Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identi...Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.展开更多
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
[Objectives]The paper was to establish a TLC identification method for Ensete wilsonii.[Methods]Usingβ-sitosterol as the reference,the effects of preparation methods of test solutions,developing solvents,developing d...[Objectives]The paper was to establish a TLC identification method for Ensete wilsonii.[Methods]Usingβ-sitosterol as the reference,the effects of preparation methods of test solutions,developing solvents,developing distances and color developing agents on TLC analysis were investigated,and the best TLC conditions for E.wilsonii were determined.[Results]The test solution prepared with 90%methanol solvent was dotted on TLC silica gel G plate,and developed with dichloromethane-toluene-methanol=10:5:1.5 as the developing solvent.Then the plate was sprayed with 10%sulfuric acid ethanol solution,and dried with hot blast for color development.Finally,the plate was examined under an ultraviolet lamp at 365 nm.The TLC results of E.wilsonii obtained showed good separation and color development effect,and the spots were clear and characteristic.[Conclusions]This method is safe,specific,and easy to operate,and can be used as a TLC identification method for E.wilsonii.展开更多
Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of...Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.展开更多
Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended perio...Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.展开更多
Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission system.The existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not con...Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission system.The existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider the topology connection of multiple branches for simultaneous identification.(2)Transient bad data is ignored by methods,and the random selection of terminal section data may cause the distortion of PI and have serious consequences.Therefore,a multi-task PI model considering multiple TLs’spatial constraints and massive electrical section data is proposed in this paper.The Graph Attention Network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission network.Multi-Task strategy of Hard Parameter Sharing is used to identify the conductance ofmultiple branches simultaneously.Experiments show that themethod has good accuracy and robustness.Due to the consideration of spatial constraints,the method can also obtain more accurate conductance values under different training and testing conditions.展开更多
The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining...The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.展开更多
[Objectives]This study was conducted to establish simple, efficient, stable, standardized and practical identification methods for sugarcane resistance to white leaf disease(SCWL), and promote the breeding for sugarca...[Objectives]This study was conducted to establish simple, efficient, stable, standardized and practical identification methods for sugarcane resistance to white leaf disease(SCWL), and promote the breeding for sugarcane resistance to SCWL. [Methods]The identification technology of sugarcane resistance to SCWL was systematically studied and explored from the aspects of sugarcane material treatment and planting, inoculation liquid preparation, inoculation method, disease investigation, grading standard formulation, etc., and two sets of simple, efficient, stable, standardized and practical accurate identification methods for sugarcane resistance to SCWL were created for the first time, namely, the seed cane coating inoculation method and the stem-cutting inoculation method at the growth stage. The seed cane coating inoculation method includes the steps of directly screening SCWL phytoplasma, extracting juice from cane and adding 10 times of sterile water to prepare an inoculation liquid, spraying seed cane on plastic film to keep moisture, planting the inoculated materials in barrels in an insect-proof greenhouse for cultivation, investigating the incidence rate 30 d after inoculation, and evaluating the disease resistance according to the 1-5 level standard. The method of stem-cutting inoculation includes the steps of directly screening sugarcane stems carrying SCWL phytoplasma and adding 10 times of sterile water to prepare an inoculation liquid, cultivating the identification materials in an insect-proof greenhouse, dropping 100 μl of the inoculation liquid into each root incision with a pipette gun at the age of 6 months, investigating the incidence rate 20 d after planting, and evaluating the disease resistance according to the 1-5 level standard. [Results] The two methods are similar to the natural transmission method. After inoculation, SCML occurred significantly, with high sensitivity and good reproducibility. The results of resistance identification were consistent with those of natural disease in the field. Through the two inoculation methods and field natural disease investigation, the resistance of 10 main cultivars to SCML was identified, which was true and reliable. [Conclusions] This study can provide standard varieties for identification of SCML resistance in the future.展开更多
For the compound sand body, the interlayer is an important factor affecting the adjustment of oil production structure and remaining oil distribution. According to the origin of argillaceous interlayer, the interlayer...For the compound sand body, the interlayer is an important factor affecting the adjustment of oil production structure and remaining oil distribution. According to the origin of argillaceous interlayer, the interlayer is divided into three types, including barriers between two single layers, intercalations between two single sands and intercalations in a single sand. In this study, the upper limit of physical properties of interlayer was obtained by analyzing the relationship between physical parameters and production index per-meter. The discriminant index and comprehensive discriminant chart of interlayer were obtained by grey correlation method, which realize the quantitative identification of different types of interlayer. The intercalations between two single sands in the target area are distributed almost in the whole area, which is one of the most important factors influencing the mining effect of compound sand, so the planar distribution is mainly aimed at it. Firstly, through cross-well comparison, we summarize three interlayer patterns, then establish their forward modeling, so as to obtain the vertical seismic characteristics of different patterns. Secondly, according to the thickness of intercalations between two single sands, we take the top of bottom sand as the baseline, extract the average amplitude attribute from the upper and lower 3 ms, then, according to the seismic section and planar characteristics of the well, the interlayer structures represented by different seismic section and planar characteristics are summarized. Finally, starting from the real drilling interlayer of the well, the planar spread of interlay can be obtained according to their variation trend and distribution.展开更多
Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration.By combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to imp...Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration.By combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to improve identification accuracy.1D DenseNet is built using standard 1D CNN and DenseNet basic blocks,and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling.When using submodels for damage identification,the voting method ideas in ensemble learning are used to vote on the results of each submodel,and then vote centrally.Finally,the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed method.The results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of accuracy.Furthermore,the submodel is visualized to demonstrate its operation mode.展开更多
This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed alg...This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.展开更多
In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the d...In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.展开更多
Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through trainin...Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through training and optimization algorithms,and a complete apple disease identification system was developed with the model as the core,and evaluated for its performance in terms of accuracy,recall rate and speed.This study provides a reliable AI-based apple disease diagnosis solution for the apple planting industry in the southern Xinjiang,hoping to help farmers better manage and protect crop health.展开更多
This paper presents an identification method of the scalar Preisach model to consider the effect of reversible magnetization in the process of distribution function identification.By reconsidering the identification p...This paper presents an identification method of the scalar Preisach model to consider the effect of reversible magnetization in the process of distribution function identification.By reconsidering the identification process by stripping the influence of reversible components from the measurement data,the Preisach distribution function is identified by the pure irreversible components.In this way,the simulation accuracy of both limiting hysteresis loops and the inner internal symmetrical small hysteresis loop is ensured.Furthermore,through a discrete Preisach plane with a hybrid discretization method,the irreversible magnetic flux density components are computed more efficiently through the improved Preisach model.Finally,the proposed method results are compared with the traditional method and the traditional method considering reversible magnetization and validated by the laboratory test for the B30P105 electrical steel by Epstein frame.展开更多
The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be diff...The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.展开更多
In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can n...In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can not solve an identification problem with infinitely many solutions well.Then we propose PRCs identification based on the minimal norm method,which satisfies observability conditions and has advantages of high computing efficiency and short time consumption.The two identification methods are applied in a water network,and their identification results are compared under the same conditions.From the results,we know that PRCs identification based on the minimal norm method has advantages of higher computing efficiency,shorter time consumption and higher precision.展开更多
This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave loc...This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.展开更多
In this paper,according to the defect of methods which have low identification rate in low SNR,a new individual identification method of radiation source based on information entropy feature and SVM is presented. Firs...In this paper,according to the defect of methods which have low identification rate in low SNR,a new individual identification method of radiation source based on information entropy feature and SVM is presented. Firstly,based on the theory of multi-resolution wavelet analysis,the wavelet power spectrum of noncooperative signal can be gotten. Secondly,according to the information entropy theory,the wavelet power spectrum entropy is defined in this paper. Therefore,the database of signal's wavelet power spectrum entropy can be built in different SNR and signal parameters. Finally,the sorting and identification model based on SVM is built for the individual identification of radiation source signal. The simulation result indicates that this method has a high individual's identification rate in low SNR,when the SNR is greater than 4 dB,the identification rate can reach 100%. Under unstable SNR conditions,when the range of SNR is between 0 dB and 24 dB,the average identification rate is more than 92. 67%. Therefore,this method has a great application value in the complex electromagnetic environment.展开更多
The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty line...The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines.With the goal of achieving“carbon peak and carbon neutrality”,the schemes for clean energy generation have rapidly developed.Moreover,new energy-consuming equipment has been widely connected to the power grid,and the operating characteristics of the power system have significantly changed.Consequently,these have impacted traditional fault identification methods.Based on the time-frequency characteristics of the fault waveform,new energy-related parameters,and deep learning model,this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid.Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters.Further,a fault identification model based on adaptive deep belief networks was constructed,and its effect was verified by field data.展开更多
Enterococcus(E.)faecium is recognized as a leading cause of nosocomial infections worldwide.Infection with the organism is often difficult to treat due to its inherent ability to acquire glycopeptide resistance genes ...Enterococcus(E.)faecium is recognized as a leading cause of nosocomial infections worldwide.Infection with the organism is often difficult to treat due to its inherent ability to acquire glycopeptide resistance genes and other virulence genes[1].展开更多
基金funded by the National Natural Science Foundation of China(41907175)the Open Fund of Key Laboratory(WSRCR-2023-01)the project of the China Geological Survey(DD20230459).
文摘Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.
基金supported by CNPC-CZU Innovation Alliance and supported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology(2022YFC2806403).
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金Supported by Innovation Project of Guangxi Graduate Education of GXUCM(YCSY2022012)High-level Innovation Team and Outstanding Scholars Program of Universities and Colleges in Guangxi(GJR[2014]07)Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica(20-065-38).
文摘[Objectives]The paper was to establish a TLC identification method for Ensete wilsonii.[Methods]Usingβ-sitosterol as the reference,the effects of preparation methods of test solutions,developing solvents,developing distances and color developing agents on TLC analysis were investigated,and the best TLC conditions for E.wilsonii were determined.[Results]The test solution prepared with 90%methanol solvent was dotted on TLC silica gel G plate,and developed with dichloromethane-toluene-methanol=10:5:1.5 as the developing solvent.Then the plate was sprayed with 10%sulfuric acid ethanol solution,and dried with hot blast for color development.Finally,the plate was examined under an ultraviolet lamp at 365 nm.The TLC results of E.wilsonii obtained showed good separation and color development effect,and the spots were clear and characteristic.[Conclusions]This method is safe,specific,and easy to operate,and can be used as a TLC identification method for E.wilsonii.
文摘Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.
基金financially supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No. LH2020E016)the National Natural Science Foundation of China (Grant No.11472076)。
文摘Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.
基金supported by the National Natural Science Foundation of PR China(42075130)the Postgraduate Research and Innovation Project of Jiangsu Province(1534052101133).
文摘Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission system.The existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider the topology connection of multiple branches for simultaneous identification.(2)Transient bad data is ignored by methods,and the random selection of terminal section data may cause the distortion of PI and have serious consequences.Therefore,a multi-task PI model considering multiple TLs’spatial constraints and massive electrical section data is proposed in this paper.The Graph Attention Network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission network.Multi-Task strategy of Hard Parameter Sharing is used to identify the conductance ofmultiple branches simultaneously.Experiments show that themethod has good accuracy and robustness.Due to the consideration of spatial constraints,the method can also obtain more accurate conductance values under different training and testing conditions.
基金support received from the National Natural Science Foundation of China(Grant No.62073206)Technical Innovation Guidance Project of Shaanxi Province(Grant No.2020CGHJ-007).
文摘The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.
基金Supported by National Natural Science Foundation of China (31760504)China Agriculture Research System of MOF and MARA(CARS-170303)+1 种基金Yunling Industry and Technology Leading Talent Training Program (2018LJRC56)Special Fund for the Construction of Modern Agricultural Industry Technology System in Yunnan Province。
文摘[Objectives]This study was conducted to establish simple, efficient, stable, standardized and practical identification methods for sugarcane resistance to white leaf disease(SCWL), and promote the breeding for sugarcane resistance to SCWL. [Methods]The identification technology of sugarcane resistance to SCWL was systematically studied and explored from the aspects of sugarcane material treatment and planting, inoculation liquid preparation, inoculation method, disease investigation, grading standard formulation, etc., and two sets of simple, efficient, stable, standardized and practical accurate identification methods for sugarcane resistance to SCWL were created for the first time, namely, the seed cane coating inoculation method and the stem-cutting inoculation method at the growth stage. The seed cane coating inoculation method includes the steps of directly screening SCWL phytoplasma, extracting juice from cane and adding 10 times of sterile water to prepare an inoculation liquid, spraying seed cane on plastic film to keep moisture, planting the inoculated materials in barrels in an insect-proof greenhouse for cultivation, investigating the incidence rate 30 d after inoculation, and evaluating the disease resistance according to the 1-5 level standard. The method of stem-cutting inoculation includes the steps of directly screening sugarcane stems carrying SCWL phytoplasma and adding 10 times of sterile water to prepare an inoculation liquid, cultivating the identification materials in an insect-proof greenhouse, dropping 100 μl of the inoculation liquid into each root incision with a pipette gun at the age of 6 months, investigating the incidence rate 20 d after planting, and evaluating the disease resistance according to the 1-5 level standard. [Results] The two methods are similar to the natural transmission method. After inoculation, SCML occurred significantly, with high sensitivity and good reproducibility. The results of resistance identification were consistent with those of natural disease in the field. Through the two inoculation methods and field natural disease investigation, the resistance of 10 main cultivars to SCML was identified, which was true and reliable. [Conclusions] This study can provide standard varieties for identification of SCML resistance in the future.
文摘For the compound sand body, the interlayer is an important factor affecting the adjustment of oil production structure and remaining oil distribution. According to the origin of argillaceous interlayer, the interlayer is divided into three types, including barriers between two single layers, intercalations between two single sands and intercalations in a single sand. In this study, the upper limit of physical properties of interlayer was obtained by analyzing the relationship between physical parameters and production index per-meter. The discriminant index and comprehensive discriminant chart of interlayer were obtained by grey correlation method, which realize the quantitative identification of different types of interlayer. The intercalations between two single sands in the target area are distributed almost in the whole area, which is one of the most important factors influencing the mining effect of compound sand, so the planar distribution is mainly aimed at it. Firstly, through cross-well comparison, we summarize three interlayer patterns, then establish their forward modeling, so as to obtain the vertical seismic characteristics of different patterns. Secondly, according to the thickness of intercalations between two single sands, we take the top of bottom sand as the baseline, extract the average amplitude attribute from the upper and lower 3 ms, then, according to the seismic section and planar characteristics of the well, the interlayer structures represented by different seismic section and planar characteristics are summarized. Finally, starting from the real drilling interlayer of the well, the planar spread of interlay can be obtained according to their variation trend and distribution.
文摘Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration.By combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to improve identification accuracy.1D DenseNet is built using standard 1D CNN and DenseNet basic blocks,and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling.When using submodels for damage identification,the voting method ideas in ensemble learning are used to vote on the results of each submodel,and then vote centrally.Finally,the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed method.The results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of accuracy.Furthermore,the submodel is visualized to demonstrate its operation mode.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
文摘In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.
基金Supported by Bingtuan Science and Technology Program(2021DB0012021BB023)Science and Technology Innovation Team of Tarim University(TDZKCX202102).
文摘Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through training and optimization algorithms,and a complete apple disease identification system was developed with the model as the core,and evaluated for its performance in terms of accuracy,recall rate and speed.This study provides a reliable AI-based apple disease diagnosis solution for the apple planting industry in the southern Xinjiang,hoping to help farmers better manage and protect crop health.
基金supported by the National Natural Science Foundation of China under Grant 52007102,52207012by the State Key Laboratory of Reliability and Intelligence of Electrical Equipment under Grant EERIKF2021015。
文摘This paper presents an identification method of the scalar Preisach model to consider the effect of reversible magnetization in the process of distribution function identification.By reconsidering the identification process by stripping the influence of reversible components from the measurement data,the Preisach distribution function is identified by the pure irreversible components.In this way,the simulation accuracy of both limiting hysteresis loops and the inner internal symmetrical small hysteresis loop is ensured.Furthermore,through a discrete Preisach plane with a hybrid discretization method,the irreversible magnetic flux density components are computed more efficiently through the improved Preisach model.Finally,the proposed method results are compared with the traditional method and the traditional method considering reversible magnetization and validated by the laboratory test for the B30P105 electrical steel by Epstein frame.
基金supported by the National Natural Science Foundation of China(Grant No.61961019)the Youth Key Project of the Natural Science Foundation of Jiangxi Province of China(Grant No.20202ACBL212003).
文摘The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.
基金Sponsored by the National"Eleventh-five"Tackle Key Problem Program-China Science and Technology Support Project(Grant No.2006BAJ01A04)
文摘In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can not solve an identification problem with infinitely many solutions well.Then we propose PRCs identification based on the minimal norm method,which satisfies observability conditions and has advantages of high computing efficiency and short time consumption.The two identification methods are applied in a water network,and their identification results are compared under the same conditions.From the results,we know that PRCs identification based on the minimal norm method has advantages of higher computing efficiency,shorter time consumption and higher precision.
文摘This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.
基金Sponsored by the Nation Nature Science Foundation of China(Grant No.61201237,61301095)the Nature Science Foundation of Heilongjiang Province of China(Grant No.QC2012C069)the Fundamental Research Funds for the Central Universities(Grant No.HEUCFZ1129,HEUCF130817,HEUCF130810)
文摘In this paper,according to the defect of methods which have low identification rate in low SNR,a new individual identification method of radiation source based on information entropy feature and SVM is presented. Firstly,based on the theory of multi-resolution wavelet analysis,the wavelet power spectrum of noncooperative signal can be gotten. Secondly,according to the information entropy theory,the wavelet power spectrum entropy is defined in this paper. Therefore,the database of signal's wavelet power spectrum entropy can be built in different SNR and signal parameters. Finally,the sorting and identification model based on SVM is built for the individual identification of radiation source signal. The simulation result indicates that this method has a high individual's identification rate in low SNR,when the SNR is greater than 4 dB,the identification rate can reach 100%. Under unstable SNR conditions,when the range of SNR is between 0 dB and 24 dB,the average identification rate is more than 92. 67%. Therefore,this method has a great application value in the complex electromagnetic environment.
基金This work was supported by State Grid Science and Technology Project(B3440821K003).
文摘The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines.With the goal of achieving“carbon peak and carbon neutrality”,the schemes for clean energy generation have rapidly developed.Moreover,new energy-consuming equipment has been widely connected to the power grid,and the operating characteristics of the power system have significantly changed.Consequently,these have impacted traditional fault identification methods.Based on the time-frequency characteristics of the fault waveform,new energy-related parameters,and deep learning model,this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid.Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters.Further,a fault identification model based on adaptive deep belief networks was constructed,and its effect was verified by field data.
基金supported in parts by the research grants from the University of Malaya,Malaysia,under the Research University grant(RU002-2019)the UMCoE Top 100 Research Grant(UM.00000188/HGA.GV)
文摘Enterococcus(E.)faecium is recognized as a leading cause of nosocomial infections worldwide.Infection with the organism is often difficult to treat due to its inherent ability to acquire glycopeptide resistance genes and other virulence genes[1].
