Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soar...Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.展开更多
Classifying and ranking the huge amounts of landscape planning works of urban wetland park is always difficult due to the multi-functions （ecological, leisure, educational and disaster prevention） of the urban wetla...Classifying and ranking the huge amounts of landscape planning works of urban wetland park is always difficult due to the multi-functions （ecological, leisure, educational and disaster prevention） of the urban wetland park. Therefore, an optimizing rank system is urgently needed. Analytic Hierarchy Process （AHP） and Technique for Order Preference by Similarity to Ideal Solution （TOPSIS） models were used to rank the planning works of 30 urban wetland park based on four mainly factors, which included landscape ecological planning, landscape planning, ecological planning and economic planning. The study indicated that the AHP- TOPSIS model had good discrimination in the classification and ranking of landscape planning works of urban wetland park and it was also applicable to the planning works of other urban greenbelts.展开更多
As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost ...As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.展开更多
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established...A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.展开更多
This paper introduces a novel approach for controlling the exterior ballistic properties of spin-stabilized bullets by optimizing their internal mass distributions. Specifically, the properties of interest are the bul...This paper introduces a novel approach for controlling the exterior ballistic properties of spin-stabilized bullets by optimizing their internal mass distributions. Specifically, the properties of interest are the bullets’ stability characteristics that are examined through dynamic and gyroscopic stability parameters.New analytical expressions for aerodynamic quantities are also derived to address the compressibility of air. These expressions are utilized in a bullet model that enables efficient computation of the stability parameters for a given mass distribution. The bullet model is used in the formulation of nonlinear optimization problems that provide optimal mass distributions with respect to given goals, i.e., desired stability characteristics. The bullet types investigated in this paper are a long range bullet and a limited range training bullet. In the optimization of the mass distribution of the long range bullet, the goal is that the bullet stays stable for as long as possible. The mass distribution of the training bullet is optimized such that the bullet is stable at launch but becomes unstable shortly afterwards. The global optimal solutions obtained with the new approach fulfill the desired stability characteristics better than currently used uniformly filled bullets. Overall, the optimization approach reveals a new goal focused philosophy for bullet design compared to current trial and error design practices.展开更多
To provide theoretical basis for square honeycombs used as crashworthy structures, energy-absorption properties of metal square honeycombs and the size optimization were performed. Specific energy absorption(SEA) was ...To provide theoretical basis for square honeycombs used as crashworthy structures, energy-absorption properties of metal square honeycombs and the size optimization were performed. Specific energy absorption(SEA) was defined as the energy absorbed by the honeycomb structure per unit volume. This parameter was often used for determining the crashworthiness of thin-walled structures. In order to find the most optimized metal square honeycomb structure with the maximum SEA and the lowest peak stress, the cell length and the foil thickness of the metal honeycombs were optimized, with a low peak stress and a high SEA set as the two primary objectives. The pre-processing software Patran was used to build FE models, and the explicit solver LS-DYNA was employed to perform the crashworthiness analyses. The results show that the square honeycomb exhibits good energy absorption performance in some cases. The geometry is effective using 16.8% less buffer structure volume than the hexagonal honeycombs with a peak stress limitation of 1.21 MPa.展开更多
A geometrical parameters optimization and reducers selection method was proposed for robotic manipulators design. The Lagrangian approach was employed in deriving the dynamic model of a two-DOF manipulator. The flexib...A geometrical parameters optimization and reducers selection method was proposed for robotic manipulators design. The Lagrangian approach was employed in deriving the dynamic model of a two-DOF manipulator. The flexibility of links and joints was taken into account in the mechanical structure dimensions optimization and reducers selection, in which Timoshenko model was used to discretize the hollow links. Two criteria, i.e. maximization of fundamental frequency and minimization of self-mass/load ratio, were utilized to optimize the manipulators. The NSGA-II (fast elitist nondominated sorting genetic algorithms) was employed to solve the multi-objective optimization problem. How the joints flexibility affects the manipulators design was analyzed and shown in the numerical analysis example. The results indicate that simultaneous consideration of the joints and the links flexibility is very necessary for manipulators optimal design. Finally, several optimal combinations were provided. The effectiveness of the optimization method was proved by comparing with ADAMS simulation results. The self-mass/load ratio error of the two methods is within 10%. The maximum error of the natural frequency by the two methods is 23.74%. The method proposed in this work provides a fast and effective pathway for manipulator design and reducers selection.展开更多
Reliability, maintainability and testability （RMT） are important properties of equipment, since they have important influ- ence on operational availability and life cycle costs （LCC）. There- fore, weighting and op...Reliability, maintainability and testability （RMT） are important properties of equipment, since they have important influ- ence on operational availability and life cycle costs （LCC）. There- fore, weighting and optimizing the three properties are of great significance. A new approach for optimization of RMT parameters is proposed. First of all, the model for the equipment operation pro- cess is established based on the generalized stochastic Petri nets （GSPN） theory. Then, by solving the GSPN model, the quantitative relationship between operational availability and RMT parameters is obtained. Afterwards, taking history data of similar equipment and operation process into consideration, a cost model of design, manufacture and maintenance is developed. Based on operational availability, the cost model and parameters ranges, an optimization model of RMT parameters is built. Finally, the effectiveness and practicability of this approach are validated through an example.展开更多
In order to improve the extracellular endo-1,4-β-mannosidase(MAN) activity of recombinant Pichia pastoris, optimization of signal peptides was investigated. At first, five potential signal peptides(W1, MF4 I, INU1 A,...In order to improve the extracellular endo-1,4-β-mannosidase(MAN) activity of recombinant Pichia pastoris, optimization of signal peptides was investigated. At first, five potential signal peptides(W1, MF4 I, INU1 A, αpre, HFBI) were chosen to be analyzed by Signal P 4.0, among which W1 was designed. Then, the widely used signal peptide α-factor in expression vector p GAPZαA was replaced by those five signal peptides to reconstruct five new expression vectors. MAN activity was assayed after expression vectors were transformed into Pichia pastoris. The data show that the relative efficiencies of W1, MF4 I, INU1 A, αpre, and HFBI signal peptides are 23.5%, 203.5%, 0, 79.7%, and 120.3% compared with α-factor, respectively. The further gene copy number determination by the quantitative real-time PCR reveals that the MAN activities mediated by α-factor from 1 to 6 gene copy number levels are 12.95, 43.33, 126.63, 173.53, 103.23 and 88.63 U/m L, while those mediated by MF4 I are 79.22, 133.89, 260.14, 347.5, 206.15 and 181.89 U/m L, respectively. The maximum MAN activity reached 347.5 U/m L with 4 gene copies mediated by MF4 I. These results indicate that replacing the signal peptide α-factor with MF4 I and increasing MAN gene copies to a proper number can greatly improve the secretory expression of MAN.展开更多
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the dat...Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.展开更多
It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optima...It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.展开更多
The determination of material formula needs try-and-error experiment,and consumes large amount of time and fund.In order to solve the problem,a comprehensive method is established,via the experiment of artificial-simi...The determination of material formula needs try-and-error experiment,and consumes large amount of time and fund.In order to solve the problem,a comprehensive method is established,via the experiment of artificial-similar material formula of a mine slope.We controlled the samples by the compactness,and arranged the formula of the test group with the method of the uniform formula experiment.The physical and mechanical parameters of these samples were analyzed using the method of the partial least-squares regression(PLS).And a mathematical model of the indexes of physical and mechanics parameters relating to the factors of formulation constituents was established eventually.We used the model to analyze the effect of each formulation constituent on physical and mechanics parameters of samples.The experiment results and analysis illustrates that1)in the formulation of similar material,the effect of raw materials on the internal friction angleφand cohesion C is opposite;2)The method can highly facilitate the process of the of preparing artificial-similar materials,more economic and effective.展开更多
Decentralization and strengthening supervision are the two wings of the reform of administrative examination and approval system. At present, in the supervision process of Chinese government, the phenomenon of strict ...Decentralization and strengthening supervision are the two wings of the reform of administrative examination and approval system. At present, in the supervision process of Chinese government, the phenomenon of strict approval and slack supervision still exists in some local governments. And the dynamic supervision mechanism and supervision restriction mechanism of some local governments are still not perfect. These problems directly lead to"regulatory vacuum"and service absence and other circumstances. The existence of these circumstances will block the process of China's administrative examination and approval system reform. As a result to explore how to promote government supervision innovation and optimize government services is the first important thing, and then to create conditions for the rapid, stable and orderly progress of the administrative examination,what's more to approval system reform.Based on this thesis, the author realizes legal supervision, implement informatization supervision.It not only has increased normalized supervision, improve the administrative examination and approval all-round supervision mechanism, but also established the third party assessment normalization mechanism, and optimize the administrative examination and approval policy advocacy mechanism as well.展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the ...In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%.展开更多
The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-inpu...The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.展开更多
Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical...Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.展开更多
基金support received by the National Natural Science Foundation of China(Grant Nos.52372398&62003272).
文摘Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.
基金Supported by Henan Major Scientific and Technological Project (102102310246)
文摘Classifying and ranking the huge amounts of landscape planning works of urban wetland park is always difficult due to the multi-functions （ecological, leisure, educational and disaster prevention） of the urban wetland park. Therefore, an optimizing rank system is urgently needed. Analytic Hierarchy Process （AHP） and Technique for Order Preference by Similarity to Ideal Solution （TOPSIS） models were used to rank the planning works of 30 urban wetland park based on four mainly factors, which included landscape ecological planning, landscape planning, ecological planning and economic planning. The study indicated that the AHP- TOPSIS model had good discrimination in the classification and ranking of landscape planning works of urban wetland park and it was also applicable to the planning works of other urban greenbelts.
基金Project(B01B1203)supported by Sichuan Province Key Laboratory of Comprehensive Transportation,ChinaProject(SWJTU09BR141)supported by the Southwest Jiaotong University,China
文摘As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.
文摘A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
文摘This paper introduces a novel approach for controlling the exterior ballistic properties of spin-stabilized bullets by optimizing their internal mass distributions. Specifically, the properties of interest are the bullets’ stability characteristics that are examined through dynamic and gyroscopic stability parameters.New analytical expressions for aerodynamic quantities are also derived to address the compressibility of air. These expressions are utilized in a bullet model that enables efficient computation of the stability parameters for a given mass distribution. The bullet model is used in the formulation of nonlinear optimization problems that provide optimal mass distributions with respect to given goals, i.e., desired stability characteristics. The bullet types investigated in this paper are a long range bullet and a limited range training bullet. In the optimization of the mass distribution of the long range bullet, the goal is that the bullet stays stable for as long as possible. The mass distribution of the training bullet is optimized such that the bullet is stable at launch but becomes unstable shortly afterwards. The global optimal solutions obtained with the new approach fulfill the desired stability characteristics better than currently used uniformly filled bullets. Overall, the optimization approach reveals a new goal focused philosophy for bullet design compared to current trial and error design practices.
基金Project(07018) supported by the College Discipline Innovation Wisdom Plan in China
文摘To provide theoretical basis for square honeycombs used as crashworthy structures, energy-absorption properties of metal square honeycombs and the size optimization were performed. Specific energy absorption(SEA) was defined as the energy absorbed by the honeycomb structure per unit volume. This parameter was often used for determining the crashworthiness of thin-walled structures. In order to find the most optimized metal square honeycomb structure with the maximum SEA and the lowest peak stress, the cell length and the foil thickness of the metal honeycombs were optimized, with a low peak stress and a high SEA set as the two primary objectives. The pre-processing software Patran was used to build FE models, and the explicit solver LS-DYNA was employed to perform the crashworthiness analyses. The results show that the square honeycomb exhibits good energy absorption performance in some cases. The geometry is effective using 16.8% less buffer structure volume than the hexagonal honeycombs with a peak stress limitation of 1.21 MPa.
基金Project(2009AA04Z216) supported by the National High-Tech Research and Development Program (863 Program) of ChinaProject(2009ZX04013-011) supported by the National Science and Technology Major Project of ChinaProject supported by the HIT Oversea Talents Introduction Program,China
文摘A geometrical parameters optimization and reducers selection method was proposed for robotic manipulators design. The Lagrangian approach was employed in deriving the dynamic model of a two-DOF manipulator. The flexibility of links and joints was taken into account in the mechanical structure dimensions optimization and reducers selection, in which Timoshenko model was used to discretize the hollow links. Two criteria, i.e. maximization of fundamental frequency and minimization of self-mass/load ratio, were utilized to optimize the manipulators. The NSGA-II (fast elitist nondominated sorting genetic algorithms) was employed to solve the multi-objective optimization problem. How the joints flexibility affects the manipulators design was analyzed and shown in the numerical analysis example. The results indicate that simultaneous consideration of the joints and the links flexibility is very necessary for manipulators optimal design. Finally, several optimal combinations were provided. The effectiveness of the optimization method was proved by comparing with ADAMS simulation results. The self-mass/load ratio error of the two methods is within 10%. The maximum error of the natural frequency by the two methods is 23.74%. The method proposed in this work provides a fast and effective pathway for manipulator design and reducers selection.
文摘Reliability, maintainability and testability （RMT） are important properties of equipment, since they have important influ- ence on operational availability and life cycle costs （LCC）. There- fore, weighting and optimizing the three properties are of great significance. A new approach for optimization of RMT parameters is proposed. First of all, the model for the equipment operation pro- cess is established based on the generalized stochastic Petri nets （GSPN） theory. Then, by solving the GSPN model, the quantitative relationship between operational availability and RMT parameters is obtained. Afterwards, taking history data of similar equipment and operation process into consideration, a cost model of design, manufacture and maintenance is developed. Based on operational availability, the cost model and parameters ranges, an optimization model of RMT parameters is built. Finally, the effectiveness and practicability of this approach are validated through an example.
基金Project(13JJ9002)supported by Hunan Provincial Natural Science Foundation of ChinaProject(2012XK4081)supported by the Key Science Technology Plan Project of Hunan Provincial Science&Technology Department,ChinaProject(CX2012B124)supported by the Graduate Degree Thesis Innovation Program of Hunan Province,China
文摘In order to improve the extracellular endo-1,4-β-mannosidase(MAN) activity of recombinant Pichia pastoris, optimization of signal peptides was investigated. At first, five potential signal peptides(W1, MF4 I, INU1 A, αpre, HFBI) were chosen to be analyzed by Signal P 4.0, among which W1 was designed. Then, the widely used signal peptide α-factor in expression vector p GAPZαA was replaced by those five signal peptides to reconstruct five new expression vectors. MAN activity was assayed after expression vectors were transformed into Pichia pastoris. The data show that the relative efficiencies of W1, MF4 I, INU1 A, αpre, and HFBI signal peptides are 23.5%, 203.5%, 0, 79.7%, and 120.3% compared with α-factor, respectively. The further gene copy number determination by the quantitative real-time PCR reveals that the MAN activities mediated by α-factor from 1 to 6 gene copy number levels are 12.95, 43.33, 126.63, 173.53, 103.23 and 88.63 U/m L, while those mediated by MF4 I are 79.22, 133.89, 260.14, 347.5, 206.15 and 181.89 U/m L, respectively. The maximum MAN activity reached 347.5 U/m L with 4 gene copies mediated by MF4 I. These results indicate that replacing the signal peptide α-factor with MF4 I and increasing MAN gene copies to a proper number can greatly improve the secretory expression of MAN.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
基金supported by the National Natural Science Foundation of China(6107901361079014+4 种基金61403198)the National Natural Science Funds and Civil Aviaiton Mutual Funds(U1533128U1233114)the Programs of Natural Science Foundation of China and China Civil Aviation Joint Fund(60939003)the Natural Science Foundation of Jiangsu Province in China(BK2011737)
文摘It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.
基金Projects(41372312,51379194)supported by the National Natural Science Foundation of ChinaProject(CUGL140817)supported by the Fundamental Research Funds for the Central Universities of China University of Geosciences(Wuhan)+1 种基金Project(2014CFB894)supported by the Natural Science Foundation of Hubei Province of ChinaProject(2014M552113)supported by the China Postdoctoral Science Foundation
文摘The determination of material formula needs try-and-error experiment,and consumes large amount of time and fund.In order to solve the problem,a comprehensive method is established,via the experiment of artificial-similar material formula of a mine slope.We controlled the samples by the compactness,and arranged the formula of the test group with the method of the uniform formula experiment.The physical and mechanical parameters of these samples were analyzed using the method of the partial least-squares regression(PLS).And a mathematical model of the indexes of physical and mechanics parameters relating to the factors of formulation constituents was established eventually.We used the model to analyze the effect of each formulation constituent on physical and mechanics parameters of samples.The experiment results and analysis illustrates that1)in the formulation of similar material,the effect of raw materials on the internal friction angleφand cohesion C is opposite;2)The method can highly facilitate the process of the of preparing artificial-similar materials,more economic and effective.
基金the Research on Government Responsibility in the Process of Purchasing Public Service and the project of social science in Anhui Province(AHSKY 2014018)It is also a cooperation fund of the national administrative institute and one of the results of A Study on the Construction of the Responsibility List and the Government of the Rule of Law
文摘Decentralization and strengthening supervision are the two wings of the reform of administrative examination and approval system. At present, in the supervision process of Chinese government, the phenomenon of strict approval and slack supervision still exists in some local governments. And the dynamic supervision mechanism and supervision restriction mechanism of some local governments are still not perfect. These problems directly lead to"regulatory vacuum"and service absence and other circumstances. The existence of these circumstances will block the process of China's administrative examination and approval system reform. As a result to explore how to promote government supervision innovation and optimize government services is the first important thing, and then to create conditions for the rapid, stable and orderly progress of the administrative examination,what's more to approval system reform.Based on this thesis, the author realizes legal supervision, implement informatization supervision.It not only has increased normalized supervision, improve the administrative examination and approval all-round supervision mechanism, but also established the third party assessment normalization mechanism, and optimize the administrative examination and approval policy advocacy mechanism as well.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金Supported by the Beijing Municipal Science&Technology Commission(Z211100004421012),the Key Reaserch and Development Pro⁃gram of China(2022YFF0605902)。
文摘In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%.
文摘The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.
文摘Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.
