Fast Identification Method Research Articles

Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non‐identical Distribution

ABSTRACTThis paper proposes a method to improve the identification performance of linear dynamic systems by utilizing knowledge from samples of non‐identical distribution systems. Traditional identification methods heavily rely on the quality of the dataset, such as sample length and noise level, which constrains their performance due to the assumption of identical distribution. Motivated by the concept of sample‐based transfer learning, we propose a sample transfer identification method and derive the condition to avoid negative transfer. We develop a fast iterative transfer identification method for low storage costs, considering the computational burden imposed by the sample size from the source system. Additionally, based on the fast iterative transfer identification method, considering the need to update the current measurement data model in real time, a fast iterative online sample transfer identification method is explored. Through simulations, we validate the effectiveness and superiority of the proposed methods. The results show that sample transfer identification is superior to non‐transfer identification and fast iterative sample transfer identification effectively reduces the calculation amount when dealing with low quality measurement data.

A fast impact force identification method via constructing a dynamic reduced dictionary

Impact force identification is an important aspect of structural health monitoring for online assessment of the integrity of engineering structures. Increasing the number of monitoring positions is undoubtedly beneficial to accurate force localization, however, it may increase sharply the dimension of the transfer matrix and thus lead to a large-scale inverse problem. In this case, traditional methods, such as the Iterative Shrinkage Threshold algorithm (IST) and the Two-Step Iterative Shrinkage Thresholding algorithm (TwIST), suffer from low solving efficiency. In this paper, the dynamic reduced dictionary is firstly introduced according to the sparsity of impact force in both time and spatial domains so as to lower significantly the dimension of the transfer matrix. Then, a new reduced unconstrained optimization problem is established via the dynamic reduced dictionary for impact force identification. By integrating the reduced unconstrained optimization problem into each iteration step of IST/TwIST, two novel algorithms, which are the reduced Newton iteration shrinkage threshold algorithm (rNIST) and the reduced Newton two-step iteration shrinkage threshold algorithm (rNTwIST), are proposed to solve quickly the large-scale impact force identification problem using ℓ1-norm sparse regularization. Meanwhile, the adaptive setting of regularization parameters strategy is used to construct force more fast and accurately. The proposed algorithms are numerically and experimentally demonstrated through identifying impact forces at 16-point and 81-point distributions of a plate using one sensor response. The results show that rNIST/rNTwIST can more quickly and accurately identify impact forces under numerous monitoring points compared with other ℓ1-norm regularization methods and nonconvex sparse regularization methods. Moreover, the more the number of monitoring points, the more obvious the efficiency improvement of the impact force identification by rNIST/rNTwIST method.

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

NEW DISCOVERIES IN TAXONOMY OF Dalbergia GENUS IN GUATEMALA REVEALED BY MOLECULAR APPROACHES

<p><strong>Background. </strong>The <em>Dalbergia</em> genus (Fabaceae) in Guatemala harbors valuable rosewood species; however, these timber species face significant threats from illegal logging and deforestation. Due to the morphological similarity between closely related <em>Dalbergia</em> species, accurate morphological identification is challenging, leading to uncertainty about the occurrence of new species in the country. The lack of information about the actual number of <em>Dalbergia</em> tree species complicates the development of management and conservation strategies for this endangered timber species. <strong>Objective. </strong>To elucidate the taxonomy of the tree species of the <em>Dalbergia</em> genus in Guatemala using species molecular delimitation methods. <strong>Methodology</strong>. Sixty-one <em>Dalbergia</em> specimens, collected in its natural range in Guatemala, were analyzed using nuclear (ITS) and chloroplast (matK and trnH-psbA) markers. Species delimitation was performed using three methods based on genetic distance, two based on single-locus phylogenetic trees, and two multi-loci. <strong>Results</strong>. Different Molecular Operational Taxonomic Units (MOTU) were estimated, ranging from 2 to 9 depending on the method and locus used. The molecular approaches consistently delimited the 6 species already reported for Guatemala. Furthermore, 3 MOTUs were identified that did not align with these known species, implying the presence of 3 new species for the country. <strong>Implications. </strong>Efficient molecular methods identify <em>Dalbergia</em> species from leaf samples, but standardizing wood sample identification is recommended for uncertain wood confiscation origins. This study proposes a new taxonomy of the genus Dalbergia in Guatemala and offers a fast and reliable identification method. <strong>Conclusions. </strong>With the molecular methods used in the study, three new <em>Dalbergia</em> species in Guatemala are proposed, corroborating previous suggestions based on morphological characterization. This discovery expands the existing inventory of <em>Dalbergia</em> tree species in Guatemala, comprising six previously documented species and three novel species that require detailed botanical descriptions for final naming.</p>

A new method for fast and accurate identification of tool-workpiece relative vibration based on machining surface topography

A new method for fast and accurate identification of tool-workpiece relative vibration based on machining surface topography

A fast identification method of surface heat flux based on numerical pre-calibration and regularization

Abstract Identification of surface heat flux is a technique of retrieving heat flux from the measured temperature in an object. The inverse heat conduction problem abbreviated as IHCP is ill-posed, and its highly ill-conditioned characteristics lead to unstable numerical calculation. In this paper, we build an identification equation with the theory of linear superposition. The matrix of pulse sensitivity coefficient is established by formula for one-dimensional model. And a finite element pre-calibration approach is proposed to obtain the matrix of pulse sensitivity coefficient for complex three-dimensional structures. Regularization methods including Tikhonov method and algebraic reconstruction technique are utilized to reduce the sensitivity of measurement error. Numerical simulation shows that this method is efficient and accurate.

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

A fast and efficient pseudo-direct identification method for orthotropic elastic properties of multi-layered composite plates

New types of laminated composites are continuously being developed to meet various requirements involved in engineering applications. Before mass production of these new composite materials can take place, it is necessary to identify their elastic properties. Although current identification methods based on vibration tests and optimization algorithms are highly accurate, their high computation cost is a drawback. To address this issue, this paper proposes a new method to identify orthotropic elastic properties of multi-layered composite plates. The proposed method is based on the Modal Stability Procedure, using the experimental vibration frequencies and corresponding numerical mode shapes of structures. It converges after only two or three iterations without requiring an optimization algorithm. Therefore, the computational cost of the proposed method is significantly lower than that of optimization-based methods. The effectiveness and robustness of this method are demonstrated through three examples with different material, stacking sequences, and geometries including curved plate.

Development of a top-down MS assay for specific identification of human periostin isoforms.

Periostin is a matricellular protein encoded by the POSTN gene that is alternatively spliced to produce ten different periostin isoforms with molecular weights ranging from 78 to 91kDa. It is known to promote fibrillogenesis, organize the extracellular matrix, and bind integrin-receptors to induce cell signaling. As well as being a key component of the wound healing process, it is also known to participate in the pathogenesis of different diseases including atopic dermatitis, asthma, and cancer. In both health and disease, the functions of the different periostin isoforms are largely unknown. The ability to precisely determine the isoform profile of a given human sample is fundamental for characterizing their functional significance. Identification of periostin isoforms is most often carried out at the transcriptional level using RT-PCR based approaches, but due to high sequence homogeneity, identification on the protein level has always been challenging. Top-down proteomics, where whole proteins are measured by mass spectrometry, offers a fast and reliable method for isoform identification. Here we present a fully developed top-down mass spectrometry assay for the characterization of periostin splice isoforms at the protein level.

Rapid Identification of Rhizobia Nodulating Soybean by a High-Resolution Melting Analysis

Soybean [Glycine max (L.) Merr.] is one of the most important and oldest crops. Due to its ability to form symbiotic interactions with nitrogen-fixing bacteria, it is a valuable source of nitrogen for agriculture and proteins for humans and livestock. In Europe, for instance, in Poland, the soybean cultivation area is still not large but is gradually increasing due to climate change. The lack of indigenous soybean microsymbionts in Polish soils forces the application of commercial strains to establish effective symbioses. Fast and reliable identification methods are needed to study the persistence, competitiveness, and dispersal of bradyrhizobia introduced as inocula. Our study aimed to apply real-time PCR coupled with high-resolution melting curve (HRM) analysis to detect and differentiate bacterial strains occupying soybean nodules. HRM-PCR was performed on crude extracts from nodules using primers specific for recA, a highly conserved nonsymbiotic gene. By comparing them with the reference strains, we were able to identify and assign Bradyrhiobium strains that had been introduced into field locations in Poland. In conclusion, HRM analysis was proven to be a fast and accurate method for identifying soybean microsymbionts and might be successfully used for identifying other legume-nodulating bacteria.

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Modal parameters are structural inherent characteristics that can be applied for revealing performance of railway bridges. Free vibration signals generated by a passage of train are commonly utilized to estimate the modal parameters of railway bridges due to their higher signal-to-noise ratios compared to random vibrations caused by ambient loads. However, since free vibration signals rapidly decay over time, the available free-vibration data is typically short-time. When using the fast Fourier transform-based spectral estimation method for modal identification from short-time vibration data, a phenomenon known as spectral leakage occurs, leading to miss-identification of some structural modes. In this study, the classical frequency domain decomposition (FDD) is improved for modal identification of railway bridges, in which the higher resolution auto-power spectral density (PSD) and cross-PSD functions are calculated through the autoregressive (AR) model-based method. The AR model-based method improves both the smoothness and resolution of the PSD functions compared to the fast Fourier transform technique. These AR model-based PSD functions are then employed in the FDD process to facilitate frequency and mode shape identification while avoiding spurious noise modes. The proposed eigenvalue fitting technique is subsequently utilized to estimate damping ratios. Numerical simulation data as well as vibration data from an actual bridge are analyzed to validate the proposed method, with a comparison made to the Welch’s PSD-based method. The results demonstrate that the modified FDD approach enables more effective identification of structural modes, even in the presence of closely-spaced modes.

The Effects of AI-Driven Face Restoration on Forensic Face Recognition

In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification.

1Hβ chemical shift-based phase modulated NMR methods for fast identification of amino acid types in proteins

Here we report two phase modulated NMR experiments: PM-2D HN(CACBHB) and PM-2D HN(HB), that use 1Hβ chemical shifts to rapidly identify amino acid type in proteins. The magnetization on the 1Hβ spins during the experiments is allowed to evolve for a fixed evolution period that results in phase modulation (positive or negative) of the cross peaks corresponding to various amino acid residues on their 2D HN projections, resembling a typical 2D [1H–15N]-HSQC spectrum. All amino acids except glycine can be categorized into three discernible groups based on their 1Hβ chemical shifts, resulting in unique phase patterns at different fixed evolution periods for 1Hβ, thus facilitating their identification. Remarkably, the PM-2D HN(HB) stands out among all amino acid type identification NMR techniques for its applicability with cost-effective and most routinely employed 15N-labeled protein samples for NMR studies. Furthermore, when combined effectively with the 13Cβ chemical shift-based phase modulated NMR method (PM-2D HN(CACB)), these methods resolved the identification of large groups of amino acids into relatively smaller groups. Moreover, these techniques can accelerate the sequence-specific sequential resonance assignment (SSRA) process and would help in fast tracking of assigned NMR signals exhibiting chemical shift perturbation on the 2D [1H–15N]-HSQC spectrum of proteins during various experiments (e.g., temperature change, pH change, and protein or ligand or cofactor binding) as well as in site-directed mutagenesis.

Molecular Detection of Glanders in horses at the Equestrian Club in Baghdad city

glanders, a biological warfare agent, caused by a bacterium called B mallei. It is considered a contagious, fatal, zoonotic disease. The aim of this study was to establish a fast and reliable molecular method for identification of Burkholderia mallei, this technique was used to determine the Phylogenetic tree and genetic relationship between local and international isolates of B. mallei in horses of Iraq. The research was conducted by taking 100 nasal swabs from animals, and it was found that 29 cases were positive in the PCR test, and 4 random samples were taken from the positive cases for the purpose of sequence analysis.

Experimental investigation and prediction of overall and local flow patterns in pipeline-riser systems

A unified model for predicting flow patterns in the pipeline-riser system is essential for the flow assurance of offshore oil and gas pipelines. This paper uses the visual images and pressure signals to obtain the internal correlation and the transition boundaries of the overall and local flow patterns in a 46 mm ID, 1722 m long pipeline S-shaped riser system. The flow pattern map with industrial parameters is plotted, and a fast identification method for unstable flow is proposed based on the differential pressure signal of the riser. The mapping relationship between the overall flow pattern divided by the pressure signals and the local flow pattern distinguished by the visual images is established. The transition mechanism dominated by the liquid phase is whether the gas in the downward-inclined pipeline accelerates after entering the riser base, and the transition mechanism dominated by the gas phase is whether the gas can penetrate the liquid in the riser so that the gas does not accumulate in the downward-inclined pipeline. A unified mechanistic model is proposed for predicting the overall flow pattern transition boundaries in pipeline-riser systems. The model results show a good agreement with 24 kinds of flow pattern maps under various pipeline structures, fluid properties, and separator pressures.

Establishing a fingerprinting method for fast catheter identification in HDR brachytherapy in vivo dosimetry

To use quantities measurable during in vivo dosimetry to build unique channel identifiers, that enable detection of brachytherapy errors. Treatment plan of 360 patients with prostate cancer who underwent high-dose-rate brachytherapy (range, 16-25 catheters; mean, 17) were used. A single point virtual dosimeter was placed at multiple positions within the treatment geometry, and the source-dosimeter distance and dwell time were determined for each dwell position in each catheter. These values were compared across all catheters, dwell position by dwell position, simulating a treatment delivery. A catheter was considered uniquely identified if, for a given dwell position, no other catheters had the same measured values. The minimum number of dwell positions needed to identify a specific catheter and the optimal dosimeter location uniquely were determined. The radial (r) and vertical (z) dimensions of the source-dosimeter distance were also examined for their utility in discriminating catheters. Using a virtual dosimeter with no uncertainties, all catheters were identified in 359 of the 360 cases with 9 dwell position measurements. When only the dwell time were measured, all catheters were uniquely identified after 1 dwell position. With a 2-mm spatial accuracy (r,z), all catheters were identified in 94% of the plans. Simultaneous measurement of source-dosimeter distance and dwell time ensured full catheter identification in all plans ranging from 2 to 6 dwell positions. The number of dwell positions needed to uniquely identify all catheters was lower when the distance from the implant center was higher. The most efficient fingerprinting approach involved combining source-dosimeter distance (i.e., source tracking) and dwell time. The further the dosimeter is placed from the center of the implant the better it can uniquely identify catheters.

Environment-independent textile fiber identification using Wi-Fi channel state information

Textile fiber identification is a technique that can help identify the type of target textile fiber. Existing methods usually rely on expensive detection instruments, specialized researchers, and complex processing techniques. The large number of textile fibers makes it difficult for researchers to use a stable and fast method for identification. This paper introduces a textile fiber identification method based on Wi-Fi signals, and at the same time, in the actual measurement, the signal characteristics of Wi-Fi are usually interfered with by the hardware noise and multipath propagation of channel state information (CSI) measurement equipment. To eliminate the inherent noise of CSI, we designed a denoising method based on the CSI data acquisition of textile fiber samples in independent environments. Then, the features of Wi-Fi signal wavelet packet decomposition could be extracted more stably, and the principal component analysis (PCA) method was used to reduce the data dimension. Finally, the convolutional neural network (CNN) was used to classify the data features. We conducted extensive experiments to verify the effectiveness of the proposed method. The results show that the proposed method can identify all 14 kinds of common textile fibers used in the experiment, and the average accuracy is 93.25%.

Fast identification of machine tool spindle system temperature rise based on multi-model fusion and improved D-S evidence theory

Thermal equilibrium test is the key means to obtain the thermal characteristics of machine tools. In order to shorten the test period and reduce the research and development cost, a novel fast temperature rise identification method for machine tool spindle systems is proposed. The existing prediction identification methods ignore the limitation of the single prediction model, leading to large error fluctuations in different environments. In this study, various intelligent prediction models are combined with the improved D-S evidence theory to improve the accuracy and robustness of the prediction. Firstly, based on the virtual prediction, the evidence identification framework is established through the multiple evaluations of the data information in the evidence segment. Then, the weight allocation of each basic prediction model is carried out by the evidence combination theory. In this process, the evidence identification framework is reconstructed according to the improved strategy to avoid the high conflict problem in classical evidence theory. Finally, the fusion prediction of multiple models can be realized. The VM-850L machining center was selected as the research object for the thermal equilibrium test to evaluate the proposed method. The results show that the proposed multi-model fusion prediction method can accurately predict the temperature rise of selected points in a short time. Moreover, the prediction accuracy is significantly improved compared with the traditional single model. The proposed method has good universality and is expected to be popularized and applied more widely.

D3Rings: A Fast and Accurate Method for Ring System Identification and Deep Generation of Drug-Like Cyclic Compounds.

Continuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds, as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in three different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, and fused and bridged rings. Based on this, three deep generative models, namely, VAE, AAE, and CharRNN, were trained and used to construct two data sets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged data sets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like data set against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/.

Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy

The overuse of plastics releases large amounts of microplastics. These tiny and complex pollutants may cause immeasurable damage to human social life. Raman spectroscopy detection technology is widely used in the detection, identification and analysis of microplastics due to its advantages of fast speed, high sensitivity and non-destructive. In this work, we first recorded the Raman spectra of eight common plastics in daily life. By adjusting parameters such as laser wavelength, laser power, and acquisition time, the Raman data under different acquisition conditions were diversified, and the corresponding Raman spectra were obtained, and a database of eight household plastics was established. Combined with deep learning algorithms, an accurate, fast and simple classification and identification method for 8 types of plastics is established. Firstly, the acquired spectral data were preprocessed for baseline correction and noise reduction, Then, four machine learning algorithms, linear discriminant analysis (LDA), decision tree, support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN), are used to classify and identify the preprocessed data. The results showed that the classification accuracy of the three machine learning models for the Raman spectra of standard plastic samples were 84%, 93% and 93% respectively. The 1D-CNN model has an accuracy rate of up to 97% for Raman spectroscopy. Our study shows that the combination of Raman spectroscopy detection techniques and deep learning algorithms is a very valuable approach for microplastic classification and identification.