Thin-layer Identification,Extraction Process and Content Determination of Chlorogenic Acid in Laggera alata(D.Don)Sch.Bip.ex Oliv.

[Objectives]This paper aims to establish thin-layer identification and content determination method for Laggera alata(D.Don)Sch.Bip.ex Oliv.with chlorogenic acid as the index component and compare the content of chlorogenic acid in L.alata from different places in Guangxi.[Methods]Silica gel GF254 thin-layer plate was used for identification under an ultraviolet lamp(365 nm),with butyl acetate-formic acid-water(V∶V∶V=7∶2.5∶2.5)as a developing agent.The content of chlorogenic acid was determined under the following chromatographic conditions:column,Inertsil ODS-3 C18 column(4.60 mm×250 mm,5μm);mobile phase,methanol-0.1%phosphoric acid(28∶72);detection wavelength,329 nm;flow rate,1.0 mL/min;column temperature,25℃;and injection volume,10μL.[Results]Chlorogenic acid can be detected by thin layer chromatography with clear spot and good specificity.Chlorogenic acid showed a good linear relationship in the injection amount range of 0.099-0.99μg(R^(2)=0.9999).The content of chlorogenic acid in L.alata varied greatly among the 10 different producing areas in Guangxi.L.alata produced in Dee Township,Longlin,Baise,Guangxi showed the highest chlorogenic acid content,and that produced in Shangsi County and Pingle County showed the lowest chlorogenic acid content.[Conclusions]This method can effectively identify L.alata and accurately determine the content of chlorogenic acid,thereby providing a scientific basis for the development and utilization of L.alata resources.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Pollution source identification methods and remediation technologies of groundwater: A review

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.

Characteristics and main controlling factors of intra-platform shoal thin-layer dolomite reservoirs:A case study of Middle Permian Qixia Formation in Gaoshiti-Moxi area of Sichuan Basin,SW China

Based on the study of the distribution of intra-platform shoals and the characteristics of dolomite reservoirs in the Middle Permian Qixia Formation in the Gaoshiti–Moxi area of the Sichuan Basin,SW China,the controlling factors of reservoir development were analyzed,and the formation model of“intra-platform shoal thin-layer dolomite reservoir”was established.The Qixia Formation is a regressive cycle from bottom to top,in which the first member(Qi1 Member)develops low-energy open sea microfacies,and the second member(Qi2 Member)evolves into intra-platform shoal and inter-shoal sea with decreases in sea level.The intra-platform shoal is mainly distributed near the top of two secondary shallowing cycles of the Qi2 Member.The most important reservoir rock of the Qixia Formation is thin-layer fractured-vuggy dolomite,followed by vuggy dolomite.The semi-filled saddle dolomite is common in fracture-vug,and intercrystalline pores and residual dissolution pores combined with fractures to form the effective pore-fracture network.Based on the coupling analysis of sedimentary and diagenesis characteristics,the reservoir formation model of“pre-depositional micro-paleogeomorphology controlling shoal,sedimentary shoal controlling dolomite,penecontemporaneous dolomite benefiting preservation of pores,and late hydrothermal action effectively improving reservoir quality”was systematically established.The“first-order high zone”micro-paleogeomorphology before the deposition of the Qixia Formation controlled the development of large area of intra-platform shoals in Gaoshiti area during the deposition of the Qi2 Member.Shoal facies is the basic condition of early dolomitization,and the distribution range of intra-platform shoal and dolomite reservoir is highly consistent.The grain limestone of shoal facies is transformed by two stages of dolomitization.The penecontemporaneous dolomitization is conducive to the preservation of primary pores and secondary dissolved pores.The burial hydrothermal fluid enters the early dolomite body along the fractures associated with the Emeishan basalt event,makes it recrystallized into medium–coarse crystal dolomite.With the intercrystalline pores and the residual vugs after the hydrothermal dissolution along the fractures,the high-quality intra-platform shoal-type thin-layer dolomite reservoirs are formed.The establishment of this reservoir formation model can provide important theoretical support for the sustainable development of Permian gas reservoirs in the Sichuan Basin.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Molecular Identification of Mycobacterium Strains Responsible of Bovine Tuberculosis Cases in Bobo-Dioulasso Slaughterhouse, Burkina Faso

Bovine tuberculosis (bTB) is an endemic zoonosis significantly affects animal health in Burkina Faso. The primary causative agent is Mycobacterium tuberculosis (M. tuberculosis) complex, mainly M. bovis. Cattle are considered as natural reservoir of M. bovis. However, in Burkina Faso, the circulation of these strains remains poorly understood and documented. This study aimed to identify and characterize Mycobacterium strains from suspected carcasses during routine meat inspection at Bobo-Dioulasso refrigerated slaughterhouse. A prospective cross-sectional study was conducted from January 2021 to December 2022 on cases of seizures linked to suspected bovine tuberculosis. Microbiological and molecular analyzes were used for mycobacterial strain isolation and characterization. Out of 50 samples, 24% tested positive by microscopy and 12% by culture. Molecular analysis identified 6 strains of Mycobacteria, exclusively Mycobacterium bovis specifically the subspecies bovis (Mycobacterium bovis subsp bovis). In conclusion, M. bovis subsp bovis is the primary agent responsible for bovine tuberculosis in Bobo-Dioulasso. Continuous monitoring of mycobacterial strains is therefore necessary for the effective control of this pathology in the local cattle population.

Fluid identification and tight oil layer classification for the southwestern Mahu Sag,Junggar Basin using NMR logging-based spectrum decomposition

The intricate distribution of oil and water in tight rocks makes pinpointing oil layers challenging.While conventional identification methods offer potential solutions,their limited accuracy precludes them from being effective in their applications to unconventional reservoirs.This study employed nuclear magnetic resonance(NMR)spectrum decomposition to dissect the NMR T_(2)spectrum into multiple subspectra.Furthermore,it employed laboratory NMR experiments to ascertain the fluid properties of these sub-spectra,aiming to enhance identification accuracy.The findings indicate that fluids of distinct properties overlap in the T_(2)spectra,with bound water,movable water,bound oil,and movable oil appearing sequentially from the low-value zone to the high-value zone.Consequently,an oil layer classification scheme was proposed,which considers the physical properties of reservoirs,oil-bearing capacity,and the characteristics of both mobility and the oil-water two-phase flow.When applied to tight oil layer identification,the scheme's outcomes align closely with actual test results.A horizontal well,deployed based on these findings,has produced high-yield industrial oil flow,underscoring the precision and dependability of this new approach.

Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology

The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.

TLC Identification and Extraction Process of Rubiasin-1-methyl Ether from Yao Medicine Chuanlianzhu

[Objectives]To establish a thin-layer chromatography(TLC)method for the determination of rubiadin-1-methyl ether in Yao Medicine Chuanlianzhu(Damnacanthus giganteus).[Methods]A silica gel G thin-layer plate was adopted for TLC.Petroleum ether(60-90℃)-chloroform-methanol-water(7:15:3:1)was used as the developing solvent and inspected under ultraviolet lamp(365 nm).The content was determined by Inertsil ODS-3 C 18 column(4.60 mm×250 mm,5μm),mobile phase:acetonitrile-0.2%phosphoric acid gradient elution,detection wavelength 277 nm,flow rate 1.0 mL/min,column temperature 30℃,injection volume 10μL.[Results]The spots of 10 Chuanlianzhu samples from different origins showed the same color at the same position as the control,and the spots were clear and specific.The injection volume of rubiadin-1-methyl ether showed a good linear relationship in the range of 2.90-145μg(R=0.9996).The average recovery rate of rubiadin-1-methyl ether in the low,medium and high dose groups of Yao Medicine Chuanlianzhu was 98.72%,and RSD=1.78%.[Conclusions]This method can effectively identify Yao Medicine Chuanlianzhu medicinal materials and accurately determine the content of rubiadin-1-methyl ether in the medicinal materials.It provides a scientific basis for the development and utilization of Yao Medicine Chuanlianzhu medicinal resources.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

A Transient-Pressure-Based Numerical Approach for Interlayer Identification in Sand Reservoirs

Almost all sandstone reservoirs contain interlayers. The identification and characterization of these interlayers iscritical for minimizing the uncertainty associated with oilfield development and improving oil and gas recovery.Identifying interlayers outside wells using identification methods based on logging data and machine learning isdifficult and seismic-based identification techniques are expensive. Herein, a numerical model based on seepageand well-testing theories is introduced to identify interlayers using transient pressure data. The proposed modelrelies on the open-source MATLAB Reservoir Simulation Toolbox. The effects of the interlayer thickness, position,and width on the pressure response are thoroughly investigated. A procedure for inverting interlayer parametersin the reservoir using the bottom-hole pressure is also proposed. This method uses only transient pressuredata during well testing and can effectively identify the interlayer distribution near the wellbore at an extremelylow cost. The reliability of the model is verified using effective oilfield examples.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Application of Next Generation Sequencing for Rapid Identification of Lactic Acid Bacteria

The rapid identification of lactic acid bacteria,which are essential microorganisms in the food industry,is of great significance for industrial applications.The identification of lactic acid bacteria traditionally relies on the isolation and identification of pure colonies.While this method is well-established and widely used,it is not without limitations.The subjective judgment inherent in the isolation and purification process introduces potential for error,and the incomplete nature of the isolation process can result in the loss of valuable information.The advent of next generation sequencing has provided a novel approach to the rapid identification of lactic acid bacteria.This technology offers several advantages,including rapidity,accuracy,high throughput,and low cost.Next generation sequencing represents a significant advancement in the field of DNA sequencing.Its ability to rapidly and accurately identify lactic acid bacteria strains in samples with insufficient information or in the presence of multiple lactic acid bacteria sets it apart as a valuable tool.The application of this technology not only circumvents the potential errors inherent in the traditional method but also provides a robust foundation for the expeditious identification of lactic acid bacteria strains and the authentication of bacterial powder in industrial applications.This paper commences with an overview of traditional and molecular biology methods for the identification of lactic acid bacteria.While each method has its own advantages,they are not without limitations in practical application.Subsequently,the paper provides an introduction of the principle,process,advantages,and disadvantages of next generation sequencing,and also details its application in strain identification and rapid identification of lactic acid bacteria.The objective of this study is to provide a comprehensive and reliable basis for the rapid identification of industrial lactic acid bacteria strains and the authenticity identification of bacterial powder.

Understanding the Global Identification with China’s Stories: A Cross-Cultural Perspective

Cross-cultural storytelling is a primary way for humankind to seek mutual recognition of value orientations between cultures,which facilitates the ability to jointly address the problems of human existence in the context of globalization.In this study,we conducted an interview survey of 6,130 respondents who were college students or graduates from 107 countries.The results show that there were a number of cross-cultural values embodied in China’s stories seen by the respondents as part of a common vision for the future of humankind and widely identified guidance on collaborative responses to global challenges.These cross-cultural values are common prosperity,ecological harmony,individual-collective integration,the urgency of global peace,as well as respect for multicultural and indigenous development paths.

Identification of Lubricating Oil Additives Using XGBoost and Ant Colony Optimization Algorithms

To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。

Pharmacognostic Identification of Hedyotis auricularia and Mitracarpus villosus

[Objectives]To conduct the pharmacognostic identification of Hedyotis auricularia and Mitracarpus villosus in Guangxi and provide a scientific basis for their identification.[Methods]The characteristics of original plants were studied by origin identification method;the properties and characteristics were studied by character identification method;and the microscopic features of the roots,stems,leaves and medicinal powder of H.auricularia and M.villosus in Guangxi were studied by paraffin method and powder slicing method.[Results](i)Origin identification.H.auricularia:Leaves leathery,apex acuminate,base cuneate;petiole shorter;cyme axillary;corolla hairy at throat;fruit indehiscent at maturity;testa black after drying.M.villosus:Leaf apex short pointed,base attenuate,blade sessile;flowers small,clustered in axillary;fruits dehiscent by lid at or below middle at maturity,seeds dark brown.(ii)Character identification.Fracture surface of H.auricularia uneven,white in outer layer and sepia in inner layer.Fracture surface of M.villosus hollow,uneven and white.(iii)Microscopic identification.H.auricularia:Root phloem thick,cambium visible,duct cells quasi-polygonal,large;rays obvious.Stem transection quasi-circular square,often with non-glandular hairs on epidermis;calcium oxalate raphides present in leaf parenchymal cells.Power grayish brown,starch granules single-grained;calcium oxalate raphides frequent,calcium oxalate clustered crystals occasional;catheter spiral,rarely annular,stomata infinitive.M.villosus:Root parenchyma cells with scattered calcium oxalate raphides,calcium oxalate clustered crystals and brownish red substances visible.Stem transection quasi-square,edge angle with 4 short narrow wings.Powder brown,simple starch granules numerous,compound starch granules also present;calcium oxalate raphides numerous,calcium oxalate clustered crystals and calcium oxalate square cubic crystals also present;catheter spiral,stomata paracytic.[Conclusions]The above transaction microscopic characteristics of the roots,stems and leaves and powder characteristics can be used as the identification features of H.auricularia and M.villosus.

Pharmacognosy research and identification of Euphorbia prostrata Ait.

Background:Euphorbia prostrata Ait.is an annual herb widely distributed in the southern region of China with great medical values on Anti-inflammation,insect repellent,treatment of diarrhea.Despite its extensive uses as a traditional Chinese medicine,no systematic research on the identification of E.prostrata has been reported.Methods:The study aimed to establish an accurate identification system for E.prostrata through traditional pharmacognostical methods,including botanical origin,morphological characters,medicinal material characters,microscopic characters,physicochemical parameters determination,phytochemical screening,and DNA barcoding analysis.Results:Physicochemical results show that this plant likely contains flavonoids,anthraquinones,and other substances.The ITS loci of the nuclear genome and psbA-trnH loci of the chloroplast genome were selected and evaluated,which were the most variable loci.Conclusion:The findings of this study are expected to contribute to the development of species identification,as well as provide references for authenticity identification,genetic relationship analysis,and further utilization of E.prostrata.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

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.

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.