Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research w...Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwater managers to achieve sustainable groundwater development at the regional scale.展开更多
Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to ...Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.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
Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identificatio...Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.展开更多
Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate...Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.展开更多
The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemic...The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1∶200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe2O3 and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies.展开更多
With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social rela...With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.展开更多
In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the sy...In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.展开更多
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
Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
Regional stream sediment surveys at a 1:200,000 scale reveal positive and negative regional multi-element geochemical anomalies over medium to large copper-polymetallic orefields of different genetic types in China. R...Regional stream sediment surveys at a 1:200,000 scale reveal positive and negative regional multi-element geochemical anomalies over medium to large copper-polymetallic orefields of different genetic types in China. Regional geochemical anomalies of orefield refer to those geochemical anomalies that are related to metallogenesis of an orefield in a certain area. The anomaly area is typically 10 to 100 km2. The regional multi-element anomalies related to mineralization can be divided into three groups, that is, the ore-element anomaly association, indicator element anomaly association, and metallogenic environmental element anomaly association. Their common spatial distributions over ore deposits or orefields possess unique structures. The model of spatial structure of regional multi-element geochemical anomalies (RAGSS) of an orefield delineates structural feature possessed by orderly spatial distributions of different groups of multi-element anomaly associations related to orefield metallogenesis. It is used to outline the common metallogenetic anomaly visage that is composed of the orderly spatial distribution of different groups of multi-element anomaly associations. The orderly spatial distribution of multi-element anomalies over an orefield reflects element distributions as they are changed from a dispersed 'out-of-order' state into a concentrated 'orderly' state during the mineralization of an orefield. Three different patterns of the spatial anomaly structure related to mineralization in an orefield can be concluded: (1) nested pattern; (2) eccentric pattern and; (3) peripheral pattern. There are marked differences between multi-element anomaly patterns related and not related to mineralization. RAGSS models of orefields can be used to better understand and evaluate regional multi-element anomalies and identify ore types.展开更多
Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when ...Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.展开更多
This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method ...This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.展开更多
The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies...The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized eigenvalue problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer’s confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.展开更多
This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-thresho...This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.展开更多
The model of back-propagation neural network (BPNN) was presented to demonstrate the effect of restrictive ecological factors, COD/SO 4 2- ratio, pH value, alkalinity (ALK) and SO 4 2- loading rate (Ns), on sulfat...The model of back-propagation neural network (BPNN) was presented to demonstrate the effect of restrictive ecological factors, COD/SO 4 2- ratio, pH value, alkalinity (ALK) and SO 4 2- loading rate (Ns), on sulfate reduction of Sulfate Reducing Bacteria (SRB) in an acidogenic sulfate reducing reactor supplied with molasses as sole organic carbon source and sodium sulfate as electron acceptor. The compare of experimental results and computer simulation was also discussed. It was shown that the method of BPNN had a powerful ability to analyze the ecological characteristic of acidogenic sulfate reducing ecosystem quantitatively.展开更多
Sheytoor Iron Ore deposit is located in Yazd province of Iran (Bafq). The most abundant ore is magnetite, which can be seen in the form of mass and granular tissue in various forms of self-shaped, semi-self-shaped and...Sheytoor Iron Ore deposit is located in Yazd province of Iran (Bafq). The most abundant ore is magnetite, which can be seen in the form of mass and granular tissue in various forms of self-shaped, semi-self-shaped and amorphous. The main purpose of this study is to identify the geochemical relationship of phosphorus and sulfur elements and also three-dimensional modeling of mineralization of these elements in iron ore. In order to achieve the research goal, methods such as k-mean clustering technique, concentration-volume fractal as well as block modeling with kriging estimator and Inverse Distance Weighting (IDW) interpolator were used. The model of geochemical behavior of phosphorus and sulfur elements compared to iron is of great importance because these two elements are known as deleterious elements in mineral processing and steelmaking processes, which are the post-mining stages. Existence of geochemical model and identification of elements’ behavior towards each other play a key role in optimizing mining operations in order to achieve geometallurgical goals. The results of this study are the three-dimensional model of mineralization of iron, phosphorus and sulfur elements, separation of phosphorus and sulfur mineralization communities and also presenting the model of enrichment community of these two elements. All the results are in line with geometallurgical studies and can optimize the next steps by optimizing the mining process.展开更多
The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-el...The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.展开更多
A model based damage identification was proposed by facilitating parameter sensitivity analysis and applied to a general overhead travelling crane.As updating reference data,experimental modal frequency was obtained b...A model based damage identification was proposed by facilitating parameter sensitivity analysis and applied to a general overhead travelling crane.As updating reference data,experimental modal frequency was obtained by operational modal analysis(OMA)under ambient excitation.One dimensional damage function was defined to identify the damage by bending stiffness.The results showed that the model updating method could locate the damage and quantitatively describe the structure.The average error of eigenvalues between updated model analysis and the experimental results was less than 4% which proved the accuracy reliable.The comparison of finite element analysis and the test results of the deflection under the capacity load further verified the feasibility of this method.展开更多
This study analyzed the developing environments of hydrocarbon source rocks in the Ordos Basin and evaluated carbonate rocks as hydrocarbon source rocks and their distributions on account of the fact that China’s mar...This study analyzed the developing environments of hydrocarbon source rocks in the Ordos Basin and evaluated carbonate rocks as hydrocarbon source rocks and their distributions on account of the fact that China’s marine carbonate rocks as hydrocarbon source rocks are characterized by the intensive thermal evolution and relatively low abundance of organic matter, by taking the Lower Paleozoic of the Ordos Basin for example and in light of the calculated enrichment coefficients of trace elements, the profile analysis of trace element contents, ratios of relevant elements, and stable isotopic compositions and their three-dimensional diagrammatization in combination with the necessary organic parameters. As for the Ordos Basin, TOC=0.2% is an important boundary value. Studies have shown that in the strata TOC>0.2%, V/(V+Ni)>0.50, Zr/Rb<2, Rb/K(×104)>30, Z>122, Th/U>0.80, Zn and Mo are enriched with a positive δ13Ccarb excursion. All these indicated a stagnant and stratified sedimentary environment that has low energy, anoxia and high salinity in bottom water. In these strata the geological conditions are good for the preservation of organic matter, hence favoring the development of hydrocarbon source rocks. These strata have δ13Corg<-28‰ (Ⅰ- Ⅱ type) and high hydrocarbon-generated potential. The Klimory and Wulalik formations show certain regularities in those aspects, therefore, they can be regarded as the potential effective hydrocarbon source rocks. In the strata TOC≤0.2%, Zr/Rb>1, V/(V+Ni)<0.50, Rb/K<30, Th/U<0.80, Cu, Zn, etc are depleted, and δ13Corg values range from -24‰ to -28‰. All these facts showed that most of the carbonate rocks or mudstones were formed in high-energy oxidizing environments, thus unfavorable to the development of hydrocarbon source rocks. It is feasible to make use of the geochemical method to comprehensively assess the highly evolved marine carbonates rocks as potential hydrocarbon source rocks and their distributions.展开更多
文摘Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwater managers to achieve sustainable groundwater development at the regional scale.
基金This project is supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX22_0124)the National Natural Science Foundation of China(NO.61374153).
文摘Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.
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 by the grants from the Natural Science Foundation of Hubei Province(No.2020CFB780)the Fundamental Research Funds for the Central Universities(No.2017KFYXJJ020).
文摘Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.
文摘Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.
基金Supported by National Key Basic Research Development Planning Project(No.2015CB453005)
文摘The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1∶200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe2O3 and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies.
文摘With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.
文摘In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.
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.
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
文摘Regional stream sediment surveys at a 1:200,000 scale reveal positive and negative regional multi-element geochemical anomalies over medium to large copper-polymetallic orefields of different genetic types in China. Regional geochemical anomalies of orefield refer to those geochemical anomalies that are related to metallogenesis of an orefield in a certain area. The anomaly area is typically 10 to 100 km2. The regional multi-element anomalies related to mineralization can be divided into three groups, that is, the ore-element anomaly association, indicator element anomaly association, and metallogenic environmental element anomaly association. Their common spatial distributions over ore deposits or orefields possess unique structures. The model of spatial structure of regional multi-element geochemical anomalies (RAGSS) of an orefield delineates structural feature possessed by orderly spatial distributions of different groups of multi-element anomaly associations related to orefield metallogenesis. It is used to outline the common metallogenetic anomaly visage that is composed of the orderly spatial distribution of different groups of multi-element anomaly associations. The orderly spatial distribution of multi-element anomalies over an orefield reflects element distributions as they are changed from a dispersed 'out-of-order' state into a concentrated 'orderly' state during the mineralization of an orefield. Three different patterns of the spatial anomaly structure related to mineralization in an orefield can be concluded: (1) nested pattern; (2) eccentric pattern and; (3) peripheral pattern. There are marked differences between multi-element anomaly patterns related and not related to mineralization. RAGSS models of orefields can be used to better understand and evaluate regional multi-element anomalies and identify ore types.
基金supported by the National Nature Science Foundation of China(Grant No.71401052)the National Social Science Foundation of China(Grant No.17BGL156)the Key Project of the National Social Science Foundation of China(Grant No.14AZD024)
文摘Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.
基金Projects(61573052,61273132)supported by the National Natural Science Foundation of China
文摘This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.
文摘The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized eigenvalue problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer’s confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.
基金This work was supported by the National Natural Science Foundation of China(62076025).
文摘This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.
文摘The model of back-propagation neural network (BPNN) was presented to demonstrate the effect of restrictive ecological factors, COD/SO 4 2- ratio, pH value, alkalinity (ALK) and SO 4 2- loading rate (Ns), on sulfate reduction of Sulfate Reducing Bacteria (SRB) in an acidogenic sulfate reducing reactor supplied with molasses as sole organic carbon source and sodium sulfate as electron acceptor. The compare of experimental results and computer simulation was also discussed. It was shown that the method of BPNN had a powerful ability to analyze the ecological characteristic of acidogenic sulfate reducing ecosystem quantitatively.
文摘Sheytoor Iron Ore deposit is located in Yazd province of Iran (Bafq). The most abundant ore is magnetite, which can be seen in the form of mass and granular tissue in various forms of self-shaped, semi-self-shaped and amorphous. The main purpose of this study is to identify the geochemical relationship of phosphorus and sulfur elements and also three-dimensional modeling of mineralization of these elements in iron ore. In order to achieve the research goal, methods such as k-mean clustering technique, concentration-volume fractal as well as block modeling with kriging estimator and Inverse Distance Weighting (IDW) interpolator were used. The model of geochemical behavior of phosphorus and sulfur elements compared to iron is of great importance because these two elements are known as deleterious elements in mineral processing and steelmaking processes, which are the post-mining stages. Existence of geochemical model and identification of elements’ behavior towards each other play a key role in optimizing mining operations in order to achieve geometallurgical goals. The results of this study are the three-dimensional model of mineralization of iron, phosphorus and sulfur elements, separation of phosphorus and sulfur mineralization communities and also presenting the model of enrichment community of these two elements. All the results are in line with geometallurgical studies and can optimize the next steps by optimizing the mining process.
文摘The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.
基金supported by the Research Program of General Administration of Quality Supervision,Inspec-tion and Quarantine of the People's Republic of China(AQSIQ)(No.2014QK182)the Key Laboratory of Risk Identification and Structural Damage Detection Technology for Large Cranes of Jiangsu Province,Donghua Testing Technology Co.,Ltd
文摘A model based damage identification was proposed by facilitating parameter sensitivity analysis and applied to a general overhead travelling crane.As updating reference data,experimental modal frequency was obtained by operational modal analysis(OMA)under ambient excitation.One dimensional damage function was defined to identify the damage by bending stiffness.The results showed that the model updating method could locate the damage and quantitatively describe the structure.The average error of eigenvalues between updated model analysis and the experimental results was less than 4% which proved the accuracy reliable.The comparison of finite element analysis and the test results of the deflection under the capacity load further verified the feasibility of this method.
基金This research project was financially supported jointly by the ResearchProject of the National Key Basic Research Development Program (973Program) of China (Nos. 2001CB209102 & G1999043306), and the KeyResearch Project, SINOPEC (No. P04040).
文摘This study analyzed the developing environments of hydrocarbon source rocks in the Ordos Basin and evaluated carbonate rocks as hydrocarbon source rocks and their distributions on account of the fact that China’s marine carbonate rocks as hydrocarbon source rocks are characterized by the intensive thermal evolution and relatively low abundance of organic matter, by taking the Lower Paleozoic of the Ordos Basin for example and in light of the calculated enrichment coefficients of trace elements, the profile analysis of trace element contents, ratios of relevant elements, and stable isotopic compositions and their three-dimensional diagrammatization in combination with the necessary organic parameters. As for the Ordos Basin, TOC=0.2% is an important boundary value. Studies have shown that in the strata TOC>0.2%, V/(V+Ni)>0.50, Zr/Rb<2, Rb/K(×104)>30, Z>122, Th/U>0.80, Zn and Mo are enriched with a positive δ13Ccarb excursion. All these indicated a stagnant and stratified sedimentary environment that has low energy, anoxia and high salinity in bottom water. In these strata the geological conditions are good for the preservation of organic matter, hence favoring the development of hydrocarbon source rocks. These strata have δ13Corg<-28‰ (Ⅰ- Ⅱ type) and high hydrocarbon-generated potential. The Klimory and Wulalik formations show certain regularities in those aspects, therefore, they can be regarded as the potential effective hydrocarbon source rocks. In the strata TOC≤0.2%, Zr/Rb>1, V/(V+Ni)<0.50, Rb/K<30, Th/U<0.80, Cu, Zn, etc are depleted, and δ13Corg values range from -24‰ to -28‰. All these facts showed that most of the carbonate rocks or mudstones were formed in high-energy oxidizing environments, thus unfavorable to the development of hydrocarbon source rocks. It is feasible to make use of the geochemical method to comprehensively assess the highly evolved marine carbonates rocks as potential hydrocarbon source rocks and their distributions.
