Traditional Identification Methods Research Articles

Identification and application of dynamic parameters of manipulator based on improved IRLS algorithm

Purpose To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm. Design/methodology/approach First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data. Findings Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently. Originality/value It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

Cross-species single-cell analysis reveals divergence and conservation of peripheral blood mononuclear cells

BackgroundSingle-cell transcriptome sequencing (scRNA-seq) has revolutionized the study of immune cells by overcoming the limitations of traditional antibody-based identification and isolation methods. This advancement allows us to obtain comprehensive gene expression profiles from a diverse array of vertebrate species, facilitating the identification of various cell types. Comparative immunology across vertebrates presents a promising approach to understanding the evolution of immune cell types. In this study, we conducted a comparative transcriptome analysis of peripheral blood mononuclear cells (PBMCs) at the single-cell level across 12 species.ResultsOur findings shed light on the cellular compositional features of PBMCs, spanning from fish to mammals. Notably, we identified genes that exhibit vertebrate universality in characterizing immune cells. Moreover, our investigation revealed that monocytes have maintained a conserved transcriptional regulatory program throughout evolution, emphasizing their pivotal role in orchestrating immune cells to execute immune programs.ConclusionsThis comprehensive analysis provides valuable insights into the evolution of immune cells across vertebrates.

Improved CSW-YOLO Model for Bitter Melon Phenotype Detection

As a crop with significant medicinal value and nutritional components, the market demand for bitter melon continues to grow. The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm resources crucial for breeding work. To address the limitations of time-consuming and less accurate traditional manual identification methods, there is a need to enhance the automation and intelligence of bitter melon phenotype detection. This study developed a bitter melon phenotype detection model named CSW-YOLO. By incorporating the ConvNeXt V2 module to replace the backbone network of YOLOv8, the model’s focus on critical target features is enhanced. Additionally, the SimAM attention mechanism was introduced to compute attention weights for neurons without increasing the parameter count, further enhancing the model’s recognition accuracy. Finally, WIoUv3 was introduced as the bounding box loss function to improve the model’s convergence speed and positioning capabilities. The model was trained and tested on a bitter melon image dataset, achieving a precision of 94.6%, a recall of 80.6%, a mAP50 of 96.7%, and an F1 score of 87.04%. These results represent improvements of 8.5%, 0.4%, 11.1%, and 4% in precision, recall, mAP50, and F1 score, respectively, over the original YOLOv8 model. Furthermore, the effectiveness of the improvements was validated through heatmap analysis and ablation experiments, demonstrating that the CSW-YOLO model can more accurately focus on target features, reduce false detection rates, and enhance generalization capabilities. Comparative tests with various mainstream deep learning models also proved the superior performance of CSW-YOLO in bitter melon phenotype detection tasks. This research provides an accurate and reliable method for bitter melon phenotype identification and also offers technical support for the visual detection technologies of other agricultural products.

Post‐earthquake structural damage identification and safety evaluation using point clouds

AbstractEfficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.

Microelectrode and microchannel integrated micro-robotic arm for bacteria sensing and on-demand drug delivery

Traditional methods such as polymerase chain reaction (PCR) face significant limitations in identifying unknown bacterial targets and in the detection of live multi-strain bacterial coinfections. These methods often fail to offer real-time solutions and precise treatments for diverse bacterial communities present in situ. To address these challenges, we introduce a novel micro-robotic arm system equipped with an integrated sensing electrode and a microchannel. This system combines a scanning electrochemical probe and a microfluidic channel to enable simultaneous biosensing and on-demand drug delivery. The dual functionality of this device successfully addresses the limitations of traditional bacterial identification methods by providing real-time detection and treatment capabilities. Specifically, the technology monitors oxygen levels to detect aerobic bacterial contamination and initiates immediate drug delivery upon detection. The efficacy of this approach was validated using porcine eyeballs, artificial wound samples, and fabric gauzes. The results demonstrate accurate identification of aerobic bacteria and effective pinpointed treatment with a hydrogel-based therapeutic. This innovative method, merging electrochemical sensing with precise drug delivery, presents a significant advancement in managing multi-strain bacterial coinfections, offering a groundbreaking solution to a persistent clinical challenge.

RSNC-YOLO: A Deep-Learning-Based Method for Automatic Fine-Grained Tuna Recognition in Complex Environments

Tuna accounts for 20% of the output value of global marine capture fisheries, and it plays a crucial role in maintaining ecosystem stability, ensuring global food security, and supporting economic stability. However, improper management has led to significant overfishing, resulting in a sharp decline in tuna populations. For sustainable tuna fishing, it is essential to accurately identify the species of tuna caught and to count their numbers, as these data are the foundation for setting scientific catch quotas. The traditional manual identification method suffers from several limitations and is prone to errors during prolonged operations, especially due to factors like fatigue, high-intensity workloads, or adverse weather conditions, which ultimately compromise its accuracy. Furthermore, the lack of transparency in the manual process may lead to intentional underreporting, which undermines the integrity of fisheries’ data. In contrast, an intelligent, real-time identification system can reduce the need for human labor, assist in more accurate identification, and enhance transparency in fisheries’ management. This system not only provides reliable data for refined management but also enables fisheries’ authorities to dynamically adjust fishing strategies in real time, issue timely warnings when catch limits are approached or exceeded, and prevent overfishing, thus ultimately contributing to sustainable tuna management. In light of this need, this article proposes the RSNC-YOLO algorithm, an intelligent model designed for recognizing tuna in complex scenarios on fishing vessels. Based on YOLOv8s-seg, RSNC-YOLO integrates Reparameterized C3 (RepC3), Selective Channel Down-sampling (SCDown), a Normalization-based Attention Module (NAM), and C2f-DCNv3-DLKA modules. By utilizing a subset of images selected from the Fishnet Open Image Database, the model achieves a 2.7% improvement in mAP@0.5 and a 0.7% improvement in mAP@0.5:0.95. Additionally, the number of parameters is reduced by approximately 30%, and the model’s weight size is reduced by 9.6 MB, while maintaining an inference speed comparable to that of YOLOv8s-seg.

Advancements in DNA Metabarcoding Protocols for Monitoring Zooplankton in Marine and Brackish Environments

Over the past century, numerous studies have proposed various organisms for the biomonitoring of aquatic systems, but only recently has zooplankton emerged as a promising indicator of water quality. The traditional identification methods, however, can be inefficient in the context of monitoring efforts, as they are often time consuming and costly. DNA metabarcoding offers a powerful alternative, providing a more efficient and reliable approach to monitor zooplankton communities. In this review, we assess the current state-of-the-art methodologies used to evaluate marine and brackish zooplankton communities through the DNA metabarcoding workflow. While several emerging approaches have been reported, no standardization has been achieved so far. The DNA extraction step has gained the most consensus, with the widespread use of commercial kits (DNeasy Blood & Tissue kit employed in ca. 25% of the studies), though there is still a significant variation in kit selection. Additionally, 18S and COI were the main molecular markers employed (ca. 61% and 54%, respectively) though the target region varied in the former. Moreover, many methodologies, particularly those used for processing zooplankton samples, lack practical validation. Some studies also fail to provide sufficient detail in their methodology descriptions hindering reproducibility. Overall, DNA metabarcoding shows great potential for the efficient monitoring of zooplankton communities, but further effort is needed to establish standardized practices and optimize the current approaches across the entire methodological pipeline.

Score-based likelihood ratios for barefootprint evidence using deep learning features.

As the court put forward higher requirements for quantitative evaluation and scientific standards of forensic evidence, how to objectively and scientifically express identification opinions has become a challenge for traditional forensic identification methods. Score-based likelihood ratios are mathematical methods for quantitative evaluation of forensic evidence. However, due to the subtle differences in inter-class barefootprints, there is no automatic barefootprints matching algorithm with high accuracy under large-scale dataset validation, and there are few studies related to deep learning barefootprint features for evidence evaluation in court. Therefore, score-based likelihood ratios for barefootprint evidence using deep learning features are proposed by this paper. Firstly, the largest barefootprint dataset (BFD) is constructed, which contains 54,118 barefootprint images from 3000 individuals. Then, an automatic barefootprint feature extraction and matching algorithm is proposed, which achieves a retrieval accuracy of 98.4% on BFD and an AUC of 0.989 for barefootprint validation. Next, Cosine distance, Euclidean distance and Manhattan distance are employed to measure the comparison scores between intra-class and inter-class barefootprints using deep learning features in four dimensions of 64, 128, 512 and 1024, respectively. The performance of proposed model is evaluated by comparing the values and the Tippett plot. Finally, simulated crime scene barefootprint samples are constructed to verify the practical application of the proposed method, which provide further support for the quantitative evaluation of barefootprint evidence in court.

A Data-Driven Model for Classification of Traditional Chinese Medicine Materials

This paper proposes a data-driven classification model for traditional Chinese medicinal herbs based on mid-infrared spectral data. Addressing the limitations of traditional identification methods when the herbs’ appearance is damaged or incomplete, this study employs machine learning techniques to achieve accurate classification of medicinal herb types through spectral data preprocessing and feature extraction. Firstly, the Savitzky-Golay convolution smoothing method and Standard Normal Variate (SNV) transformation were used for denoising the spectral data. Then, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional spectral data and extract the key features. Finally, the Gaussian Mixture Model (GMM) was applied to cluster the reduced data, categorizing the medicinal herbs into six classes. The results show that this method produces the accurate and stable classification. The constructed model is not only applicable to the classification and origin identification of medicinal herbs but also provides an important reference value for the classification and origin identification of other plant species.

Enhancing mosquito classification through self-supervised learning

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.

Phenotypic and Molecular Identification of M.Canis

The current study was conducted to compare morphological and molecular identification methods for the fungus M.canis isolated from 42 patients with dermatophytosis who were clinically diagnosed by a dermatologist, after review by a consultant dermatologist and venereologist in Al-Muthanna Governorate at Al-Hussein Teaching Hospital. In the period from January to November 2024, using traditional methods of isolation and laboratory identification, and molecular methods using polymerase chain reaction (PCR) technology.

Bacterial profile-based body fluid identification using a machine learning approach.

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction. This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches. Bacterial profiles were determined by sequencing the hypervariable region of the 16S rRNA gene,using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples. The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen. Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

Medical image recognition based on multilayer neural network

As a common accidental injury, accurate identification of burns and scalded injuries is of great significance to the development of treatment programs and prognosis assessment. Traditional identification methods are subjective and inaccurate, this paper proposes a burns and scalds grade identification technique based on multilayer neural network. The three-degree classification standard of burns and scalds is described, and the structure and principle of multilayer neural network are introduced, including the network structure composed of input layer, hidden layer and output layer, forward propagation, back propagation and training process. The implementation steps of burns and scald grade recognition technology based on multi-layer neural network are discussed in detail, such as data acquisition, image pre-processing, feature extraction, classifier design, model training and evaluation. Through experiments, the model is tested using a dataset containing 362 burns and scalds images, and the model achieves more than 90% accuracy on the test set with high accuracy and reliability. The multi-layer neural network model is used to classify the burn and scald data, which improves the diagnostic accuracy, shortens the delay time of the disease, reduces the pressure of professional doctors, and helps patients have a preliminary understanding of the degree of burn.

Low-resource dynamic loading identification of nonlinear system using pretraining

To address the challenge of load identification in nonlinear systems, an audio neural network-based method called WaveNet is proposed that leverages its capability to capture long-term dependencies in mechanical systems, enabling accurate load identification. Unlike traditional dynamic load identification methods that often encounter difficulties with matrix solutions, this approach takes advantage of WaveNet’s capabilities, enhancing both accuracy and efficiency. We integrate pre-training and transfer learning techniques to address the data scarcity challenges often encountered in real-world engineering applications. By transferring features across distributed datasets, this method reduces the dependency on single-task data, thereby improving model robustness. The performance of the WaveNet method is rigorously evaluated against traditional benchmarks such as Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) benchmarks under random load conditions applied to a complex structural framework. The proposed method achieves a root mean squared error (RMSE) of 1.521 and a determination coefficient (R²) of 0.996 in the random load case, demonstrating superior accuracy compared to other approaches. Moreover, its applicability is verified through simulations of both impact load and harmonic load scenarios, showcasing the effectiveness of transfer learning in overcoming domain discrepancies. Finally, the method is tested in random experiments to validate its engineering applicability. The results highlight the significant accuracy improvement in low-resource tasks achieved through pre-training, showcasing the potential and value of the proposed method.

A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Fault identification of rolling bearings plays a crucial role in maintaining the efficient and stable operation of equipment. Although traditional fault identification methods have made certain progress, they still lack in model feature extraction capabilities and generalization ability. In this paper, a frequency channel-attention based vision Transformer method is proposed for rolling bearings intelligent fault identification. Using frequency domain channel-attention mechanism, the proposed method is able to preserve fundamental fault information and integrate the frequency characteristics of the vibration signals. The proposed method also leverages the inherent self-attention mechanism of vision Transformer to recognize long-range dependencies within the signal data. This integration of attention not only enhances the model’s sensitivity to signal frequency characteristics but also enables the visualization of the attention mechanism, thereby increasing the model’s interpretability. Additionally, a shift linear layer is proposed to reduce the model’s computational demands while maintaining its robust feature extraction capabilities. This proposed method directly uses the collected vibration raw signals to achieve precise fault identification of rolling bearings, and experimental validation on two datasets demonstrates the model’s diagnostic accuracy under across working conditions.

Automated Bird Species Identification Through Machine Learning Techniques

The taxonomy of bird species is fundamental to ecological research, conservation efforts, and biodiversity monitoring. Traditional identification methods, which rely on field notes and visual assessments by trained ornithologists, are often labor-intensive, time-consuming, and prone to error. In recent years, machine learning algorithms and pre-trained models such as ResNet, Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT) have shown significant promise in automating bird species classification. This study explores the application of these advanced models in identifying bird species from visual data, discussing key challenges, methodologies, and the potential to achieve high classification accuracy with reliable confidence scores. By leveraging deep learning techniques, we aim to enhance the precision and scalability of bird taxonomy, supporting more efficient ecological studies and conservation practices.

Multitask-Transfer-Learning Method for Random-Force Frequency Identification Considering Multisource Uncertainties

Accurate reconstruction of unknown external forces from measurable responses is critical for ensuring structural safety and minimizing maintenance costs of aircraft structures. This paper presents a novel multitask-transfer-learning method for random-force frequency identification that accounts for modeling and measurement uncertainties. A data-driven convolutional neural network (CNN) model is utilized to capture the relationship between the power spectral densities of external forces and measured responses, addressing the inherent ill-posedness of traditional model-driven force identification methods. To shorten the frequency-dependent training time in the full frequency domain, a transfer-learning strategy is implemented, fine-tuning hyperparameters from a CNN model trained at one source frequency to another target frequency. Furthermore, an iterative dimensionwise collocation method based on nonprobabilistic interval modeling is introduced to quantify the uncertain boundaries of external loads caused by multisource uncertainties. By incorporating a multitask-learning framework, the process of establishing CNN models for collocated samples is accelerated, reducing the computational effort for uncertainty quantification. The proposed method is validated through both numerical and experimental examples, demonstrating its accuracy, robustness, and computational efficiency for force identification in the full frequency domain, even under conditions of insufficient measurements, measurement noises, and material dispersions.

Non-destructive and rapid identification of yeasts by nearinfrared spectroscopy and machine learning

This study aimed to apply near-infrared (NIR) spectroscopy combined with machine learning techniques to identify yeast strains rapidly and practically, comparing the results with traditional molecular identification methods. Yeasts were isolated from the digestive tracts of aquatic mining insects collected in the extreme north of the Western Amazon (Roraima), Brazil, and preserved through cryopreservation and mineral oil methods. Molecular identification involved PCR amplification and sequencing of ribosomal DNA regions. NIR spectroscopy, coupled with multivariate analysis and machine learning algorithms such as principal component analysis (PCA), hierarchical cluster analysis (HCA), k-nearest neighbor (KNN), and soft independent modeling by class analogy (SIMCA), was used to analyze and classify the yeast samples, accurately identified yeast strains at the genus and species levels, achieving 100% accuracy in both the calibration and validation sets. The results indicate that this method provides a rapid, non-destructive, and environmentally friendly alternative to traditional molecular techniques, making it suitable for real-time, in situ analysis with minimal sample manipulation.

A Mechanical Fault Identification Method for On-Load Tap Changers Based on Hybrid Time—Frequency Graphs of Vibration Signals and DSCNN-SVM with Small Sample Sizes

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference.

Research on improved dynamic load identification method based on Kalman filter under noise interference

As a mathematical approach for solving inverse problems, the dynamic load identification method is particularly useful when directly measuring the load acting on a structure is challenging. The Kalman filter (KF) algorithm is commonly employed for dynamic load identification. However, in practice, measurement noise can significantly affect the accuracy of the KF algorithm's results. In this study, we investigate the impact of response noise on the accuracy of load identification results using the KF algorithm. To eliminate the influence of measurement noise on identification accuracy, we propose an improved dynamic load identification method. This method incorporates adaptive techniques to assess varying levels of response noise and improve the load identification accuracy. Notably, this method is applicable in both physical and modal domains. We conduct numerical simulations using a 5-DOF system under different noise conditions. Compared to traditional dynamic load identification method based on KF algorithm, the improved method consistently achieves superior accuracy in load identification across various noise levels. Moreover, an experiment is conducted to further validate the feasibility of the proposed method. The results showed that the proposed method is effective and can achieve high-precision identification of unknown loads.