Model Identification Research Articles

Bridging the gap in online hate speech detection: A comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter

Bridging the gap in online hate speech detection: A comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter

Identifying patterns in financial markets: extending the statistical jump model for regime identification

Identifying patterns in financial markets: extending the statistical jump model for regime identification

Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy

In recent years, the occurrence of high-voltage cable buffer layer ablation faults has become frequent, posing a serious threat to the safe and stable operation of cables. Failure to promptly detect and address such faults may lead to cable breakdowns, impacting the normal operation of the power system. To overcome the limitations of existing methods for identifying buffer layer ablation faults in high-voltage cables, a method for identifying buffer layer ablation faults based on frequency domain impedance spectroscopy and artificial intelligence is proposed. Firstly, based on the cable distributed parameter model and frequency domain impedance spectroscopy, a mathematical model of the input impedance of a cable containing buffer layer ablation faults is derived. Through a simulation, the input impedance spectroscopy at the first end of the cables under normal conditions, buffer layer ablation, local aging, and inductive faults is performed, enabling the identification of inductive and capacitive faults through a comparative analysis. Secondly, the frequency domain amplitude spectroscopy of the buffer layer ablation and local aging faults are used as datasets and are input into a neural network model for training and validation to identify buffer layer ablation and local aging faults. Finally, using multiple evaluation metrics to assess the neural network model validates the superiority of the MLP neural network in cable fault identification models and experimentally confirms the effectiveness of the proposed method.

The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes

Dark tea refers to a kind of post-fermented product, and its quality and price vary owing to the distinct altitudes at which it grows. In this study, a novel method based on high performance liquid chromatography with a diode-array detector (HPLC-DAD) and an evaporative light scattering detector (HPLC-ELSD) was proposed for the classification of dark teas from distinct altitudes in China. Through implementing a strategy fusing feature-level data to construct a combined dataset, the classification performance of dark teas from distinct altitudes in China was evaluated after preprocessing. The results suggested that, through the feature fusion strategy, the identification accuracy rate increased from <70% of a single detector to 76.923%. After the implementation of preprocessing, the identification accuracy rate was further improved. Typically, the model identification accuracy rate after short-time Fourier Transform (STFT) treatment reached 92.85%, and the AUROC value was higher than 0.84, exhibiting a favorable generalization ability. This study provides a new thinking for the identification technology of dark teas from different altitudes in China.

Fractional-Order Equivalent-Circuit Model Identification of Commercial Lithium-Ion Batteries

Abstract The precise identification of electrical model parameters of Li-ion batteries is essential for efficient usage and better prediction of the battery performance. In this work, the model identification performance of two metaheuristic optimization algorithms is compared. The algorithms in comparison are the Marine Predator Algorithm (MPA) and the Partial Reinforcement Optimizer (PRO) to find the optimal. Three fractional-order (FO) electrical equivalent circuit models (ECMs) of Li-ion batteries with different levels of complexity are used to fit the electrochemical impedance spectroscopy (EIS) data operating under different states of charge (SOC) and different operating temperatures. It is found that there is a tradeoff between ECM complexity, identification accuracy, and precision.

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides.

With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier). By considering multiple features, including amino acid composition (AAC) and pseudo amino acid composition (PseAAC) of antimicrobial peptides, we aimed to improve the accuracy and generalization ability of the identification process. To validate the superiority of our model, we employed five-fold cross-validation and compared it with other commonly used methods for antimicrobial peptide identification. In the experimental results on an independent test set, E-CLEAP achieved accuracies of 97.33% and 84% for the AAC and PseAAC features, respectively. The results demonstrated that our model outperformed other methods in all evaluation metrics. The findings of this study highlight the potential of the E-CLEAP model in enhancing the efficiency and accuracy of antimicrobial peptide screening, which holds significant implications for drug development, disease treatment, and biotechnology advancement. Future research can further optimize the model by incorporating additional features and information, as well as validating its reliability on larger datasets and in real-world environments. The source code and all datasets are publicly available at https://github.com/Wangsicheng52/E-CLEAP.

Biomarker identification and risk prediction model development for differentiated thyroid carcinoma lung metastasis based on primary lesion proteomics

Abstract Purpose: The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis of DTC (LMDTC). This population poses a significant challenge in clinical practice, necessitating the urgent development of effective risk stratification methods and predictive tools for lung metastasis. Experimental Design: Through proteomic analysis of large samples of primary lesion and dual validation employing parallel reaction monitoring and immunohistochemistry, we identified eight hub proteins as potential biomarkers. By expanding the sample size and conducting statistical analysis on clinical features and hub protein expression, we constructed three risk prediction models. Results: This study identified eight hub proteins-SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS and VDAC2- as potential biomarkers for predicting DTC lung metastasis risk. We developed and internally validated three risk prediction models incorporating both clinical characteristics and hub protein expression. Our findings demonstrated that the combined prediction model exhibited optimal predictive performance, with the highest discrimination (AUC: 0.986) and calibration (Brier score: 0.043). Application of the combined prediction model within a specific risk threshold (0-0.97) yielded maximal clinical benefit. Finally, we constructed a nomogram based on the combined prediction model. Conclusions: As a large sample size study in lung metastatic DTC research, the identification of biomarkers through primary lesion proteomics and the development of risk prediction models integrating clinical features and hub protein biomarkers offer valuable insights for predicting DTC lung metastasis and establishing personalised treatment strategies.

A novel technology based on Raman spectroscopy for effective differentiation of alanine chiral isomers

In this study, Raman time-domain spectroscopy (R-TDS) was used to record the spectra of β-alanine, D-alanine, L-alanine, and DL-alanine molecules in the Raman shift range of 250–2000 cm−1. Baseline correction and smoothing of the original spectra yielded considerably different scattering peaks for β-alanine than the other three alanine isomers. The positions and intensities of the peaks in the spectra of D-alanine, L-alanine and DL-alanine were very similar, enabling β-alanine to be easily distinguished from the aforementioned isomers based on the Raman spectra. The Raman spectral data of D-alanine, L-alanine and DL-alanine were subjected to variational mode decomposition (VMD), whereby each spectral line was decomposed into four intrinsic mode functions (IMFs). Finally, a grid search-support vector machine (GS-SVM) classification recognition model was established for Raman spectral analysis and classification analysis using the first four IMFs as datasets. VMD-GS-SVM classification had a 96.67 % discriminative accuracy for predicted samples of the three isomers of D-alanine, L-alanine and DL-alanine. The classification recognition accuracy gradually trended downward as the number of mode components increased. The results show that the fast identification model of alanine isomers based on R-TDS and VMD-GS-SVM is effective and feasible, providing an accurate and fast optical measurement solution for the development of novel drugs and quality monitoring of fine chemicals.

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification

To support more sustainable construction, this paper experimentally investigates the cyclic behavior of laminated timber (Laminated Veneer Lumber (LVL)) and glubam (Glue Laminated Bamboo) connections with slotted-in steel plates in terms of experimental test, numerical simulations and parameter identification. Experimental tests included eight different configurations: two materials (LVL and glubam), two bolt diameters (8 and 10 mm), and one or two bolts. Two different cyclic-loading protocols were applied for each type of connection: only tension and tension/compression. The observed behavior is then compared to a finite element model developed in OpenSeesPy, which takes into account factors such as sliding, contact, pinching, cyclic stiffness, and strength degradation. To identify the best set of parameters for the model, three different approaches are considered: genetic algorithm, fast deterministic neural network, and probabilistic Bayesian method. First, the model identification is carried out by means of a genetic algorithm-based optimization. The parameter-identification results are evaluated in terms of elastic stiffness, yielding point, and ductility. Next, a sensitivity analysis is performed to determine the significance of the parameters, and an innovative approach combining neural network and sensitivity analysis is proposed for fast and preliminary parameter identification. Then, probabilistic Bayesian identification is employed to calculate the posterior distribution of the model parameters identified and the confidence bounds of the estimated response. Finally, different model identification parameters are compared and suggestions for algorithm selection are provided.

Evolving Improved Sampling Protocols for Dose–Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3–20), and it reduced the parameter variance metric by 33–37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Developing an IoT-enabled probabilistic model for quick identification of hidden radioactive materials in maritime port operations to strengthen global supply chain security

Uncovering hidden radioactive materials continues to be a major hurdle in worldwide supply chains. Recent research has not adequately investigated practical Internet of Things (IoT)-based approaches for improving and implementing efficient data fusion techniques. Current systems often misuse resources, leading to security vulnerabilities in typical settings. Our research delves into the fundamental principles of detection using both single and multiple sensor configurations, adopting a probabilistic method for merging data. We introduce a model aimed at accelerating the detection of radiation emissions in actual port operations. The results highlight the model’s effectiveness in rapid identification and determine the best conditions for its application in scenarios involving stacked containers, whether they are on ships or positioned in storage areas.

Relativistic atomic structure calculations of Li-like ions used for plasma diagnostic studies

Abstract We have carried out atomic structure calculations using systematically enlarged multiconfiguration Dirac- Fock wavefunctions of Li-like ions of the most prominent plasma impurities (Ar, Ti, Fe, Ni, Kr and W) found in presently working tokamaks. Relativistic Breit interaction and quantum electrodynamic (QED) corrections such as vacuum polarization and self-energy corrections are also included in the calculations prior to the evaluation of low lying energy levels, transition probabilities, oscillator strengths and line strengths. Selective radiative data for electric dipole and magnetic quadrupole transitions are also reported. Special emphasis is given in the computations of fundamental quantities such as oscillator strengths as they are widely used in atomic data and analysis structure (ADAS) databases to evaluate quantities such as effective collision strengths. Present com- puted values are compared with existing available results on NIST database and few similar earlier computations and a good agreement has been found. We believe that the detailed atomic data with the relativistic and QED corrections will assist in spectroscopic studies such as accurate line identification and plasma modelling work in tokamak plasma, laser induced breakdown spectroscopy (LIBS), highly charged ions clocks and astrophysical observations.

Design an efficient VARMA LSTM GRU model for identification of deep-fake images via dynamic window-based spatio-temporal analysis

Design an efficient VARMA LSTM GRU model for identification of deep-fake images via dynamic window-based spatio-temporal analysis

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogged areas is crucial for comprehending the erosion patterns of sloping cropland and preserving black soil resource. In this study, we built varied models based on two stages (one using only deep learning methods and the other combining object-based image analysis (OBIA) with deep learning methods) to identify waterlogged areas using high-resolution remote sensing data. The results showed that the five deep learning models using original remote sensing imagery achieved precision rates varying from 54.6% to 60.9%. Among these models, the DeepLabV3+-Xception model achieved the highest accuracy, as indicated by an F1-score of 53.4%. The identified imagery demonstrated a significant distinction in the two categories of waterlogged areas: sloping cropland erosion zones and erosion risk areas. The former had obvious borders and fewer misclassifications, exceeding the latter in terms of identification accuracy. Furthermore, the accuracy of the deep learning models was significantly improved when combined with object-oriented image analysis. The DeepLabV3+-MobileNetV2 model achieved the maximum accuracy, with an F1-score of 59%, which was 6% higher than that of the model using only original imagery. Moreover, this advancement mitigated issues related to boundary blurriness and image noise in the identification process. These results will provide scientific assistance in managing and reducing the impact in these places.

Modification of HFACS model for path identification of causal factors of collapse accidents in the construction industry

PurposeThis study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.Design/methodology/approachThe main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.FindingsEight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.Originality/valueThis paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

Shape optimization for interface identification in nonlocal models

AbstractShape optimization methods have been proven useful for identifying interfaces in models governed by partial differential equations. Here we consider a class of shape optimization problems constrained by nonlocal equations which involve interface–dependent kernels. We derive a novel shape derivative associated to the nonlocal system model and solve the problem by established numerical techniques. The code for obtaining the results in this paper is published at (https://github.com/schustermatthias/nlshape).