Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to ...In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.展开更多
In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrabilit...In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.展开更多
Cover cropping is a diversifying agricultural practice that can improve soil structure and function by altering the underground litter diversity and soil microbial communities.Here,we tested how a wheat cover crop alt...Cover cropping is a diversifying agricultural practice that can improve soil structure and function by altering the underground litter diversity and soil microbial communities.Here,we tested how a wheat cover crop alters the decomposition of cucumber root litter.A three-year greenhouse litterbag decomposition experiment showed that a wheat cover crop accelerates the decomposition of cucumber root litter.A microcosm litterbag experiment further showed that wheat litter and the soil microbial community could improve cucumber root litter decomposition.Moreover,the wheat cover crop altered the abundances and diversities of soil bacterial and fungal communities,and enriched several putative keystone operational taxonomic units(OTUs),such as Bacillus sp.OTU1837 and Mortierella sp.OTU1236,that were positively related to the mass loss of cucumber root litter.The representative bacterial and fungal strains B186 and M3 were isolated and cultured.In vitro decomposition tests demonstrated that both B186 and M3 had cucumber root litter decomposition activity and a stronger effect was found when they were co-incubated.Overall,a wheat cover crop accelerated cucumber root litter decomposition by altering the soil microbial communities,particularly by stimulating certain putative keystone taxa,which provides a theoretical basis for using cover crops to promote sustainable agricultural development.展开更多
In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based bou...In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.展开更多
A novel gappy technology, gappy autoencoder with proper orthogonal decomposition(Gappy POD-AE), is proposed for reconstructing physical fields from sparse data. High-dimensional data are reduced via proper orthogonal ...A novel gappy technology, gappy autoencoder with proper orthogonal decomposition(Gappy POD-AE), is proposed for reconstructing physical fields from sparse data. High-dimensional data are reduced via proper orthogonal decomposition(POD),and low-dimensional data are used to train an autoencoder(AE). By integrating the POD operator with the decoder, a nonlinear solution form is established and incorporated into a new maximum-a-posteriori(MAP)-based objective for online reconstruction.The numerical results on the two-dimensional(2D) Bhatnagar-Gross-Krook-Boltzmann(BGK-Boltzmann) equation, wave equation, shallow-water equation, and satellite data show that Gappy POD-AE achieves higher accuracy than gappy proper orthogonal decomposition(Gappy POD), especially for the data with slowly decaying singular values,and is more efficient in training than gappy autoencoder(Gappy AE). The MAP-based formulation and new gappy procedure further enhance the reconstruction accuracy.展开更多
The precise characterization of hypersonic glide vehicle(HGV) maneuver laws in complex flight scenarios still faces challenges. Non-stationary changes in flight state due to abrupt changes in maneuver modes place high...The precise characterization of hypersonic glide vehicle(HGV) maneuver laws in complex flight scenarios still faces challenges. Non-stationary changes in flight state due to abrupt changes in maneuver modes place high demands on the accuracy of modeling methods. To address this issue, a novel maneuver laws modeling and analysis method based on higher order multi-resolution dynamic mode decomposition(HMDMD) is proposed in this work. A joint time-space-frequency decomposition of the vehicle's state sequence in the complex flight scenario is achieved with the higher order Koopman assumption and standard multi-resolution dynamic mode decomposition, and an approximate dynamic model is established. The maneuver laws can be reconstructed and analyzed with extracted multi-scale spatiotemporal modes with clear physical meaning. Based on the dynamic model of HGV, two flight scenarios are established with constant angle of attack and complex maneuver laws, respectively. Simulation results demonstrate that the maneuver laws obtained using the HMDMD method are highly consistent with those derived from the real dynamic model, the modeling accuracy is better than other common modeling methods, and the method has strong interpretability.展开更多
Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the phy...Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the physical array aperture,azimuth ambiguity occurs,making range-azimuth imaging on a moving platform challenging.To address this issue,we theoretically analyze azimuth ambiguity generation in sparse motion arrays and propose a dual-aperture adaptive processing(DAAP)method for suppressing azimuth ambiguity.This method combines spatial multiple-input multiple-output(MIMO)arrays with sparse motion arrays to achieve high-resolution range-azimuth imaging.In addition,an adaptive QR decomposition denoising method for sparse array signals based on iterative low-rank matrix approximation(LRMA)and regularized QR is proposed to preprocess sparse motion array signals.Simulations and experiments show that on a two-transmitter-four-receiver array,the signal-to-noise ratio(SNR)of the sparse motion array signal after noise suppression via adaptive QR decomposition can exceed 0 dB,and the azimuth ambiguity signal ratio(AASR)can be reduced to below-20 dB.展开更多
Straw return can effectively improve farmland soil microenvironment and fertility.However,excessive straw in the topsoil adversely affects seed germination and crop growth.At present,the characteristics and key drivin...Straw return can effectively improve farmland soil microenvironment and fertility.However,excessive straw in the topsoil adversely affects seed germination and crop growth.At present,the characteristics and key driving factors of straw decomposition in dry farmlands are unclear.Based on the interactions between tillage practices including zero tillage(ZT),chisel tillage(CT),and plow tillage(PT)and nitrogen(N)fertilization,i.e.,low N(N1,180 kg ha^(-1)),normal N(N2,240 kg ha^(-1)),and high N(N3,300 kg ha^(-1)),quantitative polymerase chain reaction technology and an enzymatic detection kit were used to investigate the effects of key straw C-degrading enzyme activities and microbial abundance in soil on maize straw decomposition during the growth period of winter wheat in the winter wheat/summer maize double cropping system in a dry farmland of the Loess Plateau,China.Between 2018 and 2020,ZT and CT significantly increased winter wheat yield(by 10.94%and 12.79%,respectively)and straw decomposition velocity(by 20%and 26.67%,respectively),compared with PT.Compared to N1 and N3,N2 significantly increased wheat yield(by 4.65%and 5.31%,respectively)and straw decomposition velocity(by 26.33%and 13.21%,respectively).The partial least squares pathway modelling showed significant positive direct effects of soil moisture,NO3-,NH4+,total N,bacteria,and cellulase,laccase,and xylanase activities on straw decomposition,while soil pH,fungi,and Actinomycetes had significant negative direct effects.Overall,conservation tillage(ZT and CT)combined with N2 was beneficial for straw decomposition in the drylands of the Loess Plateau and improved straw resource utilization and basic soil fertility.The results of the study clarified the key drivers of straw decomposition in dry farmlands and provided new ideas for developing updated soil management practices and adaptive N application strategies to promote the resource utilization of straw and achieve the goals of carbon peaking and carbon neutrality.展开更多
The chemical boundaries inside the ultrafine spinodal decomposition structure in metastable β-Ti alloys can act as a new feature to architect heterogeneous microstructures.In this work,we combined two semi-empirical ...The chemical boundaries inside the ultrafine spinodal decomposition structure in metastable β-Ti alloys can act as a new feature to architect heterogeneous microstructures.In this work,we combined two semi-empirical methods,i.e.,the d-electron theory and the e/a electron concentration,to achieve the spinodal decomposition structure in a metastable β Ti-4.5Al-4.5Mo-7V-1.5Cr-1.5Zr(wt.%)alloy.Utilizing the spinodal decomposition structure,the aged Ti-Al-Mo-V-Cr-Zr alloys showed multi-architectured α precipitates spanning from micron-scale(primary α_(p))to nano-scale(secondary α_(s))that were uniformly distributed in the β-domains.Being compared with the forged sample,the multi-scale heterogeneous microstructure enables the aged β-Ti alloy to have ultra-high strength(yield strength ~1366 MPa and ultimate tensile strength ~1424 MPa)and an appreciable ductility(~9.3%).Strengthening models were proposed for the present alloys to estimate the contribution of various microstructural features to the measured yield strength.While the solid solution strengthening,β-spinodal strengthening,and back stress strengthening made comparable contributions to the strength of the forged alloy,the back stress strengthening was the predominant strengthening effect in the aged alloy.This alloy design approach based on chemical boundary engineering to construct multi-architectured α precipitates provided an effective strategy for achieving an outstanding combination of ultra-high strength and ductility in metastable β-Ti alloys.展开更多
Body-centered cubic Ti-Zr-Nb-Ta-Mo multi-principal element alloys(MPEAs),boasting a yield strength ex-ceeding one gigapascal,emerge as promising candidates for demanding structural applications.However,their limited t...Body-centered cubic Ti-Zr-Nb-Ta-Mo multi-principal element alloys(MPEAs),boasting a yield strength ex-ceeding one gigapascal,emerge as promising candidates for demanding structural applications.However,their limited tensile ductility at room temperature presents a significant challenge to their processability and large-scale implementation.This study identifies phase decomposition as a critical factor influencing the plasticity of these alloys.The microscale phase decomposition in these MPEAs during solidification,driven by miscibility gaps,manifests as dendritic structures within grains.Closer examination reveals that the MPEAs with a pronounced thermodynamic propensity for phase decomposition are also suscep-tible to analogous phenomena at the atomic level.The atomic phase decomposition is characterized by the localized aggregation of some elements across nanometric domains,culminating in the establishment of short-range orderings(SROs).It is observed that phase decomposition for these MPEAs,occurring at both microscale and atomic scale,adheres to thermodynamic principles and can be predicted using the CALPHAD approach.The impact of phase decomposition on the plasticity of MPEAs fundamentally stems from the induced heterogeneities at three distinct levels:(1)Fluctuations in mechanical properties at the micron scale;(2)Variations in the strain field at the atomic scale;(3)Bond polarization and bond index fluctuations at the electronic scale.Consequently,the key to designing high-strength and high-plasticity MPEAs lies in maximizing lattice distortion while simultaneously minimizing the adverse effects of phase decomposition on the alloy’s plasticity(grain boundary cohesion).This research not only clarifies the mechanisms underpinning the ductile-to-brittle transition in high-strength Ti-Zr-Nb-Ta-Mo MPEAs but also offers crucial guidelines for developing advanced,high-performance alloys.展开更多
A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.展开更多
Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communicati...Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.展开更多
The dominant plant litter plays a crucial role in carbon(C)and nutrients cycling as well as ecosystem functions maintenance on the Qinghai-Tibet Plateau(QTP).The impact of litter decomposition of dominant plants on ed...The dominant plant litter plays a crucial role in carbon(C)and nutrients cycling as well as ecosystem functions maintenance on the Qinghai-Tibet Plateau(QTP).The impact of litter decomposition of dominant plants on edaphic parameters and grassland productivity has been extensively studied,while its decomposition processes and relevant mechanisms in this area remain poorly understood.We conducted a three-year litter decomposition experiment in the Gansu Gannan Grassland Ecosystem National Observation and Research Station,an alpine meadow ecosystem on the QTP,to investigate changes in litter enzyme activities and bacterial and fungal communities,and clarify how these critical factors regulated the decomposition of dominant plant Elymus nutans(E.nutans)litter.The results showed that cellulose and hemicellulose,which accounted for 95%of the initial lignocellulose content,were the main components in E.nutans litter decomposition.The litter enzyme activities ofβ-1,4-glucosidase(BG),β-1,4-xylosidase(BX),andβ-D-cellobiosidase(CBH)decreased with decomposition while acid phosphatase,leucine aminopeptidase,and phenol oxidase increased with decomposition.We found that both litter bacterial and fungal communities changed significantly with decomposition.Furthermore,bacterial communities shifted from copiotrophic-dominated to oligotrophic-dominated in the late stage of litter decomposition.Partial least squares path model revealed that the decomposition of E.nutans litter was mainly driven by bacterial communities and their secreted enzymes.Bacteroidota and Proteobacteria were important producers of enzymes BG,BX,and CBH,and their relative abundances were tightly positively related to the content of cellulose and hemicellulose,indicating that Bacteroidota and Proteobacteria are the main bacterial taxa of the decomposition of E.nutans litter.In conclusion,this study demonstrates that bacterial communities are the main driving forces behind the decomposition of E.nutans litter,highlighting the vital roles of bacterial communities in affecting the ecosystem functions of the QTP by regulating dominant plant litter decomposition.展开更多
Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenario...Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.展开更多
Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of int...Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of intrinsic image structure.A novel approach is proposed to address these is-sues.Firstly,a chaotic sequence is generated using the Lorenz three-dimensional chaotic mapping to initiate the encryption process,which is XORed with each spectral band of the multispectral image to complete the initial encryption of the image.Then,a two-dimensional lifting 9/7 wavelet transform is applied to the processed image.Next,a key-sensitive Arnold scrambling technique is employed on the resulting low-frequency image.It effectively eliminates spatial redundancy in the multispectral image while enhancing the encryption process.To optimize the compression and encryption processes further,fast Tucker decomposition is applied to the wavelet sub-band tensor.It effectively removes both spectral redundancy and residual spatial redundancy in the multispectral image.Finally,the core tensor and pattern matrix obtained from the decomposition are subjected to entropy encoding,and real-time chaotic encryption is implemented during the encoding process,effectively integrating compression and encryption.The results show that the proposed algorithm is suitable for occasions with high requirements for compression and encryption,and it provides valuable insights for the de-velopment of compression and encryption in multispectral field.展开更多
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the National Natural Science Foundation of China(Nos.52470114 and 52022104)the National Key R&D Program of China(No.2022YFC3702802)the Youth Innovation Promotion Association,CAS(No.Y2021020).
文摘In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.
基金sponsored by the National Natural Science Foundations of China under Grant Nos.12301315,12235007,11975131the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ20A010009。
文摘In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.
基金supported by the National Natural Science Foundation of China(32072655 and 32272792)。
文摘Cover cropping is a diversifying agricultural practice that can improve soil structure and function by altering the underground litter diversity and soil microbial communities.Here,we tested how a wheat cover crop alters the decomposition of cucumber root litter.A three-year greenhouse litterbag decomposition experiment showed that a wheat cover crop accelerates the decomposition of cucumber root litter.A microcosm litterbag experiment further showed that wheat litter and the soil microbial community could improve cucumber root litter decomposition.Moreover,the wheat cover crop altered the abundances and diversities of soil bacterial and fungal communities,and enriched several putative keystone operational taxonomic units(OTUs),such as Bacillus sp.OTU1837 and Mortierella sp.OTU1236,that were positively related to the mass loss of cucumber root litter.The representative bacterial and fungal strains B186 and M3 were isolated and cultured.In vitro decomposition tests demonstrated that both B186 and M3 had cucumber root litter decomposition activity and a stronger effect was found when they were co-incubated.Overall,a wheat cover crop accelerated cucumber root litter decomposition by altering the soil microbial communities,particularly by stimulating certain putative keystone taxa,which provides a theoretical basis for using cover crops to promote sustainable agricultural development.
文摘In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.
基金supported by the National Natural Science Foundation of China(No.12472197)。
文摘A novel gappy technology, gappy autoencoder with proper orthogonal decomposition(Gappy POD-AE), is proposed for reconstructing physical fields from sparse data. High-dimensional data are reduced via proper orthogonal decomposition(POD),and low-dimensional data are used to train an autoencoder(AE). By integrating the POD operator with the decoder, a nonlinear solution form is established and incorporated into a new maximum-a-posteriori(MAP)-based objective for online reconstruction.The numerical results on the two-dimensional(2D) Bhatnagar-Gross-Krook-Boltzmann(BGK-Boltzmann) equation, wave equation, shallow-water equation, and satellite data show that Gappy POD-AE achieves higher accuracy than gappy proper orthogonal decomposition(Gappy POD), especially for the data with slowly decaying singular values,and is more efficient in training than gappy autoencoder(Gappy AE). The MAP-based formulation and new gappy procedure further enhance the reconstruction accuracy.
基金supported by the National Natural Science Foundation of China (Grant No. 12302056)the Postdoctoral Fellowship Program of CPSF:GZC20233445。
文摘The precise characterization of hypersonic glide vehicle(HGV) maneuver laws in complex flight scenarios still faces challenges. Non-stationary changes in flight state due to abrupt changes in maneuver modes place high demands on the accuracy of modeling methods. To address this issue, a novel maneuver laws modeling and analysis method based on higher order multi-resolution dynamic mode decomposition(HMDMD) is proposed in this work. A joint time-space-frequency decomposition of the vehicle's state sequence in the complex flight scenario is achieved with the higher order Koopman assumption and standard multi-resolution dynamic mode decomposition, and an approximate dynamic model is established. The maneuver laws can be reconstructed and analyzed with extracted multi-scale spatiotemporal modes with clear physical meaning. Based on the dynamic model of HGV, two flight scenarios are established with constant angle of attack and complex maneuver laws, respectively. Simulation results demonstrate that the maneuver laws obtained using the HMDMD method are highly consistent with those derived from the real dynamic model, the modeling accuracy is better than other common modeling methods, and the method has strong interpretability.
基金supported by the National Natural Science Foundation of China under Grant 62301051.
文摘Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the physical array aperture,azimuth ambiguity occurs,making range-azimuth imaging on a moving platform challenging.To address this issue,we theoretically analyze azimuth ambiguity generation in sparse motion arrays and propose a dual-aperture adaptive processing(DAAP)method for suppressing azimuth ambiguity.This method combines spatial multiple-input multiple-output(MIMO)arrays with sparse motion arrays to achieve high-resolution range-azimuth imaging.In addition,an adaptive QR decomposition denoising method for sparse array signals based on iterative low-rank matrix approximation(LRMA)and regularized QR is proposed to preprocess sparse motion array signals.Simulations and experiments show that on a two-transmitter-four-receiver array,the signal-to-noise ratio(SNR)of the sparse motion array signal after noise suppression via adaptive QR decomposition can exceed 0 dB,and the azimuth ambiguity signal ratio(AASR)can be reduced to below-20 dB.
基金supported by the National Natural Science Foundation of China(No.31971860).
文摘Straw return can effectively improve farmland soil microenvironment and fertility.However,excessive straw in the topsoil adversely affects seed germination and crop growth.At present,the characteristics and key driving factors of straw decomposition in dry farmlands are unclear.Based on the interactions between tillage practices including zero tillage(ZT),chisel tillage(CT),and plow tillage(PT)and nitrogen(N)fertilization,i.e.,low N(N1,180 kg ha^(-1)),normal N(N2,240 kg ha^(-1)),and high N(N3,300 kg ha^(-1)),quantitative polymerase chain reaction technology and an enzymatic detection kit were used to investigate the effects of key straw C-degrading enzyme activities and microbial abundance in soil on maize straw decomposition during the growth period of winter wheat in the winter wheat/summer maize double cropping system in a dry farmland of the Loess Plateau,China.Between 2018 and 2020,ZT and CT significantly increased winter wheat yield(by 10.94%and 12.79%,respectively)and straw decomposition velocity(by 20%and 26.67%,respectively),compared with PT.Compared to N1 and N3,N2 significantly increased wheat yield(by 4.65%and 5.31%,respectively)and straw decomposition velocity(by 26.33%and 13.21%,respectively).The partial least squares pathway modelling showed significant positive direct effects of soil moisture,NO3-,NH4+,total N,bacteria,and cellulase,laccase,and xylanase activities on straw decomposition,while soil pH,fungi,and Actinomycetes had significant negative direct effects.Overall,conservation tillage(ZT and CT)combined with N2 was beneficial for straw decomposition in the drylands of the Loess Plateau and improved straw resource utilization and basic soil fertility.The results of the study clarified the key drivers of straw decomposition in dry farmlands and provided new ideas for developing updated soil management practices and adaptive N application strategies to promote the resource utilization of straw and achieve the goals of carbon peaking and carbon neutrality.
基金supported by the National Natural Science Foundation of China(Grant Nos.92163201 and U2067219)Shaanxi Province Youth Innovation Team Project(No.22JP042)+1 种基金Shaanxi Province Innovation Team Project(No.2024RS-CXTD-58)the Fundamental Research Funds for the Central Universities(No.xtr022019004).
文摘The chemical boundaries inside the ultrafine spinodal decomposition structure in metastable β-Ti alloys can act as a new feature to architect heterogeneous microstructures.In this work,we combined two semi-empirical methods,i.e.,the d-electron theory and the e/a electron concentration,to achieve the spinodal decomposition structure in a metastable β Ti-4.5Al-4.5Mo-7V-1.5Cr-1.5Zr(wt.%)alloy.Utilizing the spinodal decomposition structure,the aged Ti-Al-Mo-V-Cr-Zr alloys showed multi-architectured α precipitates spanning from micron-scale(primary α_(p))to nano-scale(secondary α_(s))that were uniformly distributed in the β-domains.Being compared with the forged sample,the multi-scale heterogeneous microstructure enables the aged β-Ti alloy to have ultra-high strength(yield strength ~1366 MPa and ultimate tensile strength ~1424 MPa)and an appreciable ductility(~9.3%).Strengthening models were proposed for the present alloys to estimate the contribution of various microstructural features to the measured yield strength.While the solid solution strengthening,β-spinodal strengthening,and back stress strengthening made comparable contributions to the strength of the forged alloy,the back stress strengthening was the predominant strengthening effect in the aged alloy.This alloy design approach based on chemical boundary engineering to construct multi-architectured α precipitates provided an effective strategy for achieving an outstanding combination of ultra-high strength and ductility in metastable β-Ti alloys.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(Nos.2022A1515220040,2023A1515220021,and 2024A1515012353)the China Postdoctoral Science Foundation(No.2023M741370)+2 种基金the National Natural Sci-ence Foundation of China(No.52005217)the University Re-search Platform and Research Projects of Guangdong Education De-partment(No.2022ZDZX3003)The first-principles research is also supported by the Dongguan AIPU Technology Company Limited.
文摘Body-centered cubic Ti-Zr-Nb-Ta-Mo multi-principal element alloys(MPEAs),boasting a yield strength ex-ceeding one gigapascal,emerge as promising candidates for demanding structural applications.However,their limited tensile ductility at room temperature presents a significant challenge to their processability and large-scale implementation.This study identifies phase decomposition as a critical factor influencing the plasticity of these alloys.The microscale phase decomposition in these MPEAs during solidification,driven by miscibility gaps,manifests as dendritic structures within grains.Closer examination reveals that the MPEAs with a pronounced thermodynamic propensity for phase decomposition are also suscep-tible to analogous phenomena at the atomic level.The atomic phase decomposition is characterized by the localized aggregation of some elements across nanometric domains,culminating in the establishment of short-range orderings(SROs).It is observed that phase decomposition for these MPEAs,occurring at both microscale and atomic scale,adheres to thermodynamic principles and can be predicted using the CALPHAD approach.The impact of phase decomposition on the plasticity of MPEAs fundamentally stems from the induced heterogeneities at three distinct levels:(1)Fluctuations in mechanical properties at the micron scale;(2)Variations in the strain field at the atomic scale;(3)Bond polarization and bond index fluctuations at the electronic scale.Consequently,the key to designing high-strength and high-plasticity MPEAs lies in maximizing lattice distortion while simultaneously minimizing the adverse effects of phase decomposition on the alloy’s plasticity(grain boundary cohesion).This research not only clarifies the mechanisms underpinning the ductile-to-brittle transition in high-strength Ti-Zr-Nb-Ta-Mo MPEAs but also offers crucial guidelines for developing advanced,high-performance alloys.
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
基金supported in part by the National Natural Science Foundation of China (62376288,U23A20347)the Engineering and Physical Sciences Research Council of UK (EP/X041239/1)the Royal Society International Exchanges Scheme of UK (IEC/NSFC/211404)。
文摘Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.
基金funded by the National Natural Science Foundation of China(31870435)the European Union's Marie Sklodowska-Curie Action Postdoctoral Fellowship(101061660)the China Scholarship Council(202106180060).
文摘The dominant plant litter plays a crucial role in carbon(C)and nutrients cycling as well as ecosystem functions maintenance on the Qinghai-Tibet Plateau(QTP).The impact of litter decomposition of dominant plants on edaphic parameters and grassland productivity has been extensively studied,while its decomposition processes and relevant mechanisms in this area remain poorly understood.We conducted a three-year litter decomposition experiment in the Gansu Gannan Grassland Ecosystem National Observation and Research Station,an alpine meadow ecosystem on the QTP,to investigate changes in litter enzyme activities and bacterial and fungal communities,and clarify how these critical factors regulated the decomposition of dominant plant Elymus nutans(E.nutans)litter.The results showed that cellulose and hemicellulose,which accounted for 95%of the initial lignocellulose content,were the main components in E.nutans litter decomposition.The litter enzyme activities ofβ-1,4-glucosidase(BG),β-1,4-xylosidase(BX),andβ-D-cellobiosidase(CBH)decreased with decomposition while acid phosphatase,leucine aminopeptidase,and phenol oxidase increased with decomposition.We found that both litter bacterial and fungal communities changed significantly with decomposition.Furthermore,bacterial communities shifted from copiotrophic-dominated to oligotrophic-dominated in the late stage of litter decomposition.Partial least squares path model revealed that the decomposition of E.nutans litter was mainly driven by bacterial communities and their secreted enzymes.Bacteroidota and Proteobacteria were important producers of enzymes BG,BX,and CBH,and their relative abundances were tightly positively related to the content of cellulose and hemicellulose,indicating that Bacteroidota and Proteobacteria are the main bacterial taxa of the decomposition of E.nutans litter.In conclusion,this study demonstrates that bacterial communities are the main driving forces behind the decomposition of E.nutans litter,highlighting the vital roles of bacterial communities in affecting the ecosystem functions of the QTP by regulating dominant plant litter decomposition.
基金the Science and Technology Project of State Grid Corporation of China,Grant Number 5108-202304065A-1-1-ZN.
文摘Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.
基金the National Natural Science Foundation of China(No.11803036)Climbing Program of Changchun University(No.ZKP202114).
文摘Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of intrinsic image structure.A novel approach is proposed to address these is-sues.Firstly,a chaotic sequence is generated using the Lorenz three-dimensional chaotic mapping to initiate the encryption process,which is XORed with each spectral band of the multispectral image to complete the initial encryption of the image.Then,a two-dimensional lifting 9/7 wavelet transform is applied to the processed image.Next,a key-sensitive Arnold scrambling technique is employed on the resulting low-frequency image.It effectively eliminates spatial redundancy in the multispectral image while enhancing the encryption process.To optimize the compression and encryption processes further,fast Tucker decomposition is applied to the wavelet sub-band tensor.It effectively removes both spectral redundancy and residual spatial redundancy in the multispectral image.Finally,the core tensor and pattern matrix obtained from the decomposition are subjected to entropy encoding,and real-time chaotic encryption is implemented during the encoding process,effectively integrating compression and encryption.The results show that the proposed algorithm is suitable for occasions with high requirements for compression and encryption,and it provides valuable insights for the de-velopment of compression and encryption in multispectral field.
