Correction:Financ Innov 10,43(2024)https://doi.org/10.1186/s40854-023-00578-z.Following publication of the original article(Amirteimoori et al.2024),the authors reported a typesetting error in the affiliation of autho...Correction:Financ Innov 10,43(2024)https://doi.org/10.1186/s40854-023-00578-z.Following publication of the original article(Amirteimoori et al.2024),the authors reported a typesetting error in the affiliation of author Tofigh Allahviranloo.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
Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding pro...Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.展开更多
Accurately estimating the battery state of health(SOH)is essential for ensuring the safe and reliable operation of battery systems of electric vehicles.However,due to the complex and variable operating conditions enco...Accurately estimating the battery state of health(SOH)is essential for ensuring the safe and reliable operation of battery systems of electric vehicles.However,due to the complex and variable operating conditions encountered in practical applications,achieving precise and physics-informed SOH estimation remains challenging.To address these problems,this paper develops a lightweight two-stage physicsinformed neural network(TSPINN)method for SOH estimation of lithium-ion batteries with different chemistries.Specifically,this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model(ECM).Additionally,incremental capacity(IC)feature is extracted by analyzing peak values of the IC curve during the charging phase,which thereby constitutes the first stage of the proposed TSPINN,termed as physics-informed data augmentation for SOH estimation.Additionally,the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function,and ultimately,the second stage of the proposed TSPINN is developed,which is named the physics-informed loss function.The effectiveness of the TSPINN method was confirmed through the experimental data for LiNi_(0.86)Co_(0.11)Al_(0.03)O_(2)(NCA)and LiNi_(0.83)Co_(0.11)Mn_(0.07)O_(2)(NCM)battery materials under different temperature conditions.The final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error(MAE)of 0.675%,showing improvements of approximately 29.3%,60.3%,and 8.1% compared to methods using only ECM,IC,and integrated features,respectively.The results validate the effectiveness and adaptability of TSPINN,establishing it as a reliable solution for advanced battery management systems.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
Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the ...Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.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
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
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
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
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
Operating efficiency of universities is widely concerned by the education community.As a non-parametric method for efficiently handling multiple inputs and outputs,data envelopment analysis(DEA)is often used for measu...Operating efficiency of universities is widely concerned by the education community.As a non-parametric method for efficiently handling multiple inputs and outputs,data envelopment analysis(DEA)is often used for measuring the operating efficiency.However,shared input resources are often ignored in the existing DEA studies.In order to remedy the shortcoming with a focus on teaching and research processes of universities,this paper adopts an extended two-stage network DEA approach to measure the operating efficiency of 52 universities in China using a data set in 2014.The main findings show that:(1)Among the operating efficiency of 52 universities,about one third and two thirds of universities are efficient and inefficient,respectively.It may reflect some problems such as inefficient use of resources or unsatisfactory outcomes for these inefficient universities.By giving first priority to universities’teaching or research process,we provide alternative ways for teaching-oriented or research-oriented universities to benchmark and improve their performance.(2)For the heterogeneity efficiency analysis of different universities,the operating efficiency of“non-985”universities are significantly higher than that of“985”universities,while there is only a small difference on the operating efficiency between comprehensive universities and science&engineering universities.Although the efficiency of the central and western universities is slightly better than that of the eastern universities in terms of the average efficiency,there is no significant efficiency difference among the eastern,central,and western regions statistically.Hence,to improve the operating efficiency of Chinese universities,the Chinese government should improve the financial allocation mechanism and introduce successful budget performance management.For the Chinese universities,they should formulate teaching and scientific research plans according to their own research needs and development goals.展开更多
This paper aims to propose a new approach to decompose an overall data envelopment analysis model into equivalent two-stage models.In this approach,we use a minimax reference point method to set the weights and reliab...This paper aims to propose a new approach to decompose an overall data envelopment analysis model into equivalent two-stage models.In this approach,we use a minimax reference point method to set the weights and reliabilities of the two stage models so that the combined efficiency of the two stages is equal to the overall efficiency.The equivalent multi-stage models are useful to support planning for performance improvement.An illustrative example is first explored to compare the results from the new approach with those of four other existing approaches.The main finding from the comparisons is that the new decomposition approach of this paper satisfies the proposed assumptions.A case study is then conducted on a two-stage process of steel manufacturing to illustrate the validity and applicability of the proposed approach.展开更多
This paper proposes an innovative procedure for designing efficient biomass-biofuel logistics networks(BBLNs).This procedure is based on the two-stage network data envelopment analysis(TSN-DEA)models that have been de...This paper proposes an innovative procedure for designing efficient biomass-biofuel logistics networks(BBLNs).This procedure is based on the two-stage network data envelopment analysis(TSN-DEA)models that have been developed to provide specific process guidance for the managers to improve the efficiency of the decision-making unit(DMU)with the TSN process.The crucial issue of the TSN-DEA is that the overall efficiency score depends on the DMUs under evaluation.Thus,the rankings for the DMUs generated by the TSN-DEA model are inconsistent.As a result,the TSN-DEA-based ranking methods are limited.The TSN-DEA’s inconsistency frequently makes it difficult for decision-makers to select the top-rated DMUs.We develop the transformed TSN(T-TSN)DEA method by applying the multi-criteria DEA model to overcome this issue.The proposed method transforms the DMUs with any number of inputs,intermediate measures,and outputs in the TSN process,through the multi-objective programming model with a minimax objective approach,into the DMUs with two inputs and one output in the single-stage network(SSN)process.Then,the well-known DEA methods for the SSN,such as the cross-efficiency and super-efficiency DEA methods,can be applied to evaluate and rank the transformed DMUs more consistently.We exhibit the applicability of the proposed approach for the BBLN design problem.A case study of South Carolina in the USA demonstrates that the proposed method performs well in identifying efficient BBLN schemes more consistently than the traditional TSN-DEA.展开更多
The biotransformation of food waste(FW)to bioenergy has attracted considerable research attention as a means to address the energy crisis and waste disposal problems.To this end,a promising technique is two-stage anae...The biotransformation of food waste(FW)to bioenergy has attracted considerable research attention as a means to address the energy crisis and waste disposal problems.To this end,a promising technique is two-stage anaerobic digestion(TSAD),in which the FW is transformed to biohythane,a gaseous mixture of biomethane and biohydrogen.This review summarises the main characteristics of FW and describes the basic principle of TSAD.Moreover,the factors influencing the TSAD performance are identified,and an overview of the research status;economic aspects;and strategies such as pre-treatment,co-digestion,and regulation of microbial consortia to increase the biohythane yield from TSAD is provided.Additionally,the challenges and future considerations associated with the treatment of FW by TSAD are highlighted.This paper can provide valuable reference for the improvement and widespread implementation of TSAD-based FW treatment.展开更多
文摘Correction:Financ Innov 10,43(2024)https://doi.org/10.1186/s40854-023-00578-z.Following publication of the original article(Amirteimoori et al.2024),the authors reported a typesetting error in the affiliation of author Tofigh Allahviranloo.
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.12064027)。
文摘Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.
基金supported by the Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652B005)Anhui Province Applied Peak Discipline Mechanical Engineering(No.XK-XJGF004)。
文摘Accurately estimating the battery state of health(SOH)is essential for ensuring the safe and reliable operation of battery systems of electric vehicles.However,due to the complex and variable operating conditions encountered in practical applications,achieving precise and physics-informed SOH estimation remains challenging.To address these problems,this paper develops a lightweight two-stage physicsinformed neural network(TSPINN)method for SOH estimation of lithium-ion batteries with different chemistries.Specifically,this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model(ECM).Additionally,incremental capacity(IC)feature is extracted by analyzing peak values of the IC curve during the charging phase,which thereby constitutes the first stage of the proposed TSPINN,termed as physics-informed data augmentation for SOH estimation.Additionally,the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function,and ultimately,the second stage of the proposed TSPINN is developed,which is named the physics-informed loss function.The effectiveness of the TSPINN method was confirmed through the experimental data for LiNi_(0.86)Co_(0.11)Al_(0.03)O_(2)(NCA)and LiNi_(0.83)Co_(0.11)Mn_(0.07)O_(2)(NCM)battery materials under different temperature conditions.The final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error(MAE)of 0.675%,showing improvements of approximately 29.3%,60.3%,and 8.1% compared to methods using only ECM,IC,and integrated features,respectively.The results validate the effectiveness and adaptability of TSPINN,establishing it as a reliable solution for advanced battery management systems.
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 North China Electric Power Research Institute’s Self-Funded Science and Technology Project“Research on Distributed Energy Storage Optimal Configuration and Operation Control Technology for Photovoltaic Promotion in the Entire County”(KJZ2022049).
文摘Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.
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.
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.
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.
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Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
基金This research was supported by National Natural Science Foundation of China under Grants(Nos.71601064,72071067,71801067,71871081)the Major Project of the National Social Science Foundation of China(No.18ZDA064).
文摘Operating efficiency of universities is widely concerned by the education community.As a non-parametric method for efficiently handling multiple inputs and outputs,data envelopment analysis(DEA)is often used for measuring the operating efficiency.However,shared input resources are often ignored in the existing DEA studies.In order to remedy the shortcoming with a focus on teaching and research processes of universities,this paper adopts an extended two-stage network DEA approach to measure the operating efficiency of 52 universities in China using a data set in 2014.The main findings show that:(1)Among the operating efficiency of 52 universities,about one third and two thirds of universities are efficient and inefficient,respectively.It may reflect some problems such as inefficient use of resources or unsatisfactory outcomes for these inefficient universities.By giving first priority to universities’teaching or research process,we provide alternative ways for teaching-oriented or research-oriented universities to benchmark and improve their performance.(2)For the heterogeneity efficiency analysis of different universities,the operating efficiency of“non-985”universities are significantly higher than that of“985”universities,while there is only a small difference on the operating efficiency between comprehensive universities and science&engineering universities.Although the efficiency of the central and western universities is slightly better than that of the eastern universities in terms of the average efficiency,there is no significant efficiency difference among the eastern,central,and western regions statistically.Hence,to improve the operating efficiency of Chinese universities,the Chinese government should improve the financial allocation mechanism and introduce successful budget performance management.For the Chinese universities,they should formulate teaching and scientific research plans according to their own research needs and development goals.
基金the supports from National Natural Science Foundation of China(NSFC No.71671181)China Scholarship Council(CSC No.201304910099)+1 种基金supported by the European Commission under the grant No.EC-GPF-314836the US Air Force Office of Scientific Research under the Grant No.FA2386-15-1-5004.
文摘This paper aims to propose a new approach to decompose an overall data envelopment analysis model into equivalent two-stage models.In this approach,we use a minimax reference point method to set the weights and reliabilities of the two stage models so that the combined efficiency of the two stages is equal to the overall efficiency.The equivalent multi-stage models are useful to support planning for performance improvement.An illustrative example is first explored to compare the results from the new approach with those of four other existing approaches.The main finding from the comparisons is that the new decomposition approach of this paper satisfies the proposed assumptions.A case study is then conducted on a two-stage process of steel manufacturing to illustrate the validity and applicability of the proposed approach.
文摘This paper proposes an innovative procedure for designing efficient biomass-biofuel logistics networks(BBLNs).This procedure is based on the two-stage network data envelopment analysis(TSN-DEA)models that have been developed to provide specific process guidance for the managers to improve the efficiency of the decision-making unit(DMU)with the TSN process.The crucial issue of the TSN-DEA is that the overall efficiency score depends on the DMUs under evaluation.Thus,the rankings for the DMUs generated by the TSN-DEA model are inconsistent.As a result,the TSN-DEA-based ranking methods are limited.The TSN-DEA’s inconsistency frequently makes it difficult for decision-makers to select the top-rated DMUs.We develop the transformed TSN(T-TSN)DEA method by applying the multi-criteria DEA model to overcome this issue.The proposed method transforms the DMUs with any number of inputs,intermediate measures,and outputs in the TSN process,through the multi-objective programming model with a minimax objective approach,into the DMUs with two inputs and one output in the single-stage network(SSN)process.Then,the well-known DEA methods for the SSN,such as the cross-efficiency and super-efficiency DEA methods,can be applied to evaluate and rank the transformed DMUs more consistently.We exhibit the applicability of the proposed approach for the BBLN design problem.A case study of South Carolina in the USA demonstrates that the proposed method performs well in identifying efficient BBLN schemes more consistently than the traditional TSN-DEA.
基金supported by the National Natural Science Foundation of China(Nos.51978498 and 52131002)the National Key R&D Program of China(No.2019YFC1906301)。
文摘The biotransformation of food waste(FW)to bioenergy has attracted considerable research attention as a means to address the energy crisis and waste disposal problems.To this end,a promising technique is two-stage anaerobic digestion(TSAD),in which the FW is transformed to biohythane,a gaseous mixture of biomethane and biohydrogen.This review summarises the main characteristics of FW and describes the basic principle of TSAD.Moreover,the factors influencing the TSAD performance are identified,and an overview of the research status;economic aspects;and strategies such as pre-treatment,co-digestion,and regulation of microbial consortia to increase the biohythane yield from TSAD is provided.Additionally,the challenges and future considerations associated with the treatment of FW by TSAD are highlighted.This paper can provide valuable reference for the improvement and widespread implementation of TSAD-based FW treatment.
