Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through...Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through covalent bonds exhibits excellent structural stability.It has been shown that the stationary phases prepared by combining MOF and COF can make up for the poor stability of MOF@SiO_(2),and the MOF/COF composites have superior chromatographic separation performance.However,the traditional methods for preparing COF/MOF based stationary phases are generally solvent thermal synthesis.In this study,a green and low-cost synthesis method was proposed for the preparation of MOF/COF@SiO_(2) stationary phase.Firstly,COF@SiO_(2) was prepared in a choline chloride/ethylene glycol based deep eutectic solvent(DES).Secondly,another acid-base tunable DES prepared by mixing p-toluenesulfonic acid(PTSA)and 2-methylimidazole in different proportions was introduced as the reaction solvent and reactant for rapid synthesis of MOF/COF@SiO_(2).Compared with the toxic transition metal-based MOFs selected in most previous studies,a lightweight and non-toxic S-zone metal(calcium) based MOF was employed in this study.PTSA and calcium will form the calcium/oxygen-containing organic acid framework in acidic DES,which assembles with terephthalic acid dissolved in basic DES to form MOF.The strong hydrogen bonding effect of DES can facilitate rapid assembly of Ca-MOF.The obtained Ca-MOF/COF@SiO_(2) can be used for multi-mode chromatography to efficiently separate multiple isomeric/hydrophilic/hydrophobic analytes.The synthesis method of Ca-MOF/COF@SiO_(2) is green and mild,especially the use of acid-base tunable DES promotes the rapid synthesis of non-toxic Ca-MOF/COF@silica composites,which offers an innovative approach of greenly synthesizing novel MOF/COF stationary phases and extends their applications in the field of chromatography.展开更多
The flow behavior of molten steel in the thin slab mold under high casting speed conditions was investigated,with a focus on the multi-mode continuous casting and rolling mold.A steel-slag two-phase flow model was est...The flow behavior of molten steel in the thin slab mold under high casting speed conditions was investigated,with a focus on the multi-mode continuous casting and rolling mold.A steel-slag two-phase flow model was established using large eddy simulation,the volume of fluid,and magnetohydrodynamics methods through numerical simulation.The maximum flow velocity and wave height at the steel-slag interface within the mold are critical evaluation criteria for analyzing asymmetric flow under varying casting speeds and electromagnetic braking.The results indicate that the asymmetric flows within the mold do not occur synchronously.The severity of the asymmetric flow correlates with the velocity difference across the steel-slag interface.A greater biased flow prolongs the time required to revert to a steady state.When the magnetic field intensity is set to 0.24 T and the magnetic pole position is at 390 mm from the steel-slag interface,this configuration can reduce the velocity of the steel-slag interface,thereby mitigating the asymmetric flow.Additionally,it can diminish the velocity,impact depth,and impact intensity on the narrow face of the jet,thus improving the distribution of velocity and turbulent kinetic energy within the mold.This configuration prolongs the time required for the steel-slag interface to transition from a stable state to its maximum velocity and shortens the time for the interface to return to stability from an unstable state.Moreover,it ensures the positional stability of the steel-slag interface,confining its position within−3 mm.展开更多
The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy...The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.展开更多
Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature...Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature phosphorescence(RTP)detection in single system due to the formidable synthesis.Herein,a multifunctional composite of Eu&CDs@p RHO has been designed by co-assembly strategy and prepared via a facile calcination and impregnation treatment.Eu&CDs@p RHO exhibits intense fluorescence(FL)and RTP coming from two individual luminous centers,Eu3+in the free pores and CDs in the interrupted structure of RHO zeolite.Unique four-mode color outputs including pink(Eu^(3+),ex.254 nm),light violet(CDs,ex.365 nm),blue(CDs,254 nm off),and green(CDs,365 nm off)could be realized,on the basis of it,a preliminary application of advanced information encoding has been demonstrated.Given the free pores of matrix and stable RTP in water of confined CDs,a visual RTP detection of Fe^(3+)ions is achieved with the detection limit as low as 9.8μmol/L.This work has opened up a new perspective for the strategic amalgamation of luminous vips with porous zeolite to construct the advanced functional materials.展开更多
Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials prov...Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials provide a promising prospect for imaging-guided precision therapy.Considering that tumor-derived alkaline phosphatase(ALP)is over-expressed in metastatic PCa,it makes a great chance to develop a theranostics system with ALP responsive in the TME.Herein,an ALP-responsive aggregationinduced emission luminogens(AIEgens)nanoprobe AMNF self-assembly was designed for enhancing the diagnosis and treatment of metastatic PCa.The nanoprobe exhibited self-aggregation in the presence of ALP resulted in aggregation-induced fluorescence,and enhanced accumulation and prolonged retention period at the tumor site.In terms of detection,the fluorescence(FL)/computed tomography(CT)/magnetic resonance(MR)multi-mode imaging effect of nanoprobe was significantly improved post-aggregation,enabling precise diagnosis through the amalgamation of multiple imaging modes.Enhanced CT/MR imaging can achieve assist preoperative tumor diagnosis,and enhanced FL imaging technology can achieve“intraoperative visual navigation”,showing its potential application value in clinical tumor detection and surgical guidance.In terms of treatment,AMNF showed strong absorption in the near infrared region after aggregation,which improved the photothermal treatment effect.Overall,our work developed an effective aggregation-enhanced theranostic strategy for ALP-related cancers.展开更多
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
A diverse range of light and waves,spanning from near-infrared to ultraviolet,alongside ultrasound,have proven effective in propelling nanomotors.This review encapsulates the advancements in nanomotor research propell...A diverse range of light and waves,spanning from near-infrared to ultraviolet,alongside ultrasound,have proven effective in propelling nanomotors.This review encapsulates the advancements in nanomotor research propelled by waves of varying frequencies.It delves into the driving mechanisms and control methodologies of different nanomotor types,emphasizing the role of frequency.Nanomotors can be classified based on the frequency of the driving wave,encompassing ultraviolet light-driven,visible light-driven,near-infrared-driven,and ultrasounddriven variants.Each category corresponds to distinct propulsion mechanisms,including momentum transfer,photothermal effects,self-electrophoresis,and acoustic radiation force.Notably,visible light and near-infrared radiation predominantly propel momentum transfer nanomotors,while photothermal nanomotors are chiefly active within the infrared spectrum.Ultraviolet light drives most self-electrophoretic nanomotors,while ultrasound-driven nanomotors respond to acoustic radiation force.Furthermore,precise control over nanomotor speed and direction is achievable by adjusting the frequency of incident waves within a narrow range,modulating wave absorption rates.Lastly,this paper explores microwave nanomotors,an area yet to be reported,shedding light on potential driving mechanisms.展开更多
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini...Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.展开更多
In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing method...In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.展开更多
In order to eliminate the meshing interference between the flexspline and circular spline after the taper deformation of the flexspline,the radial deformation difference method,major and minor axis fitting method,and ...In order to eliminate the meshing interference between the flexspline and circular spline after the taper deformation of the flexspline,the radial deformation difference method,major and minor axis fitting method,and ellipse fitting method are used to modify the tooth thickness of the flexspline and analyze the performance indexes such as the assembly stress,transmission error,and fatigue life.Firstly,the conjugate tooth profile is solved based on the quadruple-circular-arc tooth profile and modified kinematic method.Then,based on the finite element radial deformation of the flexspline,the principle and characteristics of three modification methods are analyzed,and the modification amount of each section of the flexspline tooth is calculated.Finally,the influence of the three modification methods on the performance of the harmonic drive is compared.The results show that the radial deformation difference method can initially determine the modification amount.The minimum static assembly stress is 406.22 MPa by the major and minor axis fitting method.The ellipse fitting method has the best dynamic performance,small transmission error fluctuation,a peak-to-peak value of 3.060",and a maximum fatigue life of 10^(7.558)cycles.展开更多
As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and dr...As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and driving mechanisms of such landslides remains relatively limited.To address this knowledge gap,the present study integrated data analysis,field investigations,and remote sensing interpretation to construct a landslide database for the core area of the Qinling Mountains,and systematically analyzed the spatial patterns,development characteristics,and environmental driving factors of shallow landslides.The results reveal that shallow landslides are predominantly small-to-medium in scale,concentrated in regions with an altitude of 800–1000 m and a slope gradient of approximately 30°,with a distinct tendency to develop on sunny(southfacing)slopes.The occurrence frequency of these landslides exhibits a significant positive correlation with the soil moisture content of the weathered layer and the degree of groundwater enrichment in the study area.Specifically,these landslides are mainly developed in bedrock fissure water zones and karst fissure water zones with favorable water-bearing capacity,indicating that rainfall and surface hydrological processes are the key triggering factors for shallow landslides.Notably,vegetation exerts a mediating role in the"vegetation-hydrology-landslide"system:shallow landslides occur most frequently in areas with artificial or shrub-grass vegetation,peaking at a moderate coverage of 50%–60%.This peak suggests that vegetation within this range is ineffective at regulating soil moisture,while the interaction between specific vegetation types and hydrological enrichment further exacerbates landslide risk.By prioritizing the weights of vegetation and hydrological factors,we enhanced the information quantity model,which significantly improved its performance and increased the AUC value to 0.83.These findings confirm the pivotal roles of vegetation and hydrological factors,thereby providing a robust scientific basis for targeted landslide prevention and control in this region.展开更多
Understanding the scale-dependent dynamics of ecosystem services(ESs)and their socio-ecological drivers is essential for sustainable development.While many studies rely on static or single-scale approaches,this resear...Understanding the scale-dependent dynamics of ecosystem services(ESs)and their socio-ecological drivers is essential for sustainable development.While many studies rely on static or single-scale approaches,this research employs an integrated multi-temporal(2000–2020)and multi-scale(grid,county,and landscape levels)framework to investigate China’s Central Asian frontier,a representative dryland region.We quantified six ESs:habitat quality(HQ),net primary productivity(NPP),carbon sequestration(CS),water yield(WY),soil conservation(SC),and grain production(GP).Furthermore,we explored their interrelationships and identified the drivers influencing these services across different spatial scales.Our results revealed divergent ES trajectories:the declining HQ(−0.03 a^(−1)),NPP(−0.43 t km^(−2)a^(−1)),and SC(−3.41 t ha a^(−1))contrasted with rising WY(+2.33 mm a^(−1)),GP(+0.06 t km^(−2)a^(−1)),and CS(+0.02 t km^(−2)a^(−1)).The ES relationships were predominantly synergistic,while HQ–WY exhibited a trade-off(grid:−0.03;county:−0.02;landscape:−0.03)at temporal dimension but a synergistic relationship(grid:0.45;county:0.92;landscape:0.92)at spatial dimension.As spatial scale increased,SC–CS shifted from synergy(grid:0.001)to trade-off(county:−0.01;landscape:−0.005)in the temporal dimension,while all trade-off relationships in the spatial dimension were transformed into synergies.Key drivers of ES relationships varied with spatial scale:fraction vegetation coverage(FVC)and leaf area index(LAI)at the grid scale,annual precipitation(MAP)and soil moisture(SMA)at the county scale,and population density(POP),gross domestic product(GDP),and silt content(Silt)at the landscape scale.Based on the multi-scale findings,the study divides northern Xinjiang into Grain Priority Region,Ecological Priority Region,and Desert Containment Region,and proposes tailored management recommendations,offering a flexible framework for balancing ecological and socioeconomic needs.展开更多
This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematic...This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.展开更多
To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,w...To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination.展开更多
To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative t...To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.展开更多
This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combin...This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.展开更多
Gas Turbine Engines （GTEs） are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gaspath components of a GTE are exposed ...Gas Turbine Engines （GTEs） are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gaspath components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measure- ments and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estima- tion scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System （ANFIS）. To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under （a） diverse ambient conditions and control settings, （b） every possible combination of degradation symptoms, and （c） a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.展开更多
For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to p...For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.展开更多
基金supported by National Natural Science Foundation of China (Nos.21906124,32302202)Natural Science Foundation of Hubei Province (No.2017CFB220)Natural Science Foundation of Shandong Province (No.ZR2023MH278)。
文摘Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through covalent bonds exhibits excellent structural stability.It has been shown that the stationary phases prepared by combining MOF and COF can make up for the poor stability of MOF@SiO_(2),and the MOF/COF composites have superior chromatographic separation performance.However,the traditional methods for preparing COF/MOF based stationary phases are generally solvent thermal synthesis.In this study,a green and low-cost synthesis method was proposed for the preparation of MOF/COF@SiO_(2) stationary phase.Firstly,COF@SiO_(2) was prepared in a choline chloride/ethylene glycol based deep eutectic solvent(DES).Secondly,another acid-base tunable DES prepared by mixing p-toluenesulfonic acid(PTSA)and 2-methylimidazole in different proportions was introduced as the reaction solvent and reactant for rapid synthesis of MOF/COF@SiO_(2).Compared with the toxic transition metal-based MOFs selected in most previous studies,a lightweight and non-toxic S-zone metal(calcium) based MOF was employed in this study.PTSA and calcium will form the calcium/oxygen-containing organic acid framework in acidic DES,which assembles with terephthalic acid dissolved in basic DES to form MOF.The strong hydrogen bonding effect of DES can facilitate rapid assembly of Ca-MOF.The obtained Ca-MOF/COF@SiO_(2) can be used for multi-mode chromatography to efficiently separate multiple isomeric/hydrophilic/hydrophobic analytes.The synthesis method of Ca-MOF/COF@SiO_(2) is green and mild,especially the use of acid-base tunable DES promotes the rapid synthesis of non-toxic Ca-MOF/COF@silica composites,which offers an innovative approach of greenly synthesizing novel MOF/COF stationary phases and extends their applications in the field of chromatography.
基金support from the National Natural Science Foundation of China(Grant Nos.52174313 and 52304350)thank all members of the Hebei High Quality Steel Continuous Casting Engineering Technology Research Center at North China University of Science and Technology,Tangshan,China.
文摘The flow behavior of molten steel in the thin slab mold under high casting speed conditions was investigated,with a focus on the multi-mode continuous casting and rolling mold.A steel-slag two-phase flow model was established using large eddy simulation,the volume of fluid,and magnetohydrodynamics methods through numerical simulation.The maximum flow velocity and wave height at the steel-slag interface within the mold are critical evaluation criteria for analyzing asymmetric flow under varying casting speeds and electromagnetic braking.The results indicate that the asymmetric flows within the mold do not occur synchronously.The severity of the asymmetric flow correlates with the velocity difference across the steel-slag interface.A greater biased flow prolongs the time required to revert to a steady state.When the magnetic field intensity is set to 0.24 T and the magnetic pole position is at 390 mm from the steel-slag interface,this configuration can reduce the velocity of the steel-slag interface,thereby mitigating the asymmetric flow.Additionally,it can diminish the velocity,impact depth,and impact intensity on the narrow face of the jet,thus improving the distribution of velocity and turbulent kinetic energy within the mold.This configuration prolongs the time required for the steel-slag interface to transition from a stable state to its maximum velocity and shortens the time for the interface to return to stability from an unstable state.Moreover,it ensures the positional stability of the steel-slag interface,confining its position within−3 mm.
基金Supported by Hebei Provincial Natural Science Foundation of China(Grant Nos.E2020203174,E2020203078)S&T Program of Hebei Province of China(Grant No.226Z2202G)Science Research Project of Hebei Provincial Education Department of China(Grant No.ZD2022029).
文摘The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.
基金supported by the National Natural Science Foundation of China(No.22288101)the 111 Project(No.B17020)。
文摘Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature phosphorescence(RTP)detection in single system due to the formidable synthesis.Herein,a multifunctional composite of Eu&CDs@p RHO has been designed by co-assembly strategy and prepared via a facile calcination and impregnation treatment.Eu&CDs@p RHO exhibits intense fluorescence(FL)and RTP coming from two individual luminous centers,Eu3+in the free pores and CDs in the interrupted structure of RHO zeolite.Unique four-mode color outputs including pink(Eu^(3+),ex.254 nm),light violet(CDs,ex.365 nm),blue(CDs,254 nm off),and green(CDs,365 nm off)could be realized,on the basis of it,a preliminary application of advanced information encoding has been demonstrated.Given the free pores of matrix and stable RTP in water of confined CDs,a visual RTP detection of Fe^(3+)ions is achieved with the detection limit as low as 9.8μmol/L.This work has opened up a new perspective for the strategic amalgamation of luminous vips with porous zeolite to construct the advanced functional materials.
基金supported by Natural Science Foundation of Jilin Province(No.SKL202302002)Key Research and Development project of Jilin Provincial Science and Technology Department(No.20210204142YY)+2 种基金The Science and Technology Development Program of Jilin Province(No.2020122256JC)Beijing Kechuang Medical Development Foundation Fund of China(No.KC2023-JX-0186BQ079)Talent Reserve Program(TRP),the First Hospital of Jilin University(No.JDYY-TRP-2024007)。
文摘Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials provide a promising prospect for imaging-guided precision therapy.Considering that tumor-derived alkaline phosphatase(ALP)is over-expressed in metastatic PCa,it makes a great chance to develop a theranostics system with ALP responsive in the TME.Herein,an ALP-responsive aggregationinduced emission luminogens(AIEgens)nanoprobe AMNF self-assembly was designed for enhancing the diagnosis and treatment of metastatic PCa.The nanoprobe exhibited self-aggregation in the presence of ALP resulted in aggregation-induced fluorescence,and enhanced accumulation and prolonged retention period at the tumor site.In terms of detection,the fluorescence(FL)/computed tomography(CT)/magnetic resonance(MR)multi-mode imaging effect of nanoprobe was significantly improved post-aggregation,enabling precise diagnosis through the amalgamation of multiple imaging modes.Enhanced CT/MR imaging can achieve assist preoperative tumor diagnosis,and enhanced FL imaging technology can achieve“intraoperative visual navigation”,showing its potential application value in clinical tumor detection and surgical guidance.In terms of treatment,AMNF showed strong absorption in the near infrared region after aggregation,which improved the photothermal treatment effect.Overall,our work developed an effective aggregation-enhanced theranostic strategy for ALP-related cancers.
基金supported by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
基金supported by the National Key Research and Development Program of China(2021YFA1401103)the National Natural Science Foundation of China(52473109,52073071)+3 种基金China Scholarship Council(CSC)(202306790056)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX22_2301)111 Project(B23008)the Innovative Leading Talent Team supported by 2022Wuxi Taihu Talent Program(1096010241230120)。
文摘A diverse range of light and waves,spanning from near-infrared to ultraviolet,alongside ultrasound,have proven effective in propelling nanomotors.This review encapsulates the advancements in nanomotor research propelled by waves of varying frequencies.It delves into the driving mechanisms and control methodologies of different nanomotor types,emphasizing the role of frequency.Nanomotors can be classified based on the frequency of the driving wave,encompassing ultraviolet light-driven,visible light-driven,near-infrared-driven,and ultrasounddriven variants.Each category corresponds to distinct propulsion mechanisms,including momentum transfer,photothermal effects,self-electrophoresis,and acoustic radiation force.Notably,visible light and near-infrared radiation predominantly propel momentum transfer nanomotors,while photothermal nanomotors are chiefly active within the infrared spectrum.Ultraviolet light drives most self-electrophoretic nanomotors,while ultrasound-driven nanomotors respond to acoustic radiation force.Furthermore,precise control over nanomotor speed and direction is achievable by adjusting the frequency of incident waves within a narrow range,modulating wave absorption rates.Lastly,this paper explores microwave nanomotors,an area yet to be reported,shedding light on potential driving mechanisms.
基金funded by the Natural Science Foundation of Chongqing Municipality,grant number CSTB2022NSCQ-MSX0503.
文摘Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
基金funded by“the Fanying Special Program of the National Natural Science Foundation of China,grant number 62341307”“the Scientific research project of Jiangxi Provincial Department of Education,grant number GJJ200839”“theDoctoral startup fund of JiangxiUniversity of Technology,grant number 205200100402”.
文摘In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.
文摘In order to eliminate the meshing interference between the flexspline and circular spline after the taper deformation of the flexspline,the radial deformation difference method,major and minor axis fitting method,and ellipse fitting method are used to modify the tooth thickness of the flexspline and analyze the performance indexes such as the assembly stress,transmission error,and fatigue life.Firstly,the conjugate tooth profile is solved based on the quadruple-circular-arc tooth profile and modified kinematic method.Then,based on the finite element radial deformation of the flexspline,the principle and characteristics of three modification methods are analyzed,and the modification amount of each section of the flexspline tooth is calculated.Finally,the influence of the three modification methods on the performance of the harmonic drive is compared.The results show that the radial deformation difference method can initially determine the modification amount.The minimum static assembly stress is 406.22 MPa by the major and minor axis fitting method.The ellipse fitting method has the best dynamic performance,small transmission error fluctuation,a peak-to-peak value of 3.060",and a maximum fatigue life of 10^(7.558)cycles.
基金supported by the National Key R&D Program of China(No.2024YFF1306502)three Special Programs of the National Natural Science Foundation of China(Nos.42341101,42442045,42307220)the Basic Scientific Research Business Funds of Central Universities(Nos.300102263401,300102265501,300102264103)。
文摘As a critical ecological barrier in China,the Qinling Mountains see their ecological functions significantly impaired by frequent shallow landslides.However,existing research on the distribution characteristics and driving mechanisms of such landslides remains relatively limited.To address this knowledge gap,the present study integrated data analysis,field investigations,and remote sensing interpretation to construct a landslide database for the core area of the Qinling Mountains,and systematically analyzed the spatial patterns,development characteristics,and environmental driving factors of shallow landslides.The results reveal that shallow landslides are predominantly small-to-medium in scale,concentrated in regions with an altitude of 800–1000 m and a slope gradient of approximately 30°,with a distinct tendency to develop on sunny(southfacing)slopes.The occurrence frequency of these landslides exhibits a significant positive correlation with the soil moisture content of the weathered layer and the degree of groundwater enrichment in the study area.Specifically,these landslides are mainly developed in bedrock fissure water zones and karst fissure water zones with favorable water-bearing capacity,indicating that rainfall and surface hydrological processes are the key triggering factors for shallow landslides.Notably,vegetation exerts a mediating role in the"vegetation-hydrology-landslide"system:shallow landslides occur most frequently in areas with artificial or shrub-grass vegetation,peaking at a moderate coverage of 50%–60%.This peak suggests that vegetation within this range is ineffective at regulating soil moisture,while the interaction between specific vegetation types and hydrological enrichment further exacerbates landslide risk.By prioritizing the weights of vegetation and hydrological factors,we enhanced the information quantity model,which significantly improved its performance and increased the AUC value to 0.83.These findings confirm the pivotal roles of vegetation and hydrological factors,thereby providing a robust scientific basis for targeted landslide prevention and control in this region.
基金National Natural Science Foundation of China,No.42377302Ministry of Science and Technology of the People’s Republic of China,No.2022XJKK0904State Key Laboratory of Soil and Sustainable Agriculture,No.SKLSSA25K03。
文摘Understanding the scale-dependent dynamics of ecosystem services(ESs)and their socio-ecological drivers is essential for sustainable development.While many studies rely on static or single-scale approaches,this research employs an integrated multi-temporal(2000–2020)and multi-scale(grid,county,and landscape levels)framework to investigate China’s Central Asian frontier,a representative dryland region.We quantified six ESs:habitat quality(HQ),net primary productivity(NPP),carbon sequestration(CS),water yield(WY),soil conservation(SC),and grain production(GP).Furthermore,we explored their interrelationships and identified the drivers influencing these services across different spatial scales.Our results revealed divergent ES trajectories:the declining HQ(−0.03 a^(−1)),NPP(−0.43 t km^(−2)a^(−1)),and SC(−3.41 t ha a^(−1))contrasted with rising WY(+2.33 mm a^(−1)),GP(+0.06 t km^(−2)a^(−1)),and CS(+0.02 t km^(−2)a^(−1)).The ES relationships were predominantly synergistic,while HQ–WY exhibited a trade-off(grid:−0.03;county:−0.02;landscape:−0.03)at temporal dimension but a synergistic relationship(grid:0.45;county:0.92;landscape:0.92)at spatial dimension.As spatial scale increased,SC–CS shifted from synergy(grid:0.001)to trade-off(county:−0.01;landscape:−0.005)in the temporal dimension,while all trade-off relationships in the spatial dimension were transformed into synergies.Key drivers of ES relationships varied with spatial scale:fraction vegetation coverage(FVC)and leaf area index(LAI)at the grid scale,annual precipitation(MAP)and soil moisture(SMA)at the county scale,and population density(POP),gross domestic product(GDP),and silt content(Silt)at the landscape scale.Based on the multi-scale findings,the study divides northern Xinjiang into Grain Priority Region,Ecological Priority Region,and Desert Containment Region,and proposes tailored management recommendations,offering a flexible framework for balancing ecological and socioeconomic needs.
基金Project(2024YFB3410402)supported by the National Key R&D Program of ChinaProject(52075558)supported by the National Natural Science Foundation of China+2 种基金Project(2021RC3012)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProject(2023CXQD050)supported by the Central South University Innovation-Driven Research Program,ChinaProject(CX20230255)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.
基金supported by the National Natural Science Foundation of China(No.62350048)。
文摘To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination.
文摘To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.
基金The National High Technology Research and Development Program of China (863 Program) (No. 2007AA11Z202)the National Key Technology R & D Program of China during the 11th Five-Year Plan Period(No. 2006BAJ18B03)the Fundamental Research Funds for the Central Universities (No. DUT10RC(3) 112)
文摘This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.
基金financially co-supported by Fond de Recherche Nature et Technologies (FRQNT) from the Quebec government in Canadathe Natural Sciences and Engineering Research Council (NSERC) of CanadaLife Prediction Technologies Inc. (LPTi) in Ottawa, Canada
文摘Gas Turbine Engines （GTEs） are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gaspath components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measure- ments and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estima- tion scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System （ANFIS）. To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under （a） diverse ambient conditions and control settings, （b） every possible combination of degradation symptoms, and （c） a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.
基金supported by the National Natural Science Foundation of China(61371172)the International S&T Cooperation Program of China(2015DFR10220)+1 种基金the Ocean Engineering Project of National Key Laboratory Foundation(1213)the Fundamental Research Funds for the Central Universities(HEUCF1608)
文摘For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.
