After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s ...After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.展开更多
Micro-nano Earth Observation Satellite(MEOS)constellation has the advantages of low construction cost,short revisit cycle,and high functional density,which is considered a promising solution for serving rapidly growin...Micro-nano Earth Observation Satellite(MEOS)constellation has the advantages of low construction cost,short revisit cycle,and high functional density,which is considered a promising solution for serving rapidly growing observation demands.The observation Scheduling Problem in the MEOS constellation(MEOSSP)is a challenging issue due to the large number of satellites and tasks,as well as complex observation constraints.To address the large-scale and complicated MEOSSP,we develop a Two-Stage Scheduling Algorithm based on the Pointer Network with Attention mechanism(TSSA-PNA).In TSSA-PNA,the MEOS observation scheduling is decomposed into a task allocation stage and a single-MEOS scheduling stage.In the task allocation stage,an adaptive task allocation algorithm with four problem-specific allocation operators is proposed to reallocate the unscheduled tasks to new MEOSs.Regarding the single-MEOS scheduling stage,we design a pointer network based on the encoder-decoder architecture to learn the optimal singleMEOS scheduling solution and introduce the attention mechanism into the encoder to improve the learning efficiency.The Pointer Network with Attention mechanism(PNA)can generate the single-MEOS scheduling solution quickly in an end-to-end manner.These two decomposed stages are performed iteratively to search for the solution with high profit.A greedy local search algorithm is developed to improve the profits further.The performance of the PNA and TSSA-PNA on singleMEOS and multi-MEOS scheduling problems are evaluated in the experiments.The experimental results demonstrate that PNA can obtain the approximate solution for the single-MEOS scheduling problem in a short time.Besides,the TSSA-PNA can achieve higher observation profits than the existing scheduling algorithms within the acceptable computational time for the large-scale MEOS scheduling problem.展开更多
To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a...To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.展开更多
In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task i...In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.展开更多
A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated ...A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.展开更多
This paper investigates the problem of two-stage extended Kalman filter （TSEKF）-based fault estimation for reaction flywheels in satellite attitude control systems （ACSs）. Firstly, based on the separate-bias princ...This paper investigates the problem of two-stage extended Kalman filter （TSEKF）-based fault estimation for reaction flywheels in satellite attitude control systems （ACSs）. Firstly, based on the separate-bias principle, a satellite ACSs with actuator fault is transformed into an augmented nonlinear discrete stochastic model; then, a novel TSEKF is suggested such that it can simultane- ously estimate satellite attitude information and actuator faults no matter they are additive or mul- tiplicative; finally, the proposed approach is respectively applied to estimating bias faults and loss of effectiveness for reaction flywheels in satellite ACSs, and simulation results demonstrate the effec- tiveness of the proposed fault estimation approach.展开更多
Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions ...Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.展开更多
In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. Th...In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.展开更多
A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary ...A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.展开更多
As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions an...As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.展开更多
Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown ...Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.展开更多
Applying bio-oxidation waste solution(BOS)to chemical-biological two-stage oxidation process can significantly improve the bio-oxidation efficiency of arsenopyrite.This study aims to clarify the enhanced oxidation mec...Applying bio-oxidation waste solution(BOS)to chemical-biological two-stage oxidation process can significantly improve the bio-oxidation efficiency of arsenopyrite.This study aims to clarify the enhanced oxidation mechanism of arsenopyrite by evaluating the effects of physical and chemical changes of arsenopyrite in BOS chemical oxidation stage on mineral dissolution kinetics,as well as microbial growth activity and community structure composition in bio-oxidation stage.The results showed that the chemical oxidation contributed to destroying the physical and chemical structure of arsenopyrite surface and reducing the particle size,and led to the formation of nitrogenous substances on mineral surface.These chemical oxidation behaviors effectively promoted Fe^(3+)cycling in the bio-oxidation system and weakened the inhibitory effect of the sulfur film on ionic diffusion,thereby enhancing the dissolution kinetics of the arsenopyrite.Therefore,the bio-oxidation efficiency of arsenopyrite was significantly increased in the two-stage oxidation process.After 18 d,the two-stage oxidation process achieved total extraction rates of(88.8±2.0)%,(86.7±1.3)%,and(74.7±3.0)%for As,Fe,and S elements,respectively.These values represented a significant increase of(50.8±3.4)%,(47.1±2.7)%,and(46.0±0.7)%,respectively,compared to the one-stage bio-oxidation process.展开更多
Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving fac...Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.展开更多
BACKGROUND Two-stage revision is the most common treatment for chronic periprosthetic joint infection of the hip,involving a resection arthroplasty with or without placement of an antibiotic-loaded spacer,followed by ...BACKGROUND Two-stage revision is the most common treatment for chronic periprosthetic joint infection of the hip,involving a resection arthroplasty with or without placement of an antibiotic-loaded spacer,followed by antibiotic therapy before reimplantation.AIM To compare the outcomes and complications of two consecutive treatment protocols for two-stage revision arthroplasty of the infected hip:One using Girdlestone with an antibiotic holiday,the other using custom-made articulating spacers(CUMARS)without an antibiotic holiday.METHODS In this retrospective study,two consecutive cohorts were compared.Group A(2017-2020)underwent two-stage revision with a Girdlestone and an antibiotic holiday before reimplantation,while Group B(2020-2023)received CUMARS whenever possible,and no antibiotic holiday,or a Girdlestone if indicated.The primary outcome was successful infection eradication after one year.Secondary outcomes included surgical duration,length of hospital stay,weight-bearing allowance,discharge destination,and complications.RESULTS A total of 98 patients were included:39 patients in Group A and 59 patients in Group B.Successful infection eradication after one year was achieved in 69%of Group A and 83%of Group B(P=0.164).Patients in Group B were more frequently allowed to bear weight(64%vs 18%,P<0.001),had a shorter in-hospital stay(9 vs 16 days,P<0.001),and were more often discharged home after the first surgery(48%vs 24%,P=0.048).No significant differences were found in(mechanical)complications.CONCLUSION A protocol including CUMARS is a safe and effective treatment,offering faster recovery,shorter length of hospital stay,and enabling more patients to return home during the interval.This reduces strain on patients and the healthcare system,potentially saving costs,without compromising infection control or increasing(mechanical)complications.展开更多
The development of efficient and clean heating technologies is crucial for reducing carbon emissions in regions with severe cold regions.This research designs a novel two-stage phase change heat storage coupled solar-...The development of efficient and clean heating technologies is crucial for reducing carbon emissions in regions with severe cold regions.This research designs a novel two-stage phase change heat storage coupled solar-air source heat pump heating system structure that is specifically designed for such regions.The two-stage heat storage device in this heating system expands the storage temperature range of solar heat.The utilization of the two-stage heat storage device not onlymakes up for the instability of the solar heating system,but can also directlymeet the building heating temperature,and can reduce the influence of low-temperature outdoor environments in severe cold regions on the heating performance of the air source heat pump by using solar energy.Therefore,the two-stage phase change heat storage coupled to the solar energy-air source heat pump heating system effectively improves the utilization rate of solar energy.A numerical model of the system components and their integration was developed using TRNSYS software in this study,and various performance aspects of the system were simulated and analyzed.The simulation results demonstrated that the two-stage heat storage device can effectively store solar energy,enabling its hierarchical utilization.The low-temperature solar energy stored by the two-stage phase change heat storage device enhances the coefficient of performance of the air source heat pump by 11.1%in severe cold conditions.Using the Hooke-Jeeves optimization method,the annual cost and carbon emissions are taken as optimization objectives,with the optimized solar heat supply accounting for 52.5%.This study offers valuable insights into operational strategies and site selection for engineering applications,providing a solid theoretical foundation for the widespread implementation of this system in severe cold regions.展开更多
Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing r...Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response;however,many users are not inclined to engage actively;therefore,the full potential of demand response remains unrealized.User satisfaction must be prioritized in demand-response assessments.This study proposed a two-stage,capacity-optimization configuration method for user-level energy systems con-sidering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted,for optimizing device output and load profiles,with a focus on typical,daily,economic,and environmental objectives.The studyfindings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.展开更多
In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integra...In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integrated with phase change material(PCM)thermal storage is proposed.First,a thermal energy management framework is constructed,combining PCM thermal storage with the alkaline electrolyzer(AE)waste heat recovery and the heat pump(HP),while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation.Next,a flue gas treatment method based on purification-separation-storage coordination is adopted,achieving spatiotemporal decoupling between waste incineration and flue gas treatment.Subsequently,a two-stage optimal dispatching strategy for the multi-energy system is developed:the first stage establishes a dayahead economic dispatch model with the objective of minimizing net system costs,while the second stage introduces model predictive control(MPC)to realize intraday rolling optimization.Finally,The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver,followed by a comparative analysis of the optimized operational outcomes.Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit,simultaneously reducing carbon trading costs while increasing electricity sales revenue.The proposed scheduling strategy demonstrates effective grid peak-shaving functionality,thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste(MSW)resource utilization and low-carbon transformation of energy systems.展开更多
The emission regulations for heavy-duty diesel engines regarding nitrogen oxide(NO_(x))are becoming increasingly stringent,particularly in relation to cold start cycles.While the twostage selective catalytic reduction...The emission regulations for heavy-duty diesel engines regarding nitrogen oxide(NO_(x))are becoming increasingly stringent,particularly in relation to cold start cycles.While the twostage selective catalytic reduction(SCR)has the potential to achieve ultra-low NO_(x) emissions,several challenges remain,including the accurate prediction of ammonia(NH_(3))storage mass and the co-control of the two-stage SCR.The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy.Secondly,an initial method for predicting the NH_(3) storage based on the mass conservation law was proposed,which was subsequently improved by filling and emptying experiments.The third step involved the development of a two-stage SCR co-control strategy,including obtaining the steady-state NH_(3) storage target value,dynamic correction for NH_(3) storage target value,regulation of NH_(3) storage,and control of the close-coupled SCR urea injector state.Finally,the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle(WHTC).The results demonstrate that the composite value of engine outlet NO_(x) emissions under cold and hot start WHTC cycles is 13 g/(kW·h).Meanwhile,the composite value of tailpipe NO_(x) emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h),representing only 14%of the EU VI limit value of 0.46 g/(kW·h).Thus,the findings demonstrate that integrating an accurate NH_(3) storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NO_(x) emissions.展开更多
A two-stage source reconstruction algorithm for bioluminescence tomography (BLT) is developed using hybrid finite element method (FEM). The proposed algorithm takes full advantages of linear and quadratic FEMs, which ...A two-stage source reconstruction algorithm for bioluminescence tomography (BLT) is developed using hybrid finite element method (FEM). The proposed algorithm takes full advantages of linear and quadratic FEMs, which can be used to localize and quantify bioluminescent source accurately. In the first stage, a large permissible region is roughly determined and then iteratively evolved to reduce matrix dimension using efficient linear FEM. In the final stage, high-convergence quadratic FEM is applied to improve reconstruction result. Both numerical simulation and physical experiment are performed to evaluate the proposed algorithm. The relevant results demonstrate that quantitative reconstruction can be well achieved in terms of computation efficiency, source position, power density, and total power when compared with previous studies.展开更多
With the increasing requirements of precision mechanical systems in electronic packaging,ultra-precision machining,biomedicine and other high-tech fields,it is necessary to study a precision two-stage amplification mi...With the increasing requirements of precision mechanical systems in electronic packaging,ultra-precision machining,biomedicine and other high-tech fields,it is necessary to study a precision two-stage amplification micro-drive system that can safely provide high precision and a large amplification ratio.In view of the disadvantages of the current two-stage amplification and micro-drive system,such as poor security,low motion accuracy and limited amplification ratio,an optimization design of a precise symmetrical two-stage amplification micro-drive system was completed in this study,and its related performance was studied.Based on the guiding principle of the flexure hinge,a two-stage amplification micro-drive mechanism with no parasitic motion or non-motion direction force was designed.In addition,the structure optimization design of the mechanism was completed using the particle swarm optimization algorithm,which increased the amplification ratio of the mechanism from 5 to 18 times.A precise symmetrical two-stage amplification system was designed using a piezoelectric ceramic actuator and two-stage amplification micro-drive mechanism as the micro-driver and actuator,respectively.The driving,strength,and motion performances of the system were subsequently studied.The results showed that the driving linearity of the system was high,the strength satisfied the design requirements,the motion amplification ratio was high and the motion accuracy was high(relative error was 5.31%).The research in this study can promote the optimization of micro-drive systems.展开更多
基金supported by the State Grid Tianjin Electric Power Company Science and Technology Project (Grant No. KJ22-1-45)。
文摘After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.
基金supported by the National Natural Science Foundation of China(No.62101587)the National Funded Postdoctoral Researcher Program of China(No.GZC20233578)。
文摘Micro-nano Earth Observation Satellite(MEOS)constellation has the advantages of low construction cost,short revisit cycle,and high functional density,which is considered a promising solution for serving rapidly growing observation demands.The observation Scheduling Problem in the MEOS constellation(MEOSSP)is a challenging issue due to the large number of satellites and tasks,as well as complex observation constraints.To address the large-scale and complicated MEOSSP,we develop a Two-Stage Scheduling Algorithm based on the Pointer Network with Attention mechanism(TSSA-PNA).In TSSA-PNA,the MEOS observation scheduling is decomposed into a task allocation stage and a single-MEOS scheduling stage.In the task allocation stage,an adaptive task allocation algorithm with four problem-specific allocation operators is proposed to reallocate the unscheduled tasks to new MEOSs.Regarding the single-MEOS scheduling stage,we design a pointer network based on the encoder-decoder architecture to learn the optimal singleMEOS scheduling solution and introduce the attention mechanism into the encoder to improve the learning efficiency.The Pointer Network with Attention mechanism(PNA)can generate the single-MEOS scheduling solution quickly in an end-to-end manner.These two decomposed stages are performed iteratively to search for the solution with high profit.A greedy local search algorithm is developed to improve the profits further.The performance of the PNA and TSSA-PNA on singleMEOS and multi-MEOS scheduling problems are evaluated in the experiments.The experimental results demonstrate that PNA can obtain the approximate solution for the single-MEOS scheduling problem in a short time.Besides,the TSSA-PNA can achieve higher observation profits than the existing scheduling algorithms within the acceptable computational time for the large-scale MEOS scheduling problem.
基金The National Natural Science Foundation of China(No.62272239,62303214)Jiangsu Agricultural Science and Tech-nology Independent Innovation Fund(No.SJ222051).
文摘To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.
基金supported by National Natural Science Foundation of China (Grant Nos. 60433020, 60175024 and 60773095)European Commission under grant No. TH/Asia Link/010 (111084)the Key Science-Technology Project of the National Education Ministry of China (Grant No. 02090),and the Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, P. R. China
文摘In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.
基金Project(2006AA060201) supported by the National High Technology Research and Development Program of China
文摘A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.
文摘This paper investigates the problem of two-stage extended Kalman filter （TSEKF）-based fault estimation for reaction flywheels in satellite attitude control systems （ACSs）. Firstly, based on the separate-bias principle, a satellite ACSs with actuator fault is transformed into an augmented nonlinear discrete stochastic model; then, a novel TSEKF is suggested such that it can simultane- ously estimate satellite attitude information and actuator faults no matter they are additive or mul- tiplicative; finally, the proposed approach is respectively applied to estimating bias faults and loss of effectiveness for reaction flywheels in satellite ACSs, and simulation results demonstrate the effec- tiveness of the proposed fault estimation approach.
基金partially supported by the National Natural Science Foundation of China(41930644,61972439)the Collaborative Innovation Project of Anhui Province(GXXT-2022-093)the Key Program in the Youth Elite Support Plan in Universities of Anhui Province(gxyqZD2019010)。
文摘Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.
基金This work was supported by the National Natural Science Foundation of China (No. 60574051).
文摘In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.
基金supported by the National Key R&D Program of China(No.2021YFB3400900)the National Natural Science Foundation of China(Nos.52175373,52205435)+1 种基金Natural Science Foundation of Hunan Province,China(No.2022JJ40621)the Innovation Fund of National Commercial Aircraft Manufacturing Engineering Technology Center,China(No.COMACSFGS-2022-1875)。
文摘A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.
基金supported by the National Natural Science Foundation of China under Grant 62473328by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.
基金supported by National Natural Science Foundation of China(Grant No.62266028,62266027,U21B2027,and U24A20334)Major Science and Technology Programs in Yunnan Province(Grant No.202302AD080003,202402AG050007,and 202303AP140008)+1 种基金Yunnan Province Basic Research Program(Grant No.202301AS070047,202301AT070471,and 202401BC070021)Kunming University of Science and Technology's"Double First-rate"construction joint project(Grant No.202201BE070001-021).
文摘Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.
基金Project(52274348)supported by the National Natural Science Foundation of ChinaProject(2022JH1/10400024)supported by the Major Projects for the“Revealed Top”Science and Technology of Liaoning Province,China。
文摘Applying bio-oxidation waste solution(BOS)to chemical-biological two-stage oxidation process can significantly improve the bio-oxidation efficiency of arsenopyrite.This study aims to clarify the enhanced oxidation mechanism of arsenopyrite by evaluating the effects of physical and chemical changes of arsenopyrite in BOS chemical oxidation stage on mineral dissolution kinetics,as well as microbial growth activity and community structure composition in bio-oxidation stage.The results showed that the chemical oxidation contributed to destroying the physical and chemical structure of arsenopyrite surface and reducing the particle size,and led to the formation of nitrogenous substances on mineral surface.These chemical oxidation behaviors effectively promoted Fe^(3+)cycling in the bio-oxidation system and weakened the inhibitory effect of the sulfur film on ionic diffusion,thereby enhancing the dissolution kinetics of the arsenopyrite.Therefore,the bio-oxidation efficiency of arsenopyrite was significantly increased in the two-stage oxidation process.After 18 d,the two-stage oxidation process achieved total extraction rates of(88.8±2.0)%,(86.7±1.3)%,and(74.7±3.0)%for As,Fe,and S elements,respectively.These values represented a significant increase of(50.8±3.4)%,(47.1±2.7)%,and(46.0±0.7)%,respectively,compared to the one-stage bio-oxidation process.
基金the National Natural Science Foundation of China[Grant No.52270183].
文摘Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.
文摘BACKGROUND Two-stage revision is the most common treatment for chronic periprosthetic joint infection of the hip,involving a resection arthroplasty with or without placement of an antibiotic-loaded spacer,followed by antibiotic therapy before reimplantation.AIM To compare the outcomes and complications of two consecutive treatment protocols for two-stage revision arthroplasty of the infected hip:One using Girdlestone with an antibiotic holiday,the other using custom-made articulating spacers(CUMARS)without an antibiotic holiday.METHODS In this retrospective study,two consecutive cohorts were compared.Group A(2017-2020)underwent two-stage revision with a Girdlestone and an antibiotic holiday before reimplantation,while Group B(2020-2023)received CUMARS whenever possible,and no antibiotic holiday,or a Girdlestone if indicated.The primary outcome was successful infection eradication after one year.Secondary outcomes included surgical duration,length of hospital stay,weight-bearing allowance,discharge destination,and complications.RESULTS A total of 98 patients were included:39 patients in Group A and 59 patients in Group B.Successful infection eradication after one year was achieved in 69%of Group A and 83%of Group B(P=0.164).Patients in Group B were more frequently allowed to bear weight(64%vs 18%,P<0.001),had a shorter in-hospital stay(9 vs 16 days,P<0.001),and were more often discharged home after the first surgery(48%vs 24%,P=0.048).No significant differences were found in(mechanical)complications.CONCLUSION A protocol including CUMARS is a safe and effective treatment,offering faster recovery,shorter length of hospital stay,and enabling more patients to return home during the interval.This reduces strain on patients and the healthcare system,potentially saving costs,without compromising infection control or increasing(mechanical)complications.
基金This work was supported by the project of the Research on Energy Consumption of Office Space in Colleges and Universities under the“Dual Carbon Target”(No.CJ202301006).
文摘The development of efficient and clean heating technologies is crucial for reducing carbon emissions in regions with severe cold regions.This research designs a novel two-stage phase change heat storage coupled solar-air source heat pump heating system structure that is specifically designed for such regions.The two-stage heat storage device in this heating system expands the storage temperature range of solar heat.The utilization of the two-stage heat storage device not onlymakes up for the instability of the solar heating system,but can also directlymeet the building heating temperature,and can reduce the influence of low-temperature outdoor environments in severe cold regions on the heating performance of the air source heat pump by using solar energy.Therefore,the two-stage phase change heat storage coupled to the solar energy-air source heat pump heating system effectively improves the utilization rate of solar energy.A numerical model of the system components and their integration was developed using TRNSYS software in this study,and various performance aspects of the system were simulated and analyzed.The simulation results demonstrated that the two-stage heat storage device can effectively store solar energy,enabling its hierarchical utilization.The low-temperature solar energy stored by the two-stage phase change heat storage device enhances the coefficient of performance of the air source heat pump by 11.1%in severe cold conditions.Using the Hooke-Jeeves optimization method,the annual cost and carbon emissions are taken as optimization objectives,with the optimized solar heat supply accounting for 52.5%.This study offers valuable insights into operational strategies and site selection for engineering applications,providing a solid theoretical foundation for the widespread implementation of this system in severe cold regions.
基金supported by the science and technology foundation of Guizhou province[2022]general 013the science and technology foundation of Guizhou province[2022]general 014+1 种基金the science and technology foundation of Guizhou province GCC[2022]016-1the educational technology foundation of Guizhou province[2022]043.
文摘Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response;however,many users are not inclined to engage actively;therefore,the full potential of demand response remains unrealized.User satisfaction must be prioritized in demand-response assessments.This study proposed a two-stage,capacity-optimization configuration method for user-level energy systems con-sidering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted,for optimizing device output and load profiles,with a focus on typical,daily,economic,and environmental objectives.The studyfindings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.
文摘In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integrated with phase change material(PCM)thermal storage is proposed.First,a thermal energy management framework is constructed,combining PCM thermal storage with the alkaline electrolyzer(AE)waste heat recovery and the heat pump(HP),while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation.Next,a flue gas treatment method based on purification-separation-storage coordination is adopted,achieving spatiotemporal decoupling between waste incineration and flue gas treatment.Subsequently,a two-stage optimal dispatching strategy for the multi-energy system is developed:the first stage establishes a dayahead economic dispatch model with the objective of minimizing net system costs,while the second stage introduces model predictive control(MPC)to realize intraday rolling optimization.Finally,The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver,followed by a comparative analysis of the optimized operational outcomes.Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit,simultaneously reducing carbon trading costs while increasing electricity sales revenue.The proposed scheduling strategy demonstrates effective grid peak-shaving functionality,thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste(MSW)resource utilization and low-carbon transformation of energy systems.
基金supported by the National Natural Science Foundation of China(No.51921004).
文摘The emission regulations for heavy-duty diesel engines regarding nitrogen oxide(NO_(x))are becoming increasingly stringent,particularly in relation to cold start cycles.While the twostage selective catalytic reduction(SCR)has the potential to achieve ultra-low NO_(x) emissions,several challenges remain,including the accurate prediction of ammonia(NH_(3))storage mass and the co-control of the two-stage SCR.The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy.Secondly,an initial method for predicting the NH_(3) storage based on the mass conservation law was proposed,which was subsequently improved by filling and emptying experiments.The third step involved the development of a two-stage SCR co-control strategy,including obtaining the steady-state NH_(3) storage target value,dynamic correction for NH_(3) storage target value,regulation of NH_(3) storage,and control of the close-coupled SCR urea injector state.Finally,the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle(WHTC).The results demonstrate that the composite value of engine outlet NO_(x) emissions under cold and hot start WHTC cycles is 13 g/(kW·h).Meanwhile,the composite value of tailpipe NO_(x) emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h),representing only 14%of the EU VI limit value of 0.46 g/(kW·h).Thus,the findings demonstrate that integrating an accurate NH_(3) storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NO_(x) emissions.
基金supported by National Basic Research Program of China (973 Program) (No. 2011CB707702)National Natural Science Foundation of China (Nos. 81090272, 81000632, and 30900334)+1 种基金the Shaanxi Provincial Natural Science Foundation (No. 2009JQ8018)the Fundamental Research Funds for the Central Universities
文摘A two-stage source reconstruction algorithm for bioluminescence tomography (BLT) is developed using hybrid finite element method (FEM). The proposed algorithm takes full advantages of linear and quadratic FEMs, which can be used to localize and quantify bioluminescent source accurately. In the first stage, a large permissible region is roughly determined and then iteratively evolved to reduce matrix dimension using efficient linear FEM. In the final stage, high-convergence quadratic FEM is applied to improve reconstruction result. Both numerical simulation and physical experiment are performed to evaluate the proposed algorithm. The relevant results demonstrate that quantitative reconstruction can be well achieved in terms of computation efficiency, source position, power density, and total power when compared with previous studies.
基金The research was funded by the National Natural Science Foundation of China,No.51805428Innovation Capability Support Plan of Shaanxi Province,No.2021 TD-27.
文摘With the increasing requirements of precision mechanical systems in electronic packaging,ultra-precision machining,biomedicine and other high-tech fields,it is necessary to study a precision two-stage amplification micro-drive system that can safely provide high precision and a large amplification ratio.In view of the disadvantages of the current two-stage amplification and micro-drive system,such as poor security,low motion accuracy and limited amplification ratio,an optimization design of a precise symmetrical two-stage amplification micro-drive system was completed in this study,and its related performance was studied.Based on the guiding principle of the flexure hinge,a two-stage amplification micro-drive mechanism with no parasitic motion or non-motion direction force was designed.In addition,the structure optimization design of the mechanism was completed using the particle swarm optimization algorithm,which increased the amplification ratio of the mechanism from 5 to 18 times.A precise symmetrical two-stage amplification system was designed using a piezoelectric ceramic actuator and two-stage amplification micro-drive mechanism as the micro-driver and actuator,respectively.The driving,strength,and motion performances of the system were subsequently studied.The results showed that the driving linearity of the system was high,the strength satisfied the design requirements,the motion amplification ratio was high and the motion accuracy was high(relative error was 5.31%).The research in this study can promote the optimization of micro-drive systems.
