A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large an...A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large and complex flow structure. To fully utilize the computing power of High Performance Computing(HPC) systems, it is necessary to develop specific methodologies to optimize the performance of applications based on the particular system’s architecture. The Sunway TaihuLight supercomputer is currently ranked as the fastest supercomputer in the world. This article presents a heterogeneous parallel algorithm design and performance optimization of a high-order WENO on Sunway TaihuLight. We analyzed characteristics of kernel functions, and proposed an appropriate heterogeneous parallel model. We also figured out the best division strategy for computing tasks,and implemented the parallel algorithm on Sunway TaihuLight. By using access optimization, data dependency elimination, and vectorization optimization, our parallel algorithm can achieve up to 172× speedup on one single node, and additional 58× speedup on 64 nodes, with nearly linear scalability.展开更多
Cardinality constrained optimization problems(CCOPs) are fixed-size subset selection problems with applications in several fields. CCOPs comprising multiple scenarios, such as cardinality values that form an interval,...Cardinality constrained optimization problems(CCOPs) are fixed-size subset selection problems with applications in several fields. CCOPs comprising multiple scenarios, such as cardinality values that form an interval, can be defined as multi-scenario CCOPs(MSCCOPs). An MSCCOP is expected to optimize the objective value of each cardinality to support decision-making processes. When the computation is conducted using traditional optimization algorithms, an MSCCOP often requires several passes(algorithmic runs) to obtain all the(near-)optima, where each pass handles a specific cardinality. Such separate passes abandon most of the knowledge(including the potential superior solution structures) learned in one pass that can also be used to improve the results of other passes. By considering this situation, we propose a generic transformation strategy that can be referred to as the Mucard strategy, which converts an MSCCOP into a low-dimensional multi-objective optimization problem(MOP) to simultaneously obtain all the(near-)optima of the MSCCOP in a single algorithmic run. In essence, the Mucard strategy combines separate passes that deal with distinct variable spaces into a single pass, enabling knowledge reuse and knowledge interchange of each cardinality among genetic individuals. The performance of the Mucard strategy was demonstrated using two typical MSCCOPs. For a given number of evolved individuals, the Mucard strategy improved the accuracy of the obtained solutions because of the in-process knowledge than that obtained by untransformed evolutionary algorithms, while reducing the average runtime. Furthermore, the equivalence between the optimal solutions of the transformed MOP and the untransformed MSCCOP can be theoretically proved.展开更多
The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally...The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally, through a competitive learning process, the SOMO algorithm searches for the minimum of an objective function. The MaxMin-SOMO algorithm is the generalization of SOMO with two winners for simultaneously finding two winning neurons i.e., first winner stands for minimum and second one for maximum of the objective function. In this paper, the convergence analysis of the MaxMin-SOMO is presented. More specifically, we prove that the distance between neurons decreases at each iteration and finally converge to zero. The work is verified with the experimental results.展开更多
A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed num...A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed number of previous noisy function values as a criterion for accepting good and rejecting bad steps.The scheine allows the algorithm to move in bigger steps and avoid steps proportional to 1/k when it is expected that larger steps will improve the performance.An algorithin with the new adaptive scheme is defined for a general descent direction.The ahnost sure convergence is established.The performance of new algorithm is tested on a set of standard test problems and compared with relevant algorithms.Numerical results support theoretical expectations and verify efficiency of the algorithm regardless of chosen search direction and noise level.Numerical results on probleins arising in machine learning are also presented.Linear regression problem is considered using real data set.The results suggest that the proposed algorithln shows proinise.展开更多
A robust topology optimization design framework is developed to solve lightweight structural design problems under uncertain conditions. To enhance the calculation accuracy and flexibility of the statistical moments o...A robust topology optimization design framework is developed to solve lightweight structural design problems under uncertain conditions. To enhance the calculation accuracy and flexibility of the statistical moments of robust analysis, number theory integral method is applied to sample point selection and weight assignment. Both the structure topology optimization and number theory integral methods are combined to form a new robust topology optimization method. A suspension control arm problem is provided as a demonstration of robust topology optimization methods under loading uncertainties. Based on the results of deterministic and robust topology optimization, it is demonstrated that the proposed robust topology optimization method can produce a more robust design than that obtained by deterministic topology optimization. It is also found that this new approach is easy to apply in the existing commercial topology optimization software and thus feasible in practical engineering problems.展开更多
The development of the capabilities of computational tools has created up new possibilities for the effective use of a number of classical mathematical methods and algorithms for solving many important problems in the...The development of the capabilities of computational tools has created up new possibilities for the effective use of a number of classical mathematical methods and algorithms for solving many important problems in the power engineering. In particular, a set of algorithms are developed to optimize the modes of electric power systems based on genetic algorithms. At the same time, the issues of taking into account functional constraints in solving such problems by genetic algorithms need to be improved. In accordance with it in this article the problems of taking into account of different constraints in optimization of modes of power systems using genetic algorithms are considered. The algorithm of optimization by genetic algorithm taking into account of functional constraints in forms of equality and inequality by penalty functions is proposed. The results of research of proposed algorithm’s efficiency in example of optimization of mode of power system with 8 buses, 4 thermal power plants and 3 transmission lines with controlled power flow are presented.展开更多
In this paper, we approach the problem of obtaining approximate solution of second-order initial value problems by converting it to an optimization problem. It is assumed that the solution can be approximated by a pol...In this paper, we approach the problem of obtaining approximate solution of second-order initial value problems by converting it to an optimization problem. It is assumed that the solution can be approximated by a polynomial. The coefficients of the polynomial are then optimized using simulated annealing technique. Numerical examples with good results show the accuracy of the proposed approach compared with some existing methods.展开更多
It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil...It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator - the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution.展开更多
Due to an increased need in hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be build throughout the world. The focus of this paper is on the non-probabilistic-based...Due to an increased need in hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be build throughout the world. The focus of this paper is on the non-probabilistic-based design of new arch-type dams by applying means of robust design optimization (RDO). This type of optimization takes into account uncertainties in the loads and in the material properties of the structure. As classical procedures of probabilistic-based optimization under uncertainties, such as RDO and reliability-based design optimization (RBDO), are in general computationally expensive and rely on estimates of the system’s response variance, we will not follow a full-probabilistic approach but work with predefined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. As a result, robust and reliable designs are obtained and the result is independent from any assumptions on stochastic properties of the random variables in the model. The optimization of an arch-type dam is realized here by a robust optimization method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RDO method provides a robust solution against uncertainty. To reduce the computational cost, a ranking strategy and an approximation model are further involved to do a preliminary screening. By this means, the robust design can generate an improved arch dam structure that ensures both safety and serviceability during its lifetime.展开更多
From the perspective of the geographical distribution, considering production fare, supply chain information and quality rating of the manufacturing resource(MR), a manufacturing resource allocation(MRA) model conside...From the perspective of the geographical distribution, considering production fare, supply chain information and quality rating of the manufacturing resource(MR), a manufacturing resource allocation(MRA) model considering the geographical distribution in cloud manufacturing(CM) environment is built. The model includes two stages, preliminary selection stage and optimal selection stage. The membership function is used to select MRs from cloud resource pool(CRP) in the first stage, and then the candidate resource pool is built. In the optimal selection stage, a multi-objective optimization algorithm, particle swarm optimization(PSO) based on the method of relative entropy of fuzzy sets(REFS_PSO), is used to select optimal MRs from the candidate resource pool, and an optimal manufacturing resource supply chain is obtained at last. To verify the performance of REFS_PSO, NSGA-Ⅱ and PSO based on random weighting(RW_PSO) are selected as the comparison algorithms. They all are used to select optimal MRs at the second stage. The experimental results show solution obtained by REFS_PSO is the best. The model and the method proposed are appropriate for MRA in CM.展开更多
Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source fores...Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source forest inventories of Finland where the interpretation units are 16 m×16 m grid cells.Using these cells as simulation units in forest planning would lead to very large planning problems.This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations.This study developed a cellular automaton(CA)for aggregating grid cells into larger calculation units,which in this study were called stands.The criteria used in stand delineation were the shape and size of the stands,and homogeneity of stand attributes within the stand.The stand attributes were:main site type(upland or peatland forest),site fertility,mean tree diameter,mean tree height and stand basal area.In the CA,each cell was joined to one of its adjacent stands for several iterations,until the cells formed a compact layout of homogeneous stands.The CA had several parameters.Due to high number possible parameter combinations,particle swarm optimization was used to find the optimal set of parameter values.Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes.When the CA was optimized without any restrictions for its parameters,the resulting stand delineation consisted of small and irregular stands.A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands(<0.1 ha).However,there was within-stand variation in fertility class due to small-scale variation in the data.The stands delineated by the CA explained 66–87%of variation in stand basal area,mean tree height and mean diameter,and 41–92%of variation in the fertility class of the site.It was concluded that the CA develope展开更多
Wireless communication technologies such as fifth generation mobile networks(5 G)will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer the underlying technologies t...Wireless communication technologies such as fifth generation mobile networks(5 G)will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer the underlying technologies to entire industries to support Internet of things(IOT)technologies.Compared to existing mobile communication techniques,5 G has more varied applications and its corresponding system design is more complicated.The resurgence of artificial intelligence(AI)techniques offers an alternative option that is possibly superior to traditional ideas and performance.Typical and potential research directions related to the promising contributions that can be achieved through AI must be identified,evaluated,and investigated.To this end,this study provides an overview that first combs through several promising research directions in AI for 5 G technologies based on an understanding of the key technologies in 5 G.In addition,the study focuses on providing design paradigms including 5 G network optimization,optimal resource allocation,5 G physical layer unified acceleration,end-to-end physical layer joint optimization,and so on.展开更多
This paper considers a variation on the Dubins path problem and proposes an improved waypoint navigation (WN) algorithm called Dubins waypoint navigation (DWN). Based on the Dubins path problem, an algorithm is develo...This paper considers a variation on the Dubins path problem and proposes an improved waypoint navigation (WN) algorithm called Dubins waypoint navigation (DWN). Based on the Dubins path problem, an algorithm is developed that is updated in real-time with a horizon of three waypoints. The purpose of DWN is to overcome a problem that we find in existing WN for small-class fixed-wing unmanned aerial vehicles (UAV) of not accurately reaching waypoints. This problem results at times in high overshoot and, in the presence of wind disturbances, it can cause a vehicle to miss the waypoint and swirl around it. To prevent this, the DWN creates “new waypoints” that are in the background, called turning points. Examples illustrate the improvement of the performance of WN achieved using the DWN algorithm in terms of the targeting of waypoints while reducing fuel and time.展开更多
A surrogate-model-based aerodynamic optimization design method for cycloidal propeller in hover was proposed,in order to improve its aerodynamic efficiency,and analyze the basic criteria for its aerodynamic optimizati...A surrogate-model-based aerodynamic optimization design method for cycloidal propeller in hover was proposed,in order to improve its aerodynamic efficiency,and analyze the basic criteria for its aerodynamic optimization design.The reliability and applicability of overset mesh method were verified.An optimization method based on Kriging surrogate model was proposed to optimize the geometric parameters for cycloidal propeller in hover with the use of genetic algorithm.The optimization results showed that the thrust coefficient was increased by 3.56%,the torque coefficient reduced by 12.05%,and the figure of merit(FM)increased by 19.93%.The optimization results verified the feasibility of this design idea.Although the optimization was only carried out at a single rotation speed,the aerodynamic efficiency was also significantly improved over a wide range of rotation speeds.The optimal configuration characteristics for micro and small-sized cycloidal propeller were:solidity of 0.2-0.22,maximum pitch angle of 25°-35°,pitch axis locating at 35%-45% of the blade chord length.展开更多
Alfalfa (Medicago sativa) is an important forage crop belonging to the Fabaceae family. It is cultivated across the world for fodder and originated in Asia. Alfalfa cultivar Regen-SY was used in this study which is a ...Alfalfa (Medicago sativa) is an important forage crop belonging to the Fabaceae family. It is cultivated across the world for fodder and originated in Asia. Alfalfa cultivar Regen-SY was used in this study which is a hybrid of first-generation self-parents from Regen-S (M. sativa) and Regen-Y (Medicago falcata) research cultivars. The main objective of the study was to optimize conditions for the isolation and liquid culture of alfalfa Regen-SY protoplasts. Several factors like enzyme combination, incubation time, plant age, centrifugation speed and shaker speed affecting protoplast isolation and culture were optimized in the study. The yield and viability of the protoplasts was determined by using hemocytometer and Fluorescein diacetate (FDA) staining respectively. Results showed that factors like enzyme combination, incubation time, plant age, centrifugation speed and Mannitol concentration significantly (p ≤ 0.05) affect protoplast yield and viability whereas shaker speed didn’t result in any significant difference in the yield and viability of protoplasts. Using optimum conditions protoplasts were cultured in the liquid medium and microcalli formation was achieved after five weeks of the culture. The protocol established in this study will assist researchers in the isolation and culture of protoplasts in alfalfa and will accelerate the research processes like protoplast fusion and genetic engineering.展开更多
This work deals with phosphate ions removal in aqueous solution by adsorption carried out using two clays, both in activated form. One, non-swelling clay, rich in kaolinite, is associated with illite and quartz. The o...This work deals with phosphate ions removal in aqueous solution by adsorption carried out using two clays, both in activated form. One, non-swelling clay, rich in kaolinite, is associated with illite and quartz. The other, swelling, richer in montmorillonite, is associated with kaolinite, illite and quartz. Seven factors including these two clays were taken into account in a series of experimental designs in order to model and optimize the acidic activation process favoring a better phosphate removal. In addition to the choice of clay nature, the study was also interested in the identification of the mineral acid, between hydrochloric acid and sulfuric acid, which would promote this acidic activation. Response Surface Methodology (RSM) was used for this purpose by sequentially applying Plackett and Burman Design and Full Factorial Design (FD) for screening. Then, a central composite design (CCD) was used for modeling the activation process. A mathematical surface model has been successfully established. Thus, the best acidic activation conditions were obtained by activating the montmorillonite clay with a 2N sulfuric acid solution, in an acid/clay mass ratio of 7.5 at 100°C for 16H. The phosphate removal maximum rate obtained was estimated at 89.32% ± 0.86%.展开更多
基金supported by the National High-Tech Research and Development(863)Program of China(No.2015AA015306)the Science and Technology Plan of Beijing Municipality(No.Z161100000216147)+2 种基金the National Natural Science Foundation of China(No.61762074)Youth Foundation Program of Qinghai University(No.2016-QGY-5)the National Natural Science Foundation of Qinghai Province(No.2019-ZJ7034).
文摘A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large and complex flow structure. To fully utilize the computing power of High Performance Computing(HPC) systems, it is necessary to develop specific methodologies to optimize the performance of applications based on the particular system’s architecture. The Sunway TaihuLight supercomputer is currently ranked as the fastest supercomputer in the world. This article presents a heterogeneous parallel algorithm design and performance optimization of a high-order WENO on Sunway TaihuLight. We analyzed characteristics of kernel functions, and proposed an appropriate heterogeneous parallel model. We also figured out the best division strategy for computing tasks,and implemented the parallel algorithm on Sunway TaihuLight. By using access optimization, data dependency elimination, and vectorization optimization, our parallel algorithm can achieve up to 172× speedup on one single node, and additional 58× speedup on 64 nodes, with nearly linear scalability.
基金National Natural Science Foundation of China(Grant Nos.61751208,61502510,61773390)Outstanding Natural Science Foundation of Hunan Province(Grant No.2017JJ1001)Special Program for the Applied Basic Research of National University of Defense Technology(Grant No.ZDYYJCYJ20140601).
文摘Cardinality constrained optimization problems(CCOPs) are fixed-size subset selection problems with applications in several fields. CCOPs comprising multiple scenarios, such as cardinality values that form an interval, can be defined as multi-scenario CCOPs(MSCCOPs). An MSCCOP is expected to optimize the objective value of each cardinality to support decision-making processes. When the computation is conducted using traditional optimization algorithms, an MSCCOP often requires several passes(algorithmic runs) to obtain all the(near-)optima, where each pass handles a specific cardinality. Such separate passes abandon most of the knowledge(including the potential superior solution structures) learned in one pass that can also be used to improve the results of other passes. By considering this situation, we propose a generic transformation strategy that can be referred to as the Mucard strategy, which converts an MSCCOP into a low-dimensional multi-objective optimization problem(MOP) to simultaneously obtain all the(near-)optima of the MSCCOP in a single algorithmic run. In essence, the Mucard strategy combines separate passes that deal with distinct variable spaces into a single pass, enabling knowledge reuse and knowledge interchange of each cardinality among genetic individuals. The performance of the Mucard strategy was demonstrated using two typical MSCCOPs. For a given number of evolved individuals, the Mucard strategy improved the accuracy of the obtained solutions because of the in-process knowledge than that obtained by untransformed evolutionary algorithms, while reducing the average runtime. Furthermore, the equivalence between the optimal solutions of the transformed MOP and the untransformed MSCCOP can be theoretically proved.
基金supported by National Natural Science Foundation of China(Nos.11171367 and 61502068)the Fundamental Research Funds for the Central Universities of China(No.3132014094)+1 种基金the China Postdoctoral Science Foundation(Nos.2013M541213 and 2015T80239)Fundacao da Amaro a Pesquisa do Estado de Sao Paulo(FAPESP)Brazil(No.2012/23329-5).
文摘The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally, through a competitive learning process, the SOMO algorithm searches for the minimum of an objective function. The MaxMin-SOMO algorithm is the generalization of SOMO with two winners for simultaneously finding two winning neurons i.e., first winner stands for minimum and second one for maximum of the objective function. In this paper, the convergence analysis of the MaxMin-SOMO is presented. More specifically, we prove that the distance between neurons decreases at each iteration and finally converge to zero. The work is verified with the experimental results.
基金Macedonia scientific research projects for 2014/2015 academic year.
文摘A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed number of previous noisy function values as a criterion for accepting good and rejecting bad steps.The scheine allows the algorithm to move in bigger steps and avoid steps proportional to 1/k when it is expected that larger steps will improve the performance.An algorithin with the new adaptive scheme is defined for a general descent direction.The ahnost sure convergence is established.The performance of new algorithm is tested on a set of standard test problems and compared with relevant algorithms.Numerical results support theoretical expectations and verify efficiency of the algorithm regardless of chosen search direction and noise level.Numerical results on probleins arising in machine learning are also presented.Linear regression problem is considered using real data set.The results suggest that the proposed algorithln shows proinise.
基金Supported by the National Key Research and Development Program of China (2017YFB0103704)the National Natural Science Foundation of China (51675044).
文摘A robust topology optimization design framework is developed to solve lightweight structural design problems under uncertain conditions. To enhance the calculation accuracy and flexibility of the statistical moments of robust analysis, number theory integral method is applied to sample point selection and weight assignment. Both the structure topology optimization and number theory integral methods are combined to form a new robust topology optimization method. A suspension control arm problem is provided as a demonstration of robust topology optimization methods under loading uncertainties. Based on the results of deterministic and robust topology optimization, it is demonstrated that the proposed robust topology optimization method can produce a more robust design than that obtained by deterministic topology optimization. It is also found that this new approach is easy to apply in the existing commercial topology optimization software and thus feasible in practical engineering problems.
文摘The development of the capabilities of computational tools has created up new possibilities for the effective use of a number of classical mathematical methods and algorithms for solving many important problems in the power engineering. In particular, a set of algorithms are developed to optimize the modes of electric power systems based on genetic algorithms. At the same time, the issues of taking into account functional constraints in solving such problems by genetic algorithms need to be improved. In accordance with it in this article the problems of taking into account of different constraints in optimization of modes of power systems using genetic algorithms are considered. The algorithm of optimization by genetic algorithm taking into account of functional constraints in forms of equality and inequality by penalty functions is proposed. The results of research of proposed algorithm’s efficiency in example of optimization of mode of power system with 8 buses, 4 thermal power plants and 3 transmission lines with controlled power flow are presented.
文摘In this paper, we approach the problem of obtaining approximate solution of second-order initial value problems by converting it to an optimization problem. It is assumed that the solution can be approximated by a polynomial. The coefficients of the polynomial are then optimized using simulated annealing technique. Numerical examples with good results show the accuracy of the proposed approach compared with some existing methods.
基金the High-tech Research and Development Program of China (2014AA041802).
文摘It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator - the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution.
基金The corresponding author, Fengjie Tan, kindly likes to thank for the support of the Chinese Scholarship Council (No. 201406260202)to Bauhaus University for providing a good working environment.
文摘Due to an increased need in hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be build throughout the world. The focus of this paper is on the non-probabilistic-based design of new arch-type dams by applying means of robust design optimization (RDO). This type of optimization takes into account uncertainties in the loads and in the material properties of the structure. As classical procedures of probabilistic-based optimization under uncertainties, such as RDO and reliability-based design optimization (RBDO), are in general computationally expensive and rely on estimates of the system’s response variance, we will not follow a full-probabilistic approach but work with predefined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. As a result, robust and reliable designs are obtained and the result is independent from any assumptions on stochastic properties of the random variables in the model. The optimization of an arch-type dam is realized here by a robust optimization method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RDO method provides a robust solution against uncertainty. To reduce the computational cost, a ranking strategy and an approximation model are further involved to do a preliminary screening. By this means, the robust design can generate an improved arch dam structure that ensures both safety and serviceability during its lifetime.
基金Sponsored by the Program of Department of Science and Technology of Fujian Province(Grant No.2016H0015)the Collaborative Innovation Center of High-End Equipment Manufacturing in Fujian(Grant No.2015A003).
文摘From the perspective of the geographical distribution, considering production fare, supply chain information and quality rating of the manufacturing resource(MR), a manufacturing resource allocation(MRA) model considering the geographical distribution in cloud manufacturing(CM) environment is built. The model includes two stages, preliminary selection stage and optimal selection stage. The membership function is used to select MRs from cloud resource pool(CRP) in the first stage, and then the candidate resource pool is built. In the optimal selection stage, a multi-objective optimization algorithm, particle swarm optimization(PSO) based on the method of relative entropy of fuzzy sets(REFS_PSO), is used to select optimal MRs from the candidate resource pool, and an optimal manufacturing resource supply chain is obtained at last. To verify the performance of REFS_PSO, NSGA-Ⅱ and PSO based on random weighting(RW_PSO) are selected as the comparison algorithms. They all are used to select optimal MRs at the second stage. The experimental results show solution obtained by REFS_PSO is the best. The model and the method proposed are appropriate for MRA in CM.
文摘Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source forest inventories of Finland where the interpretation units are 16 m×16 m grid cells.Using these cells as simulation units in forest planning would lead to very large planning problems.This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations.This study developed a cellular automaton(CA)for aggregating grid cells into larger calculation units,which in this study were called stands.The criteria used in stand delineation were the shape and size of the stands,and homogeneity of stand attributes within the stand.The stand attributes were:main site type(upland or peatland forest),site fertility,mean tree diameter,mean tree height and stand basal area.In the CA,each cell was joined to one of its adjacent stands for several iterations,until the cells formed a compact layout of homogeneous stands.The CA had several parameters.Due to high number possible parameter combinations,particle swarm optimization was used to find the optimal set of parameter values.Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes.When the CA was optimized without any restrictions for its parameters,the resulting stand delineation consisted of small and irregular stands.A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands(<0.1 ha).However,there was within-stand variation in fertility class due to small-scale variation in the data.The stands delineated by the CA explained 66–87%of variation in stand basal area,mean tree height and mean diameter,and 41–92%of variation in the fertility class of the site.It was concluded that the CA develope
基金National Natural Science Foundation of China(Grant Nos.61501116,61521061).
文摘Wireless communication technologies such as fifth generation mobile networks(5 G)will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer the underlying technologies to entire industries to support Internet of things(IOT)technologies.Compared to existing mobile communication techniques,5 G has more varied applications and its corresponding system design is more complicated.The resurgence of artificial intelligence(AI)techniques offers an alternative option that is possibly superior to traditional ideas and performance.Typical and potential research directions related to the promising contributions that can be achieved through AI must be identified,evaluated,and investigated.To this end,this study provides an overview that first combs through several promising research directions in AI for 5 G technologies based on an understanding of the key technologies in 5 G.In addition,the study focuses on providing design paradigms including 5 G network optimization,optimal resource allocation,5 G physical layer unified acceleration,end-to-end physical layer joint optimization,and so on.
文摘This paper considers a variation on the Dubins path problem and proposes an improved waypoint navigation (WN) algorithm called Dubins waypoint navigation (DWN). Based on the Dubins path problem, an algorithm is developed that is updated in real-time with a horizon of three waypoints. The purpose of DWN is to overcome a problem that we find in existing WN for small-class fixed-wing unmanned aerial vehicles (UAV) of not accurately reaching waypoints. This problem results at times in high overshoot and, in the presence of wind disturbances, it can cause a vehicle to miss the waypoint and swirl around it. To prevent this, the DWN creates “new waypoints” that are in the background, called turning points. Examples illustrate the improvement of the performance of WN achieved using the DWN algorithm in terms of the targeting of waypoints while reducing fuel and time.
文摘A surrogate-model-based aerodynamic optimization design method for cycloidal propeller in hover was proposed,in order to improve its aerodynamic efficiency,and analyze the basic criteria for its aerodynamic optimization design.The reliability and applicability of overset mesh method were verified.An optimization method based on Kriging surrogate model was proposed to optimize the geometric parameters for cycloidal propeller in hover with the use of genetic algorithm.The optimization results showed that the thrust coefficient was increased by 3.56%,the torque coefficient reduced by 12.05%,and the figure of merit(FM)increased by 19.93%.The optimization results verified the feasibility of this design idea.Although the optimization was only carried out at a single rotation speed,the aerodynamic efficiency was also significantly improved over a wide range of rotation speeds.The optimal configuration characteristics for micro and small-sized cycloidal propeller were:solidity of 0.2-0.22,maximum pitch angle of 25°-35°,pitch axis locating at 35%-45% of the blade chord length.
文摘Alfalfa (Medicago sativa) is an important forage crop belonging to the Fabaceae family. It is cultivated across the world for fodder and originated in Asia. Alfalfa cultivar Regen-SY was used in this study which is a hybrid of first-generation self-parents from Regen-S (M. sativa) and Regen-Y (Medicago falcata) research cultivars. The main objective of the study was to optimize conditions for the isolation and liquid culture of alfalfa Regen-SY protoplasts. Several factors like enzyme combination, incubation time, plant age, centrifugation speed and shaker speed affecting protoplast isolation and culture were optimized in the study. The yield and viability of the protoplasts was determined by using hemocytometer and Fluorescein diacetate (FDA) staining respectively. Results showed that factors like enzyme combination, incubation time, plant age, centrifugation speed and Mannitol concentration significantly (p ≤ 0.05) affect protoplast yield and viability whereas shaker speed didn’t result in any significant difference in the yield and viability of protoplasts. Using optimum conditions protoplasts were cultured in the liquid medium and microcalli formation was achieved after five weeks of the culture. The protocol established in this study will assist researchers in the isolation and culture of protoplasts in alfalfa and will accelerate the research processes like protoplast fusion and genetic engineering.
文摘This work deals with phosphate ions removal in aqueous solution by adsorption carried out using two clays, both in activated form. One, non-swelling clay, rich in kaolinite, is associated with illite and quartz. The other, swelling, richer in montmorillonite, is associated with kaolinite, illite and quartz. Seven factors including these two clays were taken into account in a series of experimental designs in order to model and optimize the acidic activation process favoring a better phosphate removal. In addition to the choice of clay nature, the study was also interested in the identification of the mineral acid, between hydrochloric acid and sulfuric acid, which would promote this acidic activation. Response Surface Methodology (RSM) was used for this purpose by sequentially applying Plackett and Burman Design and Full Factorial Design (FD) for screening. Then, a central composite design (CCD) was used for modeling the activation process. A mathematical surface model has been successfully established. Thus, the best acidic activation conditions were obtained by activating the montmorillonite clay with a 2N sulfuric acid solution, in an acid/clay mass ratio of 7.5 at 100°C for 16H. The phosphate removal maximum rate obtained was estimated at 89.32% ± 0.86%.