Model Identification Research Articles

Proposal of MRFScore and a Regression Model for Identification of Music Relationship Indicator

Proposal of MRFScore and a Regression Model for Identification of Music Relationship Indicator

An underwater wake sensing method based on Micro Thermoelectric Generator for enhancing recognition accuracy

The accidental entry and poaching of foreign boats and autonomous underwater vehicles (AUVs) have a detrimental impact on the ecological environment of marine ranching. Currently, there is a need for a monitoring method specifically designed to monitor underwater moving objects (UMOs), such as foreign boats and AUVs, in real-time within marine ranching. Therefore, this paper creatively proposes a method for monitoring UMOs based on thermal tactile of Micro Thermoelectric Generator (MTEG). A thermal tactile perceptual model for propeller wake identification is established and the wake sensing sensor (WSS) is designed. Furthermore, the performance verification experiment of wake sensing and the target classification experiment based on machine learning are carried out to prove the feasibility of the method. The experimental results demonstrate that the propeller wakes are identified with an accuracy of more than 90% within 30s in the monitoring terminal. Besides, the method can accurately distinguish between propeller wakes and fish wakes, thereby avoiding false alarms. The reported underwater wake sensing method is a new solution to real-time monitoring and safety early warning of marine ranching.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair. With its lightweight design, high load-bearing capacity, and smooth surface, the coupled-drive joint is particularly well suited for these robots. However, the coupled nature of the joint disrupts the direct linear relationship between the input and output torques, posing challenges for dynamic modeling and practical applications. This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics. Building on this foundation, the Newton-Euler method was used to develop a dynamic model for the entire robotic arm. A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero. An experimental method was designed to compensate for gravity, inertia, and modeling errors to identify the parameters of the friction model. This method establishes a mapping relationship between the friction force and motor current. In addition, a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm. Trajectory tracking experiments were conducted during the experimental validation phase, demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm. This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms, thereby establishing a foundation for motion control in humanoid nursing robots.

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

Identification of a lumped-parameter model of the intervertebral joint from experimental data

Through predictive simulations, multibody models can aid the treatment of spinal pathologies by identifying optimal surgical procedures. Critical to achieving accurate predictions is the definition of the intervertebral joint. The joint pose is often defined by virtual palpation. Intervertebral joint stiffnesses are either derived from literature, or specimen-specific stiffnesses are calculated with optimisation methods. This study tested the feasibility of an optimisation method for determining the specimen-specific stiffnesses and investigated the influence of the assigned joint pose on the subject-specific estimated stiffness. Furthermore, the influence of the joint pose and the stiffness on the accuracy of the predicted motion was investigated. A computed tomography based model of a lumbar spine segment was created. Joints were defined from virtually palpated landmarks sampled with a Latin Hypercube technique from a possible Cartesian space. An optimisation method was used to determine specimen-specific stiffnesses for 500 models. A two-factor analysis was performed by running forward dynamic simulations for ten different stiffnesses for each successfully optimised model. The optimisations calculated a large range of stiffnesses, indicating the optimised specimen-specific stiffnesses were highly sensitive to the assigned joint pose and related uncertainties. A limited number of combinations of optimised joint stiffnesses and joint poses could accurately predict the kinematics. The two-factor analysis indicated that, for the ranges explored, the joint pose definition was more important than the stiffness. To obtain kinematic prediction errors below 1 mm and 1° and suitable specimen-specific stiffnesses the precision of virtually palpated landmarks for joint definition should be better than 2.9 mm.

Identification of cracks and mathematical model of the magnetostatic field of defects in a plate

A mathematical model of defects in the inner surface of a ferromagnetic plate is presented. The model compares crack-type defects with wide-opening corrosion defects.

Minimal model identification of drum brake squeal via SINDy

AbstractThe industrial standard in the design and development process of NVH(Noise Vibration Harshness) characteristic of brakes is the application of Finite Element(FE) models with a high number of degrees of freedom in the range of one or several millions. Nevertheless, parallel experimental investigations are still indispensable. On the other hand, minimal models with, due to the inclusion of the self-excitation process, at least two degrees of freedom are well known to be capable to explain qualitatively phenomena as instability of the desired non-vibrating solution or limit cycle oscillation but are in general very inaccurate in predicting the dynamics of a specific real brake. This is because the underlying physical assumptions are already too restrictive and model parameters (especially those referring to nonlinearities) are widely unknown. To overcome this problem, the data-driven modeling approach SINDy(Sparse Identification of Nonlinear Dynamics) is applied to identify appropriate nonlinear functions for a brake squeal minimal model. A problem thereby is the limited database. It turns out that the naive implementation of the method yielding the lowest possible residuum does not necessarily provide physically meaningful models and results, respectively. Instead, a constrained model that incorporates physical knowledge is used to robustly identify parameters and reproduce realistic dynamic behavior. Thereby, several appropriate models with coexisting limit cycles and stationary equilibrium are identified. In particular, it was found that the angular position of the brake drum has a significant influence on the model parameters and therefore must be taken into account in a model with long-term validity.

SYSTCM: A systemic web platform for objective identification of pharmacological effects based on interplay of “traditional Chinese Medicine-components-targets”

Mechanism analysis is essential for the use and promotion of Traditional Chinese Medicine (TCM). Traditional methods of network analysis relying on expert experience lack an explanatory framework, prompting the application of deep learning and machine learning for objective identification of TCM pharmacological effects. A dataset was used to construct an interacted network graph between 424 molecular descriptors and 465 pharmacological targets to represent the relationship between components and pharmacological effects. Subsequently, the optimal identification model of pharmacological effects (IPE) was established through convolution neural networks of GoogLeNet structure. The AUC values are greater than 0.8, MCC values are greater than 0.7, and ACC values are greater than 0.85 across various test datasets. Subsequently, 18 recognition models of TCM efficacy (RTE) were created using support vector machines (SVM). Integration of pharmacological effects and efficacies led to the development of the systemic web platform for identification of pharmacological effects (SYSTCM). The platform, comprising 70,961 terms, including 636 Traditional Chinese Medicines (TCMs), 8190 components, 40 pharmacological effects, and 18 efficacies. Through the SYSTCM platform, (1) Total 100 components were predicted from TCMs with anti-inflammatory pharmacological effects. (2) The pharmacological effects of complete constituents were predicted from Coptidis Rhizoma (Huang Lian). (3) The principal components, pharmacological effects, and efficacies were elucidated from Salviae Miltiorrhizae radix et rhizome (Dan Shen). SYSTCM addresses subjectivity in pharmacological effect determination, offering a potential avenue for advancing TCM drug development and clinical applications. Access SYSTCM at http://systcm.cn.

Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Identification and validation of screening models for breast cancer with 3 serum miRNAs in an 11,349 samples mixed cohort.

The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels. Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability. Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models' robustness and stability. This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated.

A novel strengthened road roughness estimation framework based on the adaptive unbiased minimum variance with unknown input

The accurate acquisition of the strengthened road roughness is significant to vehicle reliability and durability. However, the existing road roughness estimation methods are inapplicable due to the non-Gaussian characteristic of the strengthened road roughness. To fulfil this gap, this work proposes a novel strengthened road roughness estimation framework based on the adaptive unbiased minimum variance with unknown input (AUMV-UI) to accurately estimate the roughness of strengthened roads. The proposed AUMV-UI framework consists of four sub-models, i.e. the quarter suspension model, the vehicle parameter identification model, the road roughness estimation model, and the estimation error correction model. In the road roughness estimation model, the unbiased minimum variance with unknown input (UMV-UI) method is proposed for the first to estimate the strengthened road roughness, and thus the first three sub-models are also integrated as the UMV-UI framework. In the estimation error correction model, the Sage–Husa method is introduced to further correct the potential errors of the UMV-UI method. The AUMV-UI framework is validated and compared to the UMV-UI framework by simulations and experiments. The results show that the AUMV-UI framework can significantly improve the estimation accuracy, which can serve as an elegant tool to accurately acquire the strengthened road roughness.

DEDNet: Dual-Encoder DeeplabV3+ Network for Rock Glacier Recognition Based on Multispectral Remote Sensing Image

Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the cryosphere environment. Deep learning algorithms and red–green–blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of the classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from the Qilian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (mean intersection over union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer’s accuracy of 93.68% and a user’s accuracy of 94.18%. Furthermore, the model was employed in two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions.

Assessing the dynamics and trigger factors of drought propagation in the China-Pakistan economic corridor: A three-dimensional perspective

Effectively managing drought in the China-Pakistan Economic Corridor (CPEC) region requires a precise understanding of the three-dimensional characteristics of meteorological drought (MD) and agricultural drought (AD), as well as the factors that trigger their propagation. This study employed non-stationary drought indices (NSPEI and SSMI) to develop a cutting-edge 3-dimensional drought identification model. This model was used to detect MD and AD patterns from 1981 to 2022 in the CPEC region and was integrated with binomial logistic regression to identify the critical factors that drive drought propagation. This study's key findings include: 1) Between 1981 and 2022, droughts in Xinjiang, China, exhibited a discernible southward migration trend, while in Pakistan, droughts showed a northward migration pattern. Drought frequency and extent have increased over time, with affected regions becoming more widespread in CPEC. Notably, drought events with higher preceding drought contagion indices (DCI) were more likely to evolve into extreme, long-term droughts. 2) Drought area emerged as a significant positive triggering factor for drought propagation in the CPEC region. Conversely, snowmelt in Xinjiang and the leaf area index for low vegetation in Pakistan acted as triggering elements affecting negatively. 3) Various factors played a pivotal role during drought propagation process, including geographical coordinates of drought centroids, DCI, and temperature variations. Additionally, snowmelt and snow evaporation significantly impacted drought propagation in Xinjiang, while vegetation cover in Pakistan played a crucial role during the drought propagation process. By utilizing four regression models and conducting comprehensive attribution analysis, this study sheds light on the characteristics of drought propagation and the factors influencing it. These findings are valuable for enhancing early warning systems and implementing effective drought mitigation strategies in the CPEC region.

Wetting State Transition of Laser Direct Writing Aluminum Surface Based on Coupling Effect of Micro/Nanoscale Characteristics.

Micro/nanostructured metal surfaces fabricated by laser direct writing (LDW) have been widely used in wettability-related fields. Previous studies focused on the effects of surface structural patterns or chemical composition on wettability, while the coupling mechanism and respective contributions of the two are not distinct. This paper reveals the coupling effect of micro/nanoscale characteristics on the wettability of LDW aluminum surfaces and elucidates the transition mechanism between wetting states on the surfaces with linear laser energy density. Through the contact angle experiments, a wetting state transition of the LDW surface is found from a more hydrophilic than pristine rose petal effect to lotus effect. Based on the bionic analysis method of the superhydrophobicity factors of lotus leaves, the contributions to the wettability of LDW surfaces are divided into the micro/nanoscale characteristics. The theoretical model for identifying the wetting state of a rough surface is proposed. Based on this model, the average Young's contact angle, θ̅Y, is calculated, which indicates the contribution of the nanoscale characteristics. During the transition process from rose petal effect to lotus effect, θ̅Y > 90° is a necessary condition for detachment from the rose petal effect, which is contributed by the high specific surface organic adsorption at the nanoscale. What is more, the wetting state determined by the microscale characteristics further enhances its hydrophobicity, leading to the lotus effect. Based on the wetting state identification model and the Cassie-Baxter equation, the change of micro/nanoscale characteristics on aluminum surfaces after LDW treatment is presented, and the influence of micro/nanoscale characteristics on the wetting state is decoupled and quantified. This research helps to coordinate the effects of surface structure and chemical composition on wettability in the design of specific wettability functional surfaces and can also be applied to other high heat density surface processing fields.

Linear modeling techniques for liquid height regulation in two tank - Two variable system

In this paper, two linear modeling techniques are presented for regulating the liquid height in two tank - two variable system (TT-TVS). Initially, the nonlinear dynamics of TT-TVS are linearized using the Taylor series linearization technique. However, this method reveals that some parameters of TT-TVS are inexact, necessitating the adoption of system identification techniques or mathematical approaches to accurately identify the linear dynamics. To address this, two new approaches are proposed: (i) a mathematical approach utilizing real-time input-output data, and (ii) a Linear Variable Parameter Transfer Function (LVPTF) model identification approach, which employs real-time data and MATLAB curve fitting tool. A comparative analysis between the proposed identification techniques and existing methods from the literature is also presented. The results indicate that the LVPTF modeling technique offers superior accuracy in identifying the linear dynamics of TT-TVS.

A new risk level identification model for aviation safety

The aviation safety reporting system (ASRS) data, which are accident investigation reports issued by the national transportation safety board (NTSB), are often used for aviation risk level identification modeling. However, ASRS data suffer from sample imbalance and inaccurate encoding of textual information. To address these issues, this paper proposes a novel risk identification model. Initially, the pre-trained sentence bidirectional encoder representations from transformers (SBERT) model is fine-tuned using the textual information in the ASRS data, improving the accuracy of the text encoding. Subsequently, a generative adversarial network (GAN) is employed to generate samples for underrepresented categories in the ASRS data, thereby addressing the sample imbalance problem. Finally, a bayesian optimization algorithm is integrated to automatically search for the hyperparameters of the risk identification model, further enhancing the model's performance in risk level identification. Experimental results demonstrate that not only does the SBERT fine-tuning substantially improve the accuracy of text information encoding, but both data augmentation and automatic hyperparameter search also significantly contribute to the performance improvement of the risk level recognition model.

Possibilities of Predicting the Manifestation of HELLP Syndrome

Introduction. Despite the use of modern principles of treatment of severe preeclampsia, mortality rates for mother and newborn in the development of life-threatening complication of preeclampsia, such as HELLP syndrome, remains high. The introduction of accurate models of early diagnosis and prediction of the probability of manifestation and severity of the HELLP syndrome into everyday medical practice will improve the safety of delivery of pregnant women with severe preeclampsia.The aim of the study is to determine the current state of the issue and systematize current data on methods of predicting the probability of HELLP syndrome manifestation.Materials and methods. An analytical review of the literature. A qualitative analysis of clinical trials and reviews on prediction of HELLP syndrome was conducted using the following data sources: PubMed and Google Scholar. The search was carried out in Russian and English, using the keywords “predictors”, or “prediction”, and “HELLP syndrome”. The date of the last search query is 22 March 2024.Results. Anamnestic data and initial characteristics of patients with HELLP syndrome were analyzed.Discussion. The analytical review included publications devoted to the study of the influence of predictive capabilities of potential biochemical markers, clinical and anamnestic signs and instrumental examination data on the probability of HELLP syndrome development.Conclusion. Adequate prediction of the manifestation of HELLP syndrome is possible on the basis of a comprehensive analysis of all identified factors, allowing the identification of effective prognostic models to improve maternal and fetal outcomes in pregnant women with severe preeclampsia.

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach

Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach

Continuous-time Hammerstein model identification utilizing hybridization of Augmented Sine Cosine Algorithm and Game-Theoretic approach

The widespread use of dynamic systems has greatly simplified various human-operated tasks. However, the complex and nonlinear nature of these systems has posed challenges in determining their structure due to heavy reliance on modeling for theoretical and empirical purposes in academic and practical contexts. This challenge is particularly evident in the Hammerstein model, which is well-known for its recognition of structural nonlinearity. To address the limitations of uncovering an optimized continuous-time Hammerstein model, researchers have explored the practical application of the Augmented Sine Cosine Algorithm-Game Theoretic (ASCA-GT). By combining the Game Theoretic (GT) and Sine Cosine Algorithm (SCA), this hybrid approach aims to achieve a nonlinear position-updated process for improved exploration and exploitation, while effectively overcoming issues like local optima trapping. The performance of ASCA-GT was evaluated using 13 benchmark functions and mathematical scenarios, as well as the Twin-Rotor System (TRS) and Electro-Mechanical Positioning System (EMPS). The effectiveness of the proposed technique was assessed using Wilcoxon's rank test, which confirmed the superior performance of ASCA-GT compared to other metaheuristic optimization methods in determining the continuous-time Hammerstein model.