The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algori...The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalized maximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input–output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input–output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input–output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMC based flow controller and a practical application demonstrates the applicability of the algorithm.展开更多
A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristi...A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely util...Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions.In this work,an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed.Two particles are employed to represent the electrode char-acteristics of the positive and negative electrodes,respectively.Through theoretical derivation,mathematical equations are established to describe various processes within the battery,including solid-phase diffusion,li-quidphase diffusion,reaction polarization,and ohmic polarization.In addition,a method for obtaining model parameters is proposed.Finally,the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions.The identified battery elec-trochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously...Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.展开更多
High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP g...High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica...In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.展开更多
This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loo...This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loop EIV system and its coprime factor(CF)uncertainty description is first derived,based on which the FR measurements suitable for plant CF identification are able to be generated.Different factorizations of a given controller in the closed-loop system can be made best use to adjust right coprime factors(RCFs)of the plant so as to realize an improvement on the signal-to-noise ratio of identification experimental data.Subsequently,a nominal RCF model is estimated by linear matrix inequalities from the applicable FR measurements and its associated worst-case errors are quantified from a priori and a posteriori information on the underlying system.A resulting RCF perturbation model set can then be described by the nominal RCF model and its worst-case error bounds.Such a model set capable of being stabilized by the given controller is ready for its robust stabilizing controller redesign and robust performance analysis.Finally,a numerical simulation is given to show the efficacy of the proposed identification method.展开更多
Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as s...Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.展开更多
What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview wit...What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview with this journal,Professor Shen Weirong ofTsinghua University discusses these issues on the basis of his research.展开更多
The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a nove...The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines,leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms.First,the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems,and examines the voltage characteristics of faulted phases in renewable energy scenarios.The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults,whereas permanent faults do not exhibit such variations.Building on this observation,the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition,wherein low-frequency waveform components are selected to characterize fault features.Subsequently,a fault nature identification method is introduced,based on a voltage waveform time-frequency morphology comparison.By employing a windowing technique to quantify waveform differences before and after arc extinction,this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time.Finally,a 220 kV renewable energy grid connection line model is developed using PSCAD for verification.The results demonstrate that the proposed method is highly adaptable across various fault locations,transition resistances,and renewable energy control strategies,and can reliably identify fault nature in renewable energy grid connection scenarios.展开更多
Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode deco...Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode decomposition performance,which is of great significance in extracting fault features from mechanical signals.However,noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions(IMFs)that obscure signal features.To address these issues,a Precise Identification-based Mode Decomposition(PIMD)method is proposed.This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identifi-cation approach,which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering,thus benefiting the extraction of more mechanical fault features.Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-tonoise ratios and perform a highly rigorous high-low frequency decomposition,significantly outperforming EMD.Finally,mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that,compared to four mainstream methods,the PIMD method exhibits the best mode decomposition perfor-mance and can extract more and clearer mechanical fault features.展开更多
To investigate the impact of temporary structures on the mechanical behavior of shaped bridge towers during the construction process,the Dianbu River Special Bridge was selected as the engineering background.A finite ...To investigate the impact of temporary structures on the mechanical behavior of shaped bridge towers during the construction process,the Dianbu River Special Bridge was selected as the engineering background.A finite element model of the middle tower column during the construction stage was established using ABAQUS to analyze the effects of key parameters,including the angle and pretension of temporary cables,as well as the wall thickness and diameter of temporary diagonal braces.The study examines how these parameters influence the stresses at the towergirder consolidation.The results indicate that the angle of temporary cables significantly affects the tensile stresses at the tower-girder consolidation,while its impact on compressive stresses is minimal.Among all parameters,the pretension of temporary cables has the most pronounced effect on the stresses at the tower-girder consolidation.In contrast,the wall thickness of temporary diagonal braces has only a minor influence,whereas the diameter of temporary diagonal braces has an almost negligible impact.These findings provide valuable insights for optimizing the design and arrangement of temporary support structures in similar bridge construction projects.展开更多
The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi...The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.展开更多
The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained accor...The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained according to the identification procedures in the open-loop, or in the closed-loop. In the open-loop, the identification methods are well known and offer good process approximation, which is not valid for the closed-loop identification, when the system provides the feedback output and doesn’t permit it to be identified in the open-loop. This paper offers an approach for experimental identification in the closed-loop, which supposes the approximation of the process with inertial models, with or without time delay and astatism. The coefficients are calculated based on the values of the critical transfer coefficient and period of the underdamped response of the closed-loop system with P controller, when system achieves the limit of stability. Finally, the closed-loop identification was verified by the computer simulation and the obtained results demonstrated, that the identification procedure in the closed-loop offers good results in process of estimation of the model of the process.展开更多
A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to el...A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.展开更多
Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these appro...Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.展开更多
基金Supported by the National Natural Science Foundation of China(61573052,61174128)
文摘The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalized maximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input–output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input–output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input–output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMC based flow controller and a practical application demonstrates the applicability of the algorithm.
基金国家高技术研究发展计划(863计划)，the National Natural Science Foundation of China，教育部新世纪高校优秀人才计划
文摘A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金Supported by the National Natural Science Foundation of China(Grant Nos.52407238,52177210)the Youth Foundation of Shandong Provincial Natural Science Foundation(Grant No.ZR2023QE036).
文摘Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions.In this work,an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed.Two particles are employed to represent the electrode char-acteristics of the positive and negative electrodes,respectively.Through theoretical derivation,mathematical equations are established to describe various processes within the battery,including solid-phase diffusion,li-quidphase diffusion,reaction polarization,and ohmic polarization.In addition,a method for obtaining model parameters is proposed.Finally,the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions.The identified battery elec-trochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported in part by the National Natural Science Foundation of China (62431018)in part by the Guangzhou Municipal Science and Technology Bureau (SL2023A04J00435)in part by the One Hundred Youth Project of Guangdong University of Technology (263113873)。
文摘Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.
基金supported by the Science and Technology Talent Support Project of Hunan Province,China (2022TJ-N15)the Hunan Agricultural Science and Technology Innovation Fund,China (2024CX90 and 2024CX65)the Science and Technology Innovation Program of Hunan Province,China (2021NK1006)。
文摘High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
基金supported in part by the National Natural Science Foundation of China(62373070 and 52272388)in part by the Chongqing Natural Science Foundation(CSTB2024NSCQ-QCXMX0054,CSTB2022NSCQ-MSX1225 and CSTC2024YCJH-BGZXM0042)in part by the Key Research and Development Project of Anhui Province(202304a05020060).
文摘In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.
文摘This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loop EIV system and its coprime factor(CF)uncertainty description is first derived,based on which the FR measurements suitable for plant CF identification are able to be generated.Different factorizations of a given controller in the closed-loop system can be made best use to adjust right coprime factors(RCFs)of the plant so as to realize an improvement on the signal-to-noise ratio of identification experimental data.Subsequently,a nominal RCF model is estimated by linear matrix inequalities from the applicable FR measurements and its associated worst-case errors are quantified from a priori and a posteriori information on the underlying system.A resulting RCF perturbation model set can then be described by the nominal RCF model and its worst-case error bounds.Such a model set capable of being stabilized by the given controller is ready for its robust stabilizing controller redesign and robust performance analysis.Finally,a numerical simulation is given to show the efficacy of the proposed identification method.
基金supported by Joint Funds of the National Natural Science Foundation of China(Grant No.U21A20228).
文摘Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.
文摘What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview with this journal,Professor Shen Weirong ofTsinghua University discusses these issues on the basis of his research.
基金supported by State Grid Sichuan Electric Power Company science and technology project“Research on Key Technologies for Reclosing of High-Ratio New Energy Grid Connection Lines.”(Program No:52199723002Q).
文摘The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines,leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms.First,the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems,and examines the voltage characteristics of faulted phases in renewable energy scenarios.The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults,whereas permanent faults do not exhibit such variations.Building on this observation,the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition,wherein low-frequency waveform components are selected to characterize fault features.Subsequently,a fault nature identification method is introduced,based on a voltage waveform time-frequency morphology comparison.By employing a windowing technique to quantify waveform differences before and after arc extinction,this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time.Finally,a 220 kV renewable energy grid connection line model is developed using PSCAD for verification.The results demonstrate that the proposed method is highly adaptable across various fault locations,transition resistances,and renewable energy control strategies,and can reliably identify fault nature in renewable energy grid connection scenarios.
基金Supported by National Natural Science Foundation of China(Grant No.52075236)Opening Foundation of Intelligent Manufacturing Technology(Shantou University),Ministry of Education(Grant No.STME2024002)+1 种基金Hundred Doctor and Hundred Enterprise,Science and Technology Project,Ji'an City(Grant No.42064001)Guangdong Provincial University Innovation Team Project(Grant No.2020KCXTD012).
文摘Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode decomposition performance,which is of great significance in extracting fault features from mechanical signals.However,noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions(IMFs)that obscure signal features.To address these issues,a Precise Identification-based Mode Decomposition(PIMD)method is proposed.This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identifi-cation approach,which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering,thus benefiting the extraction of more mechanical fault features.Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-tonoise ratios and perform a highly rigorous high-low frequency decomposition,significantly outperforming EMD.Finally,mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that,compared to four mainstream methods,the PIMD method exhibits the best mode decomposition perfor-mance and can extract more and clearer mechanical fault features.
文摘To investigate the impact of temporary structures on the mechanical behavior of shaped bridge towers during the construction process,the Dianbu River Special Bridge was selected as the engineering background.A finite element model of the middle tower column during the construction stage was established using ABAQUS to analyze the effects of key parameters,including the angle and pretension of temporary cables,as well as the wall thickness and diameter of temporary diagonal braces.The study examines how these parameters influence the stresses at the towergirder consolidation.The results indicate that the angle of temporary cables significantly affects the tensile stresses at the tower-girder consolidation,while its impact on compressive stresses is minimal.Among all parameters,the pretension of temporary cables has the most pronounced effect on the stresses at the tower-girder consolidation.In contrast,the wall thickness of temporary diagonal braces has only a minor influence,whereas the diameter of temporary diagonal braces has an almost negligible impact.These findings provide valuable insights for optimizing the design and arrangement of temporary support structures in similar bridge construction projects.
基金supported by the Program for NIM-Basic Research Business Expenses Key Field Program,China(No.AKYCX2315).
文摘The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.
文摘The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained according to the identification procedures in the open-loop, or in the closed-loop. In the open-loop, the identification methods are well known and offer good process approximation, which is not valid for the closed-loop identification, when the system provides the feedback output and doesn’t permit it to be identified in the open-loop. This paper offers an approach for experimental identification in the closed-loop, which supposes the approximation of the process with inertial models, with or without time delay and astatism. The coefficients are calculated based on the values of the critical transfer coefficient and period of the underdamped response of the closed-loop system with P controller, when system achieves the limit of stability. Finally, the closed-loop identification was verified by the computer simulation and the obtained results demonstrated, that the identification procedure in the closed-loop offers good results in process of estimation of the model of the process.
基金Supported in part by Chinese Recruitment Program of Global Young Expert,Alexander von Humboldt Research Fellowship of Germany,the Foundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China (61074020)
文摘A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.
文摘Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.
