrates height accuracy down to 11μm.To showcase the utility of our system,we perform tool tracking over the surface of an ex vivo rat skull and visualize the 3D deformation in stretching human skin,with videos captured at up to 100 frames per second.The FiLM-Scope has the potential to improve 3D visualization in a range of microsurgical settings.
基金Supported by the National Natural Science Foundation of China (Grant No. 52071097)Hainan Provincial Natural Science Foundation of China (Grant No. 522MS162)Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (Grant No. 2021JCJQ-SYSJJ-LB06910)。
文摘Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.
文摘Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to deduce the boundary forces and investigate controlling factors. It is suggested that the continent continent collision is the dominant factor controlling the Chinese tectonic stress field. The ocean continent convergence along the subduction zone is an important factor. There exists tensile boundary force along the marginal sea.
基金financially supported by the China Scholarship Council (No. 201406105018)the National Natural Science Foundation of China (No. 21004013)the National Basic Research Program of China (No. 2011CB605701)
文摘Self-consistent field theory（SCFT）, as a state-of-the-art technique for studying the self-assembly of block copolymers, is attracting continuous efforts to improve its accuracy and efficiency. Here we present a fourth-order exponential time differencing Runge-Kutta algorithm（ETDRK4） to solve the modified diffusion equation（MDE） which is the most time-consuming part of a SCFT calculation. By making a careful comparison with currently most efficient and popular algorithms, we demonstrate that the ETDRK4 algorithm significantly reduces the number of chain contour steps in solving the MDE, resulting in a boost of the overall computation efficiency, while it shares the same spatial accuracy with other algorithms. In addition, to demonstrate the power of our ETDRK4 algorithm, we apply it to compute the phase boundaries of the bicontinuous gyroid phase in the strong segregation regime and to verify the existence of the triple point of the O70 phase, the lamellar phase and the cylindrical phase.
基金supported by the National Natural Science Foundation of China(Grant Nos.42172312 and 52211540395)support from the Institut Universitaire de France(IUF).
文摘The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock materials.In this study,we present a novel approach that introduces a 3D numerical manifold method(3D-NMM)with a geometric kernel to enhance computational efficiency.Specifically,the maximum tensile stress criterion is adopted as a crack growth criterion to achieve strong discontinuous crack growth,and a local crack tracking algorithm and an angle correction technique are incorporated to address minor limitations of the algorithm in a 3D model.The implementation of the program is carried out in Python,using object-oriented programming in two independent modules:a calculation module and a crack module.Furthermore,we propose feasible improvements to enhance the performance of the algorithm.Finally,we demonstrate the feasibility and effectiveness of the enhanced algorithm in the 3D-NMM using four numerical examples.This study establishes the potential of the 3DNMM,combined with the local tracking algorithm,for accurately modeling 3D crack propagation in brittle rock materials.
