Robust Identification Research Articles

An improved robust identification method for position independent geometric errors of the swing axis of the gear grinding machine

An improved robust identification method for position independent geometric errors of the swing axis of the gear grinding machine

A Review of Non-Destructive Raman Spectroscopy and Chemometric Techniques in the Analysis of Cultural Heritage

Today, there is an increasing concern and effort for protection, conservation, and restoration of cultural heritage materials. Non-invasive analytical methodologies such as Raman spectroscopy offers various advantages such as high speed, robust identification, low cost, and in-site analysis. Previous contributions highlighted the potential of Raman spectroscopy combined with multivariate statistics for identification and quality evaluation of cultural heritage materials such as paints, fiber, dyes, woods, stones, inks, and textile materials. Especially, application of chemometrics and multivariate statistics algorithms opens new horizons for scientists and inspectors. In conclusion, the paper provided an overview of the state-of-the-art uses of multivariate statistically equipped Raman spectroscopy methods for evaluation of cultural heritage and art materials with illustrations from previous research studies.

A robust fingerprint identification approach using a fuzzy system and novel rotation method

Forensic science has developed significantly in the last few decades. Its key role is to provide crime investigators with processed data obtained from the crime scene to achieve more accurate results presented in court. Biometrics has proved its robustness against various critical crimes encountered by forensics experts. Fingerprints are the most important biometric used until now due to their uniqueness and production low cost. The automated fingerprint identification system (AFIS) came into existence in the early 1960s through the cooperation of the countries: USA, UK, France, and Japan. Ever since it started to develop gradually because of the challenges found at the crime scenes such as fingerprints distortions and partial cuts which in turn can severely affect the final calculations made by experts. The vagueness of the results was the main motivation to build a robust fingerprint identification system that introduces new and enhanced methods in its stages to help experts make more accurate decisions. The proposed fingerprint identification system uses Fourier domain analysis for image enhancement, then the system cuts the image around the core point after applying the rotation and core point detection methods. After that, it calculates the similarity based on the distance between fingerprint histograms extracted using the Local Binary Pattern (LBP). The system's last step is to translate the results into a sensible form where it utilizes fuzziness to provide more possibilities for the answer. The proposed identification system showed high efficiency on FVC 2002 and FVC 2000 databases. For instance, the results of applying our system on FVC 2002 provided a set of three ordered matching candidates such that 97.5 % of the results provided the correct candidate as the first order, and the rest of 2.5 % provided the correct candidate as the second order.

Dynamic Unbalance Identification in Steady-State Rotating Machinery: A Hybrid Methodology Integrating Physical and Data-Driven Techniques

Rotating machinery plays a strategic role in key industrial sectors, making its analysis a subject of great interest for both academia and industry. Effective maintenance planning for this equipment is essential for asset management and for meeting current industrial requirements. To address this demand, this work presents a novel unbalance identification approach based on a digital representation of a rotating machine's dynamic behavior in relation to the development of specific faults. A hybrid methodology is proposed, integrating Finite Element Modeling, a Kalman Filter for parameter estimation, and Referenced Moving Window Principal Component Analysis. This Principal Component Analysis extension enhances vibration pattern recognition, enabling accurate fault quantification directly from slight changes in the system's steady-state behavior. The methodology uniquely eliminates the need for phase angle measurements, facilitating continuous monitoring of unbalance progression in steady-state conditions. Two case studies demonstrate the methodology's potential: one utilizing synthetic data from a Floating Production Storage and Offloading centrifugal compressor unit, and the other based on real data from a hydroelectric turbine-generator. These studies illustrate the integration of computational modeling, data-driven analysis, and monitored vibration data, achieving robust and accurate unbalance identification. This approach provides valuable insights into current capabilities and opens promising pathways for future applications, particularly in the digital twin domain for rotating machinery.

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops.

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

The Unique Behavior of Vertical Velocity in Developing Deep Convection

AbstractWhen daytime tropical convection develops away from mesoscale disturbances, it typically transitions gradually from dry to shallow to deep convection on hourly timescales. The transition is commonly associated with the formation of larger horizontal boundary‐layer structures and an increasing level of cloud organization aloft. This study demonstrates that a spectral analysis of the resolved high‐resolution sub‐cloud flow features allows for a robust identification of dominant length scales and the quantification of their growth rates during the transition. Furthermore, it is shown that temperature, moisture, and horizontal winds develop multiple length scales with the largest ones growing up to several kilometers in magnitude. However, the vertical velocity behaves in a distinct manner developing significantly smaller values, comparable over land and ocean. This indicates stronger inherent limits of vertical velocity to self‐organize by size.

Comparison of Multi-Armed Bandit Algorithms in Advertising Recommendation Systems

In today's rapidly evolving online environment, advertising recommendation systems utilize multi-armed bandit algorithms like dynamic collaborative filtering Thompson sampling (DCTS), upper confidence bound based on recommender system (UCB-RS), and dynamic -greedy algorithm (DEG) to optimize ad displays and enhance click-through rates (CTR). These algorithms must adapt to limited information and update strategies based on immediate feedback.This study employs an experimental comparison to assess the performance of the DCTS, UCB-RS, and DEG algorithms using the click-through rate prediction database from Kaggle. Five experimental sets under varied parameter settings were analyzed, employing the Receiver Operating Characteristic (ROC) curve, accuracy, and area under the curve (AUC) metrics.Results show that the DEG algorithm consistently outperforms the others, achieving higher AUC values and demonstrating robust sample identification capabilities. DEG also exhibits superior precision at high recall levels, showcasing its potential in dynamic advertising environments. Its dynamic adjustment strategy effectively balances exploration and exploitation, optimizing ad displays.The findings suggest that DEG's adaptability and stability make it particularly suitable for dynamic ad recommendation scenarios. Future research should focus on optimizing DEG's parameter settings and possibly integrating UCB-RS's exploration mechanisms to enhance performance and develop more effective strategies for advertising recommendation systems.

Robustness Assessment of Oncology Dose-Finding Trials Using the Modified Fragility Index.

The sample sizes of phase I trials are typically small; some designs may lead to inaccurate estimation of the maximum tolerated dose (MTD). The objective of this study was to propose a metric assessing whether the MTD decision is sensitive to enrolling a few additional subjects in a phase I dose-finding trial. Numerous model-based and model-assisted designs have been proposed to improve the efficiency and accuracy of finding the MTD. The Fragility Index (FI) is a widely used metric quantifying the statistical robustness of randomized controlled trials by estimating the number of events needed to change a statistically significant result to non-significant (or vice versa). We propose a modified Fragility Index (mFI), defined as the minimum number of additional participants required to potentially change the estimated MTD, to supplement existing designs identifying fragile phase I trial results. Three oncology trials were used to illustrate how to evaluate the fragility of phase I trials using mFI. The results showed that two of the trials were not sensitive to additional subjects' participation while the third trial was quite fragile to one or two additional subjects. The mFI can be a useful metric assessing the fragility of phase I trials and facilitating robust identification of MTD.

Rapid and Robust Identification of Sepsis Using SeptiCyte RAPID in a Heterogeneous Patient Population.

Background/Objective: SeptiCyte RAPID is a transcriptional host response assay that discriminates between sepsis and non-infectious systemic inflammation (SIRS) with a one-hour turnaround time. The overall performance of this test in a cohort of 419 patients has recently been described [Balk et al., J Clin Med 2024, 13, 1194]. In this study, we present the results from a detailed stratification analysis in which SeptiCyte RAPID performance was evaluated in the same cohort across patient groups and subgroups encompassing different demographics, comorbidities and disease, sources and types of pathogens, interventional treatments, and clinically defined phenotypes. The aims were to identify variables that might affect the ability of SeptiCyte RAPID to discriminate between sepsis and SIRS and to determine if any patient subgroups appeared to present a diagnostic challenge for the test. Methods: (1) Subgroup analysis, with subgroups defined by individual demographic or clinical variables, using conventional statistical comparison tests. (2) Principal component analysis and k-means clustering analysis to investigate phenotypic subgroups defined by unique combinations of demographic and clinical variables. Results: No significant differences in SeptiCyte RAPID performance were observed between most groups and subgroups. One notable exception involved an enhanced SeptiCyte RAPID performance for a phenotypic subgroup defined by a combination of clinical variables suggesting a septic shock response. Conclusions: We conclude that for this patient cohort, SeptiCyte RAPID performance was largely unaffected by key variables associated with heterogeneity in patients suspected of sepsis.

Rapid Appraisal of Wildlife Corridor Viability with Geospatial Modelling and Field Data: Lessons from Makuyuni, Tanzania

Connectivity between protected areas is necessary to prevent habitat fragmentation. Biodiverse countries like Tanzania craft legislation to promote habitat connectivity via the creation of ecological corridors, but their viability for wildlife often remains unknown. We therefore develop a scalable and replicable approach to assess and monitor multispecies corridor viability using geospatial modeling and field data. We apply and test the approach in the Makuyuni study area: an unprotected ecological corridor connecting Tarangire National Park to Essmingor mountain, Makuyuni Wildlife Park and Mto Wa Mbu Game Controlled Area. We analyzed the viability of Makuyuni as an ecological corridor by creating and validating a geospatial least-cost corridor model with field observations of wildlife and livestock. We created the model from publicly available spatial datasets augmented with manual digitization of pastoral homesteads (bomas). The least-cost corridor model identified two likely pathways for wildlife, confirmed and validated with field observations. Locations with low least-cost values were significantly correlated with more wildlife observations (Spearman’s rho = −0.448, p = 0.002). Our findings suggest that Makuyuni is a viable ecological corridor threatened by development and land use change. Our methodology presents a replicable approach for both monitoring Makuyuni and assessing corridor viability more generally. The incorporation of manually digitized homesteads (bomas) and field-based livestock observations makes corridor assessment more robust by taking into account pastoral land uses that are often missing in land cover maps. Integration of geospatial analysis and field observations is key for the robust identification of corridors for habitat connectivity.

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.

Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data

Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.

Tracking Differentiator-Based Identification Method for Temperature Predictive Control of Uncooled Heating Processes

The temperature control of uncooled heating processes presents challenges due to a substantial lag and the absence of active cooling mechanisms, which can lead to overshoot and oscillations. To address these issues, we propose an anti-disturbance identification method based on a tracking differentiator (TD) and an input-constrained temperature predictive control (ICTPC) strategy. Our approach specifically considers the impact of unknown disturbances on model identification within a second-order heating process. By employing a TD to differentiate the input and output signals, we effectively minimize the identification error caused by low-frequency disturbances, yielding a robust anti-disturbance identification technique. Following this, we establish input constraints to limit the amplitude and variation of the control input, ensuring a more controlled and predictable system response. Using the identified model, an ICTPC algorithm is designed to achieve stable temperature control in uncooled heating processes. Experimental results from a typical uncooled heating system demonstrate that our method not only significantly reduces overshoot but also effectively mitigates temperature fluctuations, leading to enhanced control performance and system stability. This study provides a practical solution for temperature control in systems without cooling capabilities, offering substantial improvements in the efficiency and quality of industrial production processes.

Robust Bilinear Form Identification: A Subgradient Method with Geometrically Decaying Stepsize in the Presence of Heavy-Tailed Noise

Robust Bilinear Form Identification: A Subgradient Method with Geometrically Decaying Stepsize in the Presence of Heavy-Tailed Noise

Integrated analysis of additive and multiplicative internal excitation parameters in misaligned-bowed-internally damped rotor systems with active magnetic bearings: Numerical and experimental identification

Precisely identifying internal excitation parameters within rotor-bearing systems is imperative for ensuring reliability and optimal performance. This paper meticulously addresses various issues, such as coupling misalignment, residual bow, internal damping, and imbalance. However, the novelty of the work lies in its detailed exploration of the interplay between these parameters, particularly emphasizing the multiplicative effect resulting from residual bow and misalignment. Introducing an innovative approach, the paper employs an active magnetic bearing (AMB) near the output rotor's disc to mitigate vibrations and enhance system stability. A robust identification algorithm is developed, leveraging least-squares fitting in the frequency domain to accurately estimate multiple internal excitation parameters and system characteristics. These parameters encompass viscous damping, internal damping, unbalance, residual bow, static and dynamic coupling misalignment, multiplicative force, and various AMB constants. The algorithm's resilience is demonstrated through rigorous testing against noise percentage errors. Furthermore, practical experimentation using a laboratory test rig validates its efficacy. Response data from proximity probes are inputted into the algorithm, successfully identifying parameters. Experimental validation is achieved by comparing irregularities in orbit plots and full spectra with numerical simulations based on the identified parameters, affirming the algorithm's reliability in real-world scenarios. This comprehensive approach offers a dependable solution for internal excitation identification in complex rotor systems.

Bioinspired interface-mediated multichannel sensor array for rapid and robust identification of bacteria

Bioinspired interface-mediated multichannel sensor array for rapid and robust identification of bacteria

Integrating an incident dataset with a question and answering language model to assist hazard identification: Comparison of an extractive and generative model

Robust hazard identification (HAZID) relies upon extensive knowledge of the system being analysed, the technical aspects, and how it will be used operationally. Typically, this knowledge is held by human participants who can draw out answers in natural language to hazard related questions based upon their own experience. However, several threats exist to this, such as high staff turnover, a poor learning from incidents capability or even insufficient Information Technology resources. Alternatively, incident databases hold vast amounts of hazard information that can be transformed into a source of knowledge. As mitigation to the aforementioned issues, this paper presents a Question and Answering (Q&A) Bidirectional Encoder Representations from Transformers (BERT) language model trained upon aviation incidents and a unique Q&A dataset. The model can extract answers to typical HAZID questions, based upon factual incident reports. Alongside this extractive approach, the paper also explores the use of a generative Large Language Model combined with an incident dataset. Both models proved a useful addition to HAZID activities based upon the Structured What If Technique (SWIFT), answering safety-themed questions based upon a retrieved context of incident reports that semantically matched the query. For the purposes of HAZID, it was suggested that the generative option is preferable based upon its ease of implementation, lower resource requirements and quality of responses. Additionally, it is shown that it is possible for organisations to train and create their own custom models for HAZID purposes. Future work may wish to consider the application of models that can hypothesize scenarios based upon incident reports, building further understanding to the relationships between causes, hazards and consequences.

FederEI: Federated Library Matching Framework for Electron Ionization Mass Spectrum Based Compound Identification.

Mass spectrometry (MS) is a powerful tool for compound structure elucidation, crucial in various domains including pharmaceuticals, chemical synthesis, environmental analysis, and metabolomics. Compound identification via spectral matching relies heavily on reference libraries, yet existing universal libraries may lack comprehensiveness. Laboratories often create their own libraries, leading to fragmentation within the scientific community. Addressing this, we propose FederEI, a federated library matching solution for EI-MS-based compound identification. FederEI enables decentralized compound identification while preserving data privacy, achieved through a server-to-server connection framework. Each laboratory retains control over its spectral data, enhancing privacy and minimizing data transmission. FederEI integrates a fast and accurate library matching algorithm, offering comparable performance to local matching while maintaining data security. Evaluation against the classic approach demonstrates FederEI's effectiveness, ensuring robust compound identification.

Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex challenge with high-dimensional data, feature selection is vital for identifying the key predictors that can facilitate effective and targeted interventions. This study aims to utilize feature selection process to identify a robust and minimal set of predictors that can aid in the early prediction of short- and long-term weight problems in children and adolescents. We utilized demographic, physical, and psychological wellbeing predictors to model weight status (normal, underweight, overweight, and obese) for 1-, 3-, and 5-year periods. To select the most influential features, we employed four feature selection methods: (1) Chi-Square test; (2) Information Gain; (3) Random Forest; (4) eXtreme Gradient Boosting (XGBoost) with six ML approaches. The stability of the feature selection methods was assessed by Jaccard's index, Spearman's rank correlation and Pearson's correlation. Model evaluation was performed by various accuracy metrics. With 3,862,820 million student-visits were included in this population-based study, the mean age of 11.6 (SD = 3.64) for the training set and 10.8 years (SD = 3.50) for the temporal test set. From the initial set of 38 predictors, we identified 6, 9, and 13 features for 1-, 3-, and 5-year predictions, respectively, by the best performed feature selection method of Chi-Square test in XGBoost models. These feature sets demonstrated excellent stability and achieved prediction accuracies of 0.82, 0.73, and 0.70; macro-AUCs of 0.94, 0.86, and 0.83; micro-AUCs of 0.96, 0.93, and 0.92 for different prediction windows, respectively. Weight, height, sex, total score of self-esteem, and age were consistently the most influential predictors across all prediction windows. Additionally, several psychological and social wellbeing predictors showed relatively high importance in long-term weight status prediction. We demonstrate the potential of ML in identifying key predictors of weight status in children and adolescents. While traditional anthropometric measures remain important, psychological and social wellbeing factors also emerge as crucial predictors, potentially informing targeted interventions to address childhood and adolescence weight problems.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Abstract The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.