Identification Method Research Articles

Coupling deflectometry measurements on membranes to the force analysis technique: opportunities and challenges

This work is grounded on the force analysis technique, an identification method that directly uses a structure's equation of motion to formulate an inverse problem, explicitly identifying the force causing the system's motion. This technique has mainly been applied on heavy objects since contact vibration measurements using accelerometers on light and thin structures are biased by the added mass and are complex to setup. Using non-contact and full-field vibration measurements (here, deflectometry), opens the possibility of applying the force analysis technique to such structures. This research aims to investigate the opportunities offered by the identification of space-time varying loadings of various types (acoustical, mechanical, and turbulent boundary layer) all these loadings being applied on a membrane. Through this study, we also strive to highlight the challenges and opportunities brought by of coupling non-contact and full-field measurements on membranes to the force analysis technique.

Identification of semen traces at a crime scene through Raman spectroscopy and machine learning

Biological fluid stains can be instrumental in solving crimes. Identification of semen can help reconstruct events in sexual assault cases and identify suspects via DNA profiling. Current methods for semen identification suffer from limitations, including destruction of the sample and potential false positives. One of the main unsolved issues is the elimination of underlying substrate interference. In this paper, chemometric approaches were developed to isolate and identify a biofluid stain on interfering substrates using Raman spectroscopy. The first approach, called Multivariate Curve Resolution with the Addition Method, combines the standard addition method with multivariate curve resolution. The second one uses a criterion based on reducing the spectrum complexity when a spectral component is removed from a Raman spectrum of a multi-component sample entirely. The results demonstrate the superiority of the first approach relative to the second for both small volume fraction of the fluid stain compared to the substrate and random noise.

Observation and analysis of infrasound signals from the 1 January 2024 Japan earthquake

The 1 January 2024 Japan Earthquake occurred off the coast of Noto Peninsula, Japan. Three infrasound arrays s located more than 1400 km away from the epicenter received both local seismic and infrasound signals atmospherically propagating from the epicenter. The back azimuth, elevation angle and other parameters are calculated for the two types of infrasound signals received by the subarray through procedures of Progressive Multi-Channel Correlation (PMCC) and Frequency-Wavenumber (F-K) analysis. Source localization and other processing are also conducted. A recognition feature based on a low-frequency multi-scale entropy algorithm is proposed, and the received seismic signals of this event are correctly identified by using Support Vector Machine (SVM). This paper explores the calculation of earthquake parameters based on local infrasound wave and atmospherically propagated infrasound signals excited by distant earthquake events, and proposes a new method for infrasound signals event identification.

A robust force identification method based on L1+2-norm approximation theory

Obtaining the real force of the structure is the basic condition for efficient vibration reduction design. However, the uneasiness and instability of the inverse problem make the force identification still challenging. On the basis of L2-norm regularization, we propose a new method for force identification based on L1+2-norm approximation theory. By introducing the L1-norm penalty operator, a joint norm approximation optimization model is constructed to limit the non-zero force and punish the force value which approaching to zero, so that it can adapt to the identification of various types of forces. Then the optimal force solution is obtained by using the Primal-dual interior point method. Finally, the effectiveness and accuracy of the proposed method are verified by numerical simulation and experiment of asymmetric plate and double-layer plate structures. Different from previous studies, the L1+2-norm method in this work has high robustness, considers the characteristics of sparse regularization and L2-norm regularization, and can adapt to the identification of continuous forces and sparse forces at the same time, which has potential engineering application value.

Novel Chiral HPLC Method for Accurate Identification and Quantification of R&S Enantiomers in Ropivacaine on Immobilized Chiral Stationary Phase: Development and Validation

Purpose: Ropivacaine is a widely used local anesthetic for managing postoperative pain, particularly in procedures such as caesarean sections. While historically used as a racemate, its structural similarity to mepivacaine and bupivacaine within the pipecoloxylidide group is notable. Both enantiomers of ropivacaine exhibit similar nerve-blocking properties, but the R enantiomer is associated with increased cardiotoxicity compared to the S-Ropivacaine, like bupivacaine. This study aimed to develop and validate precise and rapid chiral chromatographic techniques for quantifying potential (R&S enantiomers). Methods: We used normal phase chromatography with a (3,5-dimethylphenylcarbamate) immobilized- type polysaccharide stationary phase to quantify R&S enantiomers. The method followed ICH Q2(R1) guidelines, employing CHIRAL ART Amylose-SA for determining S-Ropivacaine enantiomeric purity in pharmaceutical drugs. The validation included accuracy and LOQ (limit of quantification) profiles, with measurement error assessments. Linear regression of accuracy profiles post square root transformations set acceptance limits at ±10%. Results: The method accurately quantified R&S enantiomers, adhering to ICH Q2(R1) guidelines. Validation showed high accuracy and precise LOQ profiles. Measurement error assessments confirmed reliability. Linear regression of accuracy profiles stayed within ±10% acceptance limits, confirming the method's suitability for determining S-Ropivacaine enantiomeric purity. Conclusion: The developed chiral chromatographic method provides an effective means of quantifying the enantiomeric purity of S-Ropivacaine in pharmaceutical drugs. Its adherence to ICH Q2(R1) guidelines ensures reliability and accuracy in measurement. This method enables precise determination of S-Ropivacaine content, which is crucial for optimizing therapeutic outcomes while minimizing the risk of cardiotoxicity associated with the R enantiomer.

Modeling and Verification of Cable-Hole Transmission Tension Ratio Considering the Cable Lateral Extrusion

Cable-hole transmission is widely applied in cable-driven mechanisms to reduce the mechanical size. However, the driving tension is attenuated with the cable threading through the hole caused by uncertain factors such as local deformation, friction, and other effects, and errors in cable-hole transmission occur. To improve the transmission accuracy of cable-driven mechanisms, a tension distribution model considering the cable lateral extrusion is established. Then, an analytical tension ratio of the cable-hole transmission is derived based on the perturbation method and tension distribution model. Parameters of the tension ratio are identified using a particle swarm optimization algorithm. An adaptive tension control method considering the cable lateral extrusion is designed and compared with the method excluding the cable lateral extrusion in the cable-hole transmission. Finally, a cable-hole transmission experimental device was constructed to verify the tension ratio, parameter identification, and servo control method of the cable-hole transmission. The results show the motion control accuracy of the cable-driven mechanism can be significantly improved with the tension ratio considering the cable lateral extrusion. Compared to the case excluding the cable lateral extrusion, the errors in cable-hole transmission considering the lateral extrusion are reduced by an order of magnitude, and the tension vibration is significantly weakened.

Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm

Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm

Assessment of the Possibility of Using the Trace Element Status of a Herbal Drug as an Additional Quality Criterion: Chamomile Flowers as a Case Study

INTRODUCTION. Herbal drugs can often be difficult to identify and distinguish from morphologically similar species, especially when fractionated. For the analysis of complex mixtures of herbal origin, it is reasonable to use additional identification methods, including hierarchical clustering of herbal drugs by their trace element content.AIM. This study aimed to assess the possibility of using hierarchical clustering by trace element content for complex biological matrices (plant mixtures), with officinal chamomile species as a case study.MATERIALS AND METHODS. Multiple morphologically similar species grow together with officinal chamomile species that are widely used in medical practice. The study examined the flowers and peduncles of Matricaria recutita L., Tanacetum parthenium (L.) Sch.Bip., Leucanthemum vulgare Lam., Tripleurospermum inodorum (L.) Sch.Bip., and Matricaria suaveolens Buchenau, as well as artificial mixtures of chamomile with adulterant species (as samples non-compliant with pharmacopoeial specifi ca tions). The content of trace elements was determined by inductively coupled plasma mass spectrometry (ICP-MS). The statistical treatment of the results used Statistica 10 software with a built-in data analysis algorithm for multivariate experiments.RESULTS. Having determined the content of 56 trace elements, the authors plotted a hierarchical cluster tree using cluster analysis methods. M. recutita and M. suaveolens were shown to be the closest to each other in terms of their trace element status, which correlated with their taxonomy. Artificial plant mixtures formed intermediate clusters that were observed between different chamomile species. Moreover, according to the criterion of dissimilarity, the trace element cluster of M. recutita significantly differed from the trace element clusters of all the adulterant species and mixtures containing more than 10% of the adulterant species.CONCLUSIONS. This study demonstrated the possibility of using hierarchical clustering based on the content of trace elements in a biological object to analyse complex systems, including plant mixtures, and identify differences that may have discriminative value for the quality control of herbal drugs.

Differentiated strategies for synergistic mitigation of ammonia and methane emissions from agricultural cropping systems in China

Ammonia (NH3) and methane (CH4) emissions from agricultural cropping systems in China have substantially impacted environmental quality and climate change, which become the key sector for synergizing pollution control and carbon reduction. However, the experiences of synergistic NH3 and CH4 emission governance at fine scales in this sector are still lacking. Here, we estimated NH3 and CH4 emissions from agricultural cropping systems in China in 2019 and identified regional characteristics of these emissions using advanced spatial analysis techniques. Subsequently, we developed an innovative synergistic mitigation zoning identification method to establish targeted and spatially differentiated reduction measures, addressing a critical gap in mitigation strategies for agricultural cropping systems in China. The results showed that NH3 and CH4 emissions from the three most important crops were 3.04 Tg N yr-1 and 8.13 Tg C yr-1, respectively. NH3 and CH4 emissions exhibited the highest values in Eastern China, but the highest emission densities of NH3 (49.8 kg N ha-1 yr-1) and CH4 (394 kg C ha-1 yr-1) were observed in Northwest and South China, respectively. Further, bivariate local indicators of spatial association (LISA) maps revealed that the middle and lower reaches of the Yangtze River were hotspots for simultaneous high emissions of NH3 and CH4. Finally, based on the identified eight categories of zones, region-specific mitigation measures were proposed, including improved fertilization practices, irrigation methods, and straw management. These targeted, spatially differentiated mitigation strategies provide a reference framework and feasible approach for synergistic pollution control and carbon reduction in China's agricultural sector.

Inference of the acoustic properties of transversely isotropic porous materials

Transversely isotropic porous materials are well represented by equivalent fluid models based on semi-phenomenological models such as the Johnson-Champoux-Allard-Lafarge (JCAL) model and a set of material parameters. Direct measurements of these parameters, however, are often difficult or time consuming. As an alternative, inverse estimation methods provide a simple and fast framework to identify the JCAL model parameters, since they can be performed on single impedance tube measurements. In this work, we present a stochastic inverse identification method of the JCAL parameters for transversely isotropic materials. We consider the flow resistance and characteristic viscous length and the static tortuosity to be anisotropic and the frame to be fully rigid, resulting in nine unique parameters to be identified. The method is based on reflection factor measurements of a material in different orientations corresponding the transverse and isotropic principal axes. Hence, a priori knowledge of the orientation of the principal axes of the material is required. Results of the parameter inference are presented based on experimental data for a glass wool sample.

A Credibility Monitoring Approach and Software Monitoring System for VHF Data Exchange System Data Link Based on a Combined Detection Method

Due to VDES’s higher data transmission speed and complex communication protocols, vulnerabilities within its data link infrastructure are more pronounced. To ensure the reliability of VDES data transmission, this manuscript proposes a credibility monitoring approach based on the combined detection method of radio interference detection and spoofing source identification and localization, focusing on key data link vulnerabilities outlined in the IALA G1181 VDES VDL Integrity Guide. Automated monitoring is achieved through VDES data link monitoring software (VDES(AIS 2.0)), which is based on a three-tier architecture and a Client/Server (C/S) model. The software validates monitoring techniques and software against various interference scenarios. Visualization of monitoring results, alarm notifications, and relevant data through the front-end interface enhances understanding of VDES data link credibility. This framework supports effective surveillance and detection of vulnerabilities, such as radio interference and spoofing sources.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey.

In the modern world of human-computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals.

Silicone Rubber-Packaged FBG Sensing Information and SSI-COV-Recognized Modal Parameters Motivated Damage Identification in Pipe Structures

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the ASMDI damage index, ASMDI exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems.

Identification of physically consistent dynamics parameter of the ABB IRB 360-6/1600 delta robot and its use for time-optimal motion planning under consideration of constraint forces

Model-based control schemes, forward dynamics simulations, constraint force computation and time-optimal motion planning have one major thing in common, they all depend on the dynamics parameters of the system. Physical consistency of the dynamics parameters ensures a positive definite mass matrix and correct constraint forces. The most common inverse dynamics identification method – the base-parameters – lack physical consistency. This paper proposes an identification method to identify physically consistent dynamics parameters for Delta-like robots while further showing the effects of friction in passive joints. A tailored model to compute the crucial constraint forces appearing in the mechanism based on the identified dynamics parameters is derived. This model is used to additionally consider constraint forces besides actuation torques for time-optimal motion planning of a typical pick and place task. This is done without any prior CAD data of the robot from the manufacturer.

Physics-informed deep learning for structural dynamics under moving load

Physics-informed deep learning has emerged as a promising approach that incorporates physical constraints into the model, reduces the amount of data required, and demonstrates robustness and potential in dealing with limited datasets for a variety of studies. However, several key challenges still exist, with one being the spectral bias problem of deep learning in the simulation of functions with multi-frequency features. To overcome the challenge, this study proposes a novel physics-informed deep learning method, which integrates physics-informed neural network with Fourier transform so as to solve partial differential equations in the frequency domain, thus alleviating the problem of spectral bias of neural networks in the simulation of multi-frequency functions. In addition, the proposed method is used to focus on the forward simulation and parameter inverse identification issues in structural dynamics under moving loads. To illustrate the superiority of the method, the issues of dynamic response of simply supported beams under moving loads are presented as case studies, and the performance of the method in multiple cases is analysed and discussed. The research results demonstrate the feasibility and effectiveness of the method for structural dynamics simulation and parameter inverse identifications using limited datasets.

Exploring Machine Learning Approaches for Predicting Lung Cancer: A Comparative Study

Lung cancer is becoming more common, which emphasizes the significance of precise and user-friendly prediction methods for risk assessment and early identification. This research makes use of a dataset (284 cases and 16 attributes) that was acquired from the Online Lung Cancer Prediction System. The main goal is to find out how well machine learning techniques can predict the likelihood of lung cancer and give people useful information for making decisions. The collection contains a wide range of characteristics, such as lifestyle factors, health markers, and demographic data. To thoroughly assess predictive models, the study uses a variety of machine learning methods, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, XGBOOST, CATBOOST, LightGBM and Gradient Boosting. The study also investigates the effects of hyperparameter tuning, visualization strategies, and data preprocessing techniques on model performance. Keywords: Cancer prediction· Machine learning· Ensemble models

A-264 Validation of a Novel Real-Time PCR Assay for Identification of Mycobacterium species Isolates Recovered from Clinical Culture

Abstract Background Historically, clinical mycobacteriology laboratories used the nucleic acid hybridization-based AccuProbe assay manufactured by Hologic, Inc., to rapidly rule in or rule out Mycobacterium tuberculosis (MTB) and members of the M. avium-intracellulare complex (MAC). The clinical value of this test was in its ability to rapidly rule in/out MTB from positive broth cultures and allow the initiation of appropriate antimicrobial therapy and infection prevention practices. Recent discontinuation of this commercial assay left a gap in testing available for the rapid identification of these organisms, which allowed us to develop an in-house real-time PCR test. Methods A replacement assay was developed by Labcorp R & D based on real-time PCR amplification of extracted nucleic acids from positive culture growth. Aliquots from positive AFB VersaTREK broth cultures were heat inactivated, and nucleic acids were extracted using the MagNA Pure 24 (MP24) instrument (Roche). Real-time PCR using published primer probe sets (Rocchetti, et al 2016) was performed on the QuantStudio™ 7 (QS7) instrument (Applied Biosystems®). The assay was designed with three independent primer/probe sequence targets: one for MTB Complex (IS6110), one for MAC (ITS), and one pan-target for the Mycobacterium genus (16s rRNA). Results Procedural verification was performed using 100 residual clinical specimens previously identified using the Hologic AccuProbe® assay. Categorical concordance was 97% (97/100). The three discrepant results were identified as MAC positive by PCR; however, these were not identified as MAC on the predicate assay. Analytical sensitivity for the PCR assay was established to be 1,000 CFU/mL for each target. Analytical specificity was assessed in vitro for nine Mycobacterium species, with no cross-reactivity observed. In vitro analysis of target detection in the presence of yeast (Candida glabrata) was also performed, and we saw no inhibition. Analysis of the primer/probe sets for all targets was performed in silico using XploreDB database of microbial genomes, with confirmed 100% matches for both MAC and MTB genomes compared to hundreds of species. By comparison, the PAN-Myco primer set showed decreased performance, having three mismatches (two on forward primer and one on the probe). As this target sequence is used as an Internal Control (IC) this was not problematic for overall assay performance. The assay demonstrated excellent precision (%CV) for each target: MTB = 0.74%, MAC = 1.10%, PAN-Myco = 3.19%. Conclusions Utilizing the depth of molecular methodologies available in our laboratories, we developed a rapid method for identification of MAC and MTB from positive broth cultures, which had excellent performance compared to the predicate assay. The PCR ID LDT test is now available and allows testing to continue with no interruption in patient care.

Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision

Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, image processing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machine vision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machine vision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challenges and outlining future opportunities. By Integrating state-of-the-art technology with sustainable agricultural practices, implementing an intelligent system offers a viable approach toward efficient and environmentally friendly weed management in modern agriculture.

Machine-Based and Auditory Identification of Slavic Languages

This paper presents a comparison of auditory and machine-based identification of linguistic origins. Two studies were conducted to assess the ability of lay listeners and a stateof-the-art machine approach to identify Slavic L1 from delexicalized speech samples. The first study involved 228 native speakers of the four Slavic languages (Bulgarian, Czech, Polish and Russian) who had not received any prior training in Slavic philology, phonetics, linguistics, or forensic science. Their task was to identify the linguistic origins of speakers when exposed to limited phonetic cues. The stimuli consisted of meaningless logatomes to control for the lexical information. The second study employed machine-based identification of a spoken language, based on two distinct approaches: (1) formant structure of phonetic signal and (2) a neural network and vector representation of speech samples. The data showed that Slavic native speakers, even when exposed to limited auditory cues, are able to identify speakers’ L1s. Interestingly, in the context of the Bulgarian language, the machine-based identification method performed better than the lay listeners. The results of the experiments provide insight into the advantages of hybrid approaches in investigations related to LADO (Language Analyses for the Determination of Origin). Furthermore, the outcomes of this comparison may contribute to the debate on the involvement of native speakers in L1 identification procedures for closely related languages.