Method For Automatic Identification Research Articles

Automatic recognition of slide mass and inversion analysis of landslide based on discrete element method

The slip surface is an important structure for analyzing the landslide mechanism, and it will be deformed by the traction and shear of the slide mass. For the landslide accident without artificial monitoring, if the slip surface can be restored according to the slope morphology before and after the landslide, information such as the safety factor before the damage to the slope and the volume of the slide mass can be obtained by the position of slip surface, which helps engineers to analyze the cause of the landslide. In this paper, an automatic slide mass identification and landslide inversion analysis method based on discrete element simulation is proposed. The method combines Gaussian Mixture Model (GMM), alpha shape algorithm, discrete element simulation, and the morphology of the slope before and after the landslide to invert the slip surface of the slope. This method is suitable for two and three dimensional slope models. In addition, the effect of the slope angle on the deformation of the slip surface is analyzed.

Hyperparameter-tuned batch-updated stochastic gradient descent: Plant species identification by using hybrid deep learning

The accurate identification of plant species is crucial for the conservation of biodiversity. However, traditional methods for identifying plant species are often complicated, time-consuming, and prone to errors. Therefore, it is essential to address these challenges and develop automated identification methods to enhance the efficiency and accuracy of plant species identification. In this study, a step-by-step method was utilized to identify and classify plant species. The dataset was first loaded, and then preprocessing was performed to remove noisy data. Following that, data augmentation was carried out to improve model accuracy. The deep convolutional neural network (CNN) and visual geometry group-16 (VGG-16) were then employed to extract only the relevant features, owing to their efficient learning capabilities. Feature-level fusion was accomplished by utilizing dimensionality reduction, and enhanced Spearman's principal component analysis (ESPCA) was employed to address the overfitting problem, eliminate redundant data, and reduce storage space and training time requirements. For classification, the hyperparameter-tuned batch-updated stochastic gradient descent (HP-BSGD) method was utilized. The Flavia and Swedish datasets were utilized in the experiments. The proposed hybrid classifier yielded excellent results due to its high convergence speed, good computational effectiveness, and high flexibility. To validate the experimental results, performance and comparative analyses were carried out using standard metrics. The analytical results demonstrated the superior efficiency and suitability of the proposed method in the classification of plant species over existing methods. The hybrid method achieved approximately 97% and 98.85% accuracy in the Flavia and Swedish datasets, respectively, when considering combined features. The performance of the proposed method was further enhanced by considering leaves at different stages, such as seedlings, tiny, mature, and dried leaves.

Automatic Identification Method of HPLC Platform Topology Based on Characteristic Data Extraction

The topological structure of distribution network system is complex, and the operation state changes frequently, so the obtained distribution network topological information has a high error. Therefore, this article proposes a clustering feature extraction method of load curve based on singular value decomposition. The load curve is given by the invariance of singular vectors to improve the generalization ability of feature processing. On the basis of considering the weight of load feature, the data integrity is ensured by singular value curve, and the clustering accuracy is high, which makes the load feature have practical physical significance. The experimental results show that this method can achieve good clustering effect, reduce the clustering time, improve the reliability of data transmission and communication coverage, and meet the communication access requirements of the power Internet of Things sensing layer.

Correction to: Automatic lithology identification method based on efficient deep convolutional network

Correction to: Automatic lithology identification method based on efficient deep convolutional network

Prevalence and Antibiotic Resistance of Enterococcus spp.: A Retrospective Study in Hospitals of Southeast Romania

Enterococci cause infections with various localizations, the most common being urinary infections. The purpose of the study was to identify the profile of the antimicrobial resistance of enterococci species (AMRE) isolated from patients hospitalized in three hospitals in Romania. We evaluated AMRE retrospectively (2019–2021) in various biological samples. The microbiological diagnosis was sustained by classical methods of bacteria culture and automatic identification. The sensitivity testing was performed by the Kirby–Bauer method, and the antibiotic minimum inhibitory concentration was tested by the automated Vitek system. We analyzed 86 strains of Enterococcus spp., identifying the following species: 47.7% E. faecalis, 47.7% E. faecium, 3.55% E. gallinarum, and 1% E. hirae. Most of the bacterial strains were isolated from urocultures (38.4%) and hemocultures (32.6%). Overall, the rate of vancomycin resistance was 5.8% for E. faecalis and 15.1%. for E. faecium. The prevalence of multidrug-resistant (MDR) strains was found to be 100% in E. gallinarum, 75.6% in E. faecium, and 21.9% in E. faecalis. The results confirm the high level of AMRE, which creates difficulties with adequate antibiotic prescriptions. The continuous monitoring of AMRE is essential for updating the local diagnostic and treatment protocols for enterococcal infections.

Identification and analysis of ghost reflection in the interferometers based on ZEMAX by optimization algorithms

Laser interferometers and grating interferometers based on optical interferometry are widely used in displacement measurement of precision machining and testing equipment, such as the measurement system of integrated circuit equipment, due to their high precision, noncontact, and large dynamic measurement range. The ghost reflection in optical elements may lead to the periodic nonlinear error of the interferometer and also reduce alternating current/direct current. We propose a general method for automatic ghost reflection interface identification. It can analyze the influence weight of ghost reflection for each interface of any interferometer. In addition, the manufacturing cost of the interferometer is effectively reduced by optimization algorithms that enable ghost reflection avoidance in the interferometer design. Experimental results prove the influence weight of ghost reflection at different positions in the interferometer and provide the parameter selection of the most suitable interface reflection of the interferometer.

Machine learning-based automatic identification and diagnosis of dental caries and calculus using hyperspectral fluorescence imaging.

Precise diagnosis and identification of early dental caries facilitates timely intervention and reverses the progression of the disease. Developing an objective, accurate and rapid caries and calculus automatic identification method advances clinical application and facilitates the promotion and screening of oral health in the community and family. In this study, based on 122 dental surfaces labeled by professional dentists, hyperspectral fluorescence imaging combined with machine learning algorithms was employed to construct a model for simultaneously diagnosing dental caries and calculus. Model trained by fusion features based on spectra, textures, and colors with the integrated learning algorithm has better performance and stronger generalization capabilities. The experimental results showed that the diagnostic model's accuracy, sensitivity, and specificity for identifying four different caries stages and calculus were 98.6%, 98.4%, and 99.6%, respectively. The proposed method can evaluate the whole tooth surface at the pixel level and provides discrimination enhancement and a quantitative parameter, which is expected to be a new approach for early caries diagnosis.

Automatic lithology identification method based on efficient deep convolutional network

Automatic lithology identification method based on efficient deep convolutional network

Automatic Nonlinear Subspace Identification Using Clustering Judgment Based on Similarity Filtering

Accurately determining system order plays a vital role in system identification directly related to the accuracy of identification results, especially for nonlinear system identification. Due to the need for human subjective judgment, the traditional sequence determination method easily causes uncertainty in the results; and the phenomenon of the virtual mode or omission occurs. An automatic nonlinear subspace identification method is proposed to address the aforementioned problems. When the eigenvalue decomposition of the constructed Hankel matrix is performed, the calculation range of the modal order of the system is estimated. The similarity coefficient and distance function are introduced to cluster the identified modal results, the poles of the false modes are removed to obtain the cluster stabilization diagram, and the best order of the system is received. Then, the modal parameters and nonlinear coefficients are obtained. Simulation examples are carried out to verify the effectiveness and robustness of the proposed method. An experimental study is carried out on a multilayer building with nonlinear characteristics. Compared with the traditional stabilization graph, the accuracy of the automatic order determination proposed in this paper is proven.

A physics-constrained data-driven workflow for predicting bottom hole pressure using a hybrid model of artificial neural network and particle swarm optimization

Accurate and efficient prediction of bottom hole pressure (BHP) is important for managed pressure drilling (MPD), which is essential to ensure the safety of drilling in complex formations with a narrow pore/fracture pressure envelope. Without idealized assumptions and iterative solutions, the data-driven machine learning model has higher prediction accuracy and efficiency than the mechanistic hydraulics model. However, the machine learning models suffering from the significant impact of noise data and the strict restriction of the data field, often leads to anomalous deviations. Physical constraints are inherent mappings between output values and characteristic variables, which can be applied to model training to improve the robustness of the model.In this study, wellbore flow mechanism is considered as the physical constraint, and a physics-constrained data-driven workflow is proposed for stable prediction of BHP, which is more consistent with the hydraulic mechanism. Firstly, more than 400,000 groups of field pressure data are extracted as the training dataset by an automatic identification method of drilling state. And twelve characteristic parameters of BHP were optimized, including inlet flow rate, outlet density and wellhead pressure. Embedding physical constraints into the loss function of artificial neural network (ANN) as penalty terms can induce ANN model output results within the wellbore flow mechanism. Finally, particle swarm algorithm is introduced to solve the weight and bias of ANN globally without the derivative of the restructuring loss function.The proposed model is verified based on the field pressure data. It could be found that both experiential and knowledge-based constraints can improve the accuracy and stability of the ANN model, the prediction error is significantly reduced, MRE, RMSE and MAE were respectively reduced to 0.46%, 0.34 MPa and 0.27 MPa. RMSE decreased by 20.9%, MRE and MAE decreased by 19.3% and 18.1%, respectively. Meanwhile, the R2 of the model reached 0.9871. Sensitive parameters such as flow rate have a more significant effect on the prediction accuracy of BHP, while non-sensitive parameters such as mud density can eliminate the abnormal deviation of BHP more effectively, and the constraints of multiple parameters can be superimposed. Therefore, optimizing the constraint combination according to the fluctuation characteristics of BHP is crucial to improve the accuracy and stability of the ANN model. This is an innovative exploration of the physical constraints on the data-driven model of BHP, which can provide accurate and efficient references for managed pressure drilling.

An automatic identification method of imbalanced lithology based on Deep Forest and K-means SMOTE

Accurate identification of lithology is an important basis for oil and gas exploration and reservoir geological evaluation. Logging parameters often have a complex nonlinear relationship with lithology. With the development of artificial intelligence technology, a variety of data mining algorithms have been applied to lithology identification with logging data. However, due to the constraints of practical conditions, the number of labeled lithology samples is small and the imbalance between classes is obvious, which usually affect the results of lithology identification. In this paper, we proposed a hybrid unbalanced lithology identification method based on Deep Forest and K-means SMOTE to solve the above mentioned problems. Deep Forest is the first deep model of a non-differential form base learner which can perform layer-by-layer processing and feature enhancement. Compared with traditional shallow model, ensemble model and deep neural networks, it has the best performance with the accuracy of 96.17%. K-means SMOTE oversampling is able to filter subclusters and balance the dataset by oversampling only within safe regions, reducing interference from noise points and boundary ambiguity. We achieved further performance improvements in minority lithology classification via K-means SMOTE oversampling with the accuracy of 97.17%. The comprehensive evaluation results showed that the method proposed in this paper has a good effect on the identification of unbalanced lithology, and has a practical application prospect.

Automatic identification and location method for shaft-hole interference fit crack based on deep learning

Cracking on the shaft of the interference fit joint surface is a common failure form for the shaft-hole interference fit. Because the crack is small and is located at the shaft edge, the crack signal is affected by echo and reflection signals and noise when traditional ultrasonic guided wave methods are used to detect the crack. When the crack is small-sized, the crack signal may even be submerged completely in the echo and reflection signals. Therefore, the traditional detection method is unable to obtain accurate information about the crack locations easily and thus cannot evaluate the overall safety performance of the interference fit accurately. To solve these problems, this paper presents an on-axis crack monitoring system for interference fit joints using laser ultrasound and an improved YOLOv5 network that integrates early warning, detection, positioning, and analysis functions. The system detects axial cracks at the shaft-hole interface via laser ultrasonic guided waves on the workpiece, and captures the crack information by deep learning; then, the captured crack information is analyzed to extract the essential crack location information. Finally, the method is verified experimentally. The experimental results show that this method can not only identify axial cracks at the shaft-hole interference fit surface automatically, but also can locate these cracks accurately and obtain both the number of cracks and the distance between pixels.

Automatic modal identification based on similarity filtering and fuzzy clustering

Artificially determining the model order is necessary for time domain modal identification, which affects the identification efficiency and accuracy. An automatic identification method is proposed to determine the best estimation of the model order. Based on the covariance-driven stochastic subspace identification method (SSI-COV), the singular value decomposition of the Toeplitz matrix is carried out to determine the range of the lower triangular matrix. The similarity coefficient and distance function are introduced to cluster the modes, and the poles of the false modes are removed to obtain the clustering stabilization diagram. The model order is taken as the number of clustering centers of the fuzzy c-means (FCM) algorithm, and all the poles of the stabilization diagram are clustered to get the modal parameters of each order. The effectiveness of the proposed method is verified by adopting a thin-walled cylindrical column, a triangular-prism truss in simulation, and a flexible beam in experiment. The robustness of the method is illustrated by adding white noise to the simulated response data. The advantage of the clustering stabilization diagram is demonstrated by comparing with the traditional stabilization diagram.

High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network

Dicentric chromosome analysis is the gold standard for biological dose assessment. To enhance the efficiency of biological dose assessment in large-scale radiation catastrophes, automatic identification of dicentric chromosome images is a promising and objective method. In this paper, an automatic identification method for dicentric chromosome images using two-stage convolutional neural network is proposed based on Giemsa-stained automatic microscopic imaging. To automatically segment the adhesive chromosome masses, a k-means based adaptive image segmentation and watershed segmentation algorithm is applied. The first-stage CNN is used to identify the dicentric chromosome images from all the images and the second-stage CNN works to specifically identify the dicentric chromosome images. This two-stage CNN identification method can effectively detects chromosome images with concealed centromeres, poorly expanded and long-armed entangled chromosomes, and tricentric chromosomes. The novel two-stage CNN method has a chromosome identification accuracy of 99.4%, a sensitivity of 85.8% sensitivity, and a specificity of 99.6%, effectively reducing the false positive rate of dicentric chromosome. The analysis speed of this automatic identification method can be 20 times quicker than manual detection, providing a valuable reference for other image identification situations with small target rates.

Automatic Construction Hazard Identification Integrating On-Site Scene Graphs with Information Extraction in Outfield Test

Construction hazards occur at any time in outfield test sites and frequently result from improper interactions between objects. The majority of casualties might be avoided by following on-site regulations. However, workers may be unable to comply with the safety regulations fully because of stress, fatigue, or negligence. The development of deep-learning-based computer vision and on-site video surveillance facilitates safety inspections, but automatic hazard identification is often limited due to the semantic gap. This paper proposes an automatic hazard identification method that integrates on-site scene graph generation and domain-specific knowledge extraction. A BERT-based information extraction model is presented to automatically extract the key regulatory information from outfield work safety requirements. Subsequently, an on-site scene parsing model is introduced for detecting interaction between objects in images. An automatic safety checking approach is also established to perform PPE compliance checks by integrating detected textual and visual relational information. Experimental results show that our proposed method achieves strong performance in various metrics on self-built and widely used public datasets. The proposed method can precisely extract relational information from visual and text modalities to facilitate on-site hazard identification.

Automatic batch recognition of rock deformation areas based on image segmentation methods

Image recording and analysis is an important but time-consuming method for understanding the rock mechanics mechanism. In this paper, a method for automatic batch identification of rock deformation areas is proposed. We crop the original image to remove irrelevant background. And we use adaptive threshold segmentation, region growth segmentation and global threshold segmentation and combine the characteristics of the image to identify the rock deformation area. Finally, we use image morphology processing to make the recognition result more accurate. For validation, 359 images of the rock samples of the uniaxial compression test were quickly identified. The identification time was approximately 5′56.83′. The average relative error of the method in the X and Y directions is 10.88% and 8.60%, respectively. In addition, using the identification results and the stress-strain curve, it was found that the water content and initial crack length of rock increase, and the compressive strength decreases; the effect of the initial fracture inclination on the compressive strength of the rock is not obvious.

Research on the interactive design of electric vehicle interior based on voice sensing and visual imagery

With the complete function of modern automobiles, in-vehicle intelligent devices are becoming more and more complex and the requirements for human-computer interaction are also increasing. The research proposes a speech recognition method that combines multi-window estimation spectral subtraction and dynamic time warping to enhance the denoising ability and speech recognition ability of in-vehicle devices. It also proposes actions based on a Gaussian hybrid segmentation algorithm and a visual image functional space segmentation algorithm. The automatic identification method and the validity of the algorithm are verified. The results show that under different input signal-to-noise ratios, the denoising capability of the method is improved by 2.45% to 31.47% over the baseline method. And the accuracy of speech recognition in the vehicle environment is 92.3% to 98.7%. It is hoped that this research can make some contributions to the upgrading of voice and visual interaction within electric vehicles.

Distribution Network Topology Identification Using Asynchronous Transformer Monitoring Data

The topology of a distribution network with a high penetration of fluctuating renewable energy is usually adjusted frequently for the reliable and economical operation. This paper proposes an automatic topology identification method based on measurements from widely distributed transformer terminal units (TTUs) to align the topology model in the management system with the actual topology of the distribution network. The proposed method formulates the topology identification problem as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L2$</tex-math></inline-formula> -norm minimization problem. The challenge lies in the absence of phase angle measurements and the sampling errors caused by the non-synchronous sampling time of different TTUs. Adding variables into the minimization problem for the unavailable phase angles introduces nonlinearity and non-convexity, and the non-synchronous sampling time can cause significant sampling errors when measuring the fluctuating power injections. The non-convexity is addressed by proposing a novel linearization and convex relaxation approach to convert the formulated minimization problem into a convex mixed integer quadratically constrained program (MIQCP) problem. The significant sampling error is addressed by generating pseudo measurements from Lagrange interpolation to be used as alternatives to the actual power measurements having significant errors. Case studies conducted in test systems demonstrate the effectiveness and efficiency of the proposed method.

Generation of Friction Coefficient Function for Hot Forging with Autopilot FEA and Simultaneous Identification of Friction and Heat Transfer Coefficients

An automatic simultaneous identification method for the friction coefficient and heat transfer coefficient is proposed for accurate hot forging process simulation. We established an autopilot finite element analysis system (autopilot FEA; APFEA) that repeats the FEA of the hot ring compression test, minimizing the errors, defined by the compression load and minimum inner diameter reduction ratio, between results of the experiment and the FEA with a machine-learningbased optimization algorithm. The automatic generation of a surface-pressure-dependent friction coefficient function, the selection of the friction law from Coulomb’s and shear friction lows, and the automatic simultaneous identification of the sigmoid curve friction and heat transfer coefficient functions are performed using the APFEA system and the results of a hot ring compression test performed with a quasi-air-suspended heating tester designed by us. As a result, the optimized friction and heat transfer parameters are identified, and the reasonableness of the friction law is evaluated.

Classification methods and models for automatic determination of goods code by foreign economic activity goods nomenclature

Classification methods and models for automatic determination of goods code according to the commodity nomenclature of foreign economic activity (CN FEA) are considered. In the classification procedure, the object is reduced to one or more classes based on the results of the comparison to evaluate the proximity and make a conclusion. The relationship between vectors calculated by scalar multiplication was used as a measure of proximity. In order to determine the most informative symbols for classes, linguostatistical methods based on the information about the a priori probability of occurrence of terms and symbols were used. In doing so, macro-average and micro-average methods, which are considered effective in assessing the quality of classification for several classes, were used. Also, on the basis of syntactic and linguostatistical analysis, a generalized scheme of the process of automatic classification of goods in column 31_1 of the goods declaration graph is proposed. This recommended method of automatic identification of the CN FEA code allows participants of foreign economic activity to ease the processes of filling out declarations for goods.