Accurate Model Research Articles

Accurate Modeling and Optimal Rotor Flux Control of a Bearingless Induction Motor With the Specific Pole

Accurate Modeling and Optimal Rotor Flux Control of a Bearingless Induction Motor With the Specific Pole

Accurate Prediction and Modeling of Overbreak Phenomenon in Tunnel Excavation Using Rock Engineering System Method

Accurate Prediction and Modeling of Overbreak Phenomenon in Tunnel Excavation Using Rock Engineering System Method

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

<p>Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still little research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares several machine learning methods to predict hotspots in Kalimantan. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Some of the machine learning methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, several measures of sensitivity and feature importance used are variance-based, density-based, and distribution-based sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the ML model concluded that the Bayesian linear regression model outperformed other ML models, based on RMSE and explained variance score. Meanwhile, tree-based models, such as random forest and gradient boosting, are indicative of overfit. Based on the results of sensitivity analysis and feature importance, the number of dry days is the most important feature for the Bayesian linear regression model in predicting the number of hotspots in Kalimantan.</p>

Development of a fed-batch-scale anaerobic co-digestion control system based on multivariable output error state space and model predictive control

This paper describes a feeding control system for fed-batch-scale anaerobic co-digestion. The proposed system is based on the multivariable output error state space and uses model predictive control to regulate the biogas flow output of anaerobic co-digestion. This control system combines predictive modeling, receding-horizon optimization, and state feedback. An accurate linear model of anaerobic co-digestion is obtained through the multivariable output error state space method using experimental data, and the accuracy of the model is evaluated using a goodness-of-fit index and the root mean square error. After augmenting the model, a predictive control system is established. The system states are observed through a Kalman filter, and the constrained receding-horizon optimization problem is solved using quadratic programming. Finally, the stability of the control system is demonstrated from both open- and closed-loop perspectives. The control system is then constructed and used in actual experiments, with the target flow output set at 100 NmL/h. The final flow output indicates that the control system achieves good control performance. This research enables the biogas output of anaerobic co-digestion to be controlled from a linear perspective through a concise control algorithm with strong practical application prospects.

ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.

Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning-driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF-VIM) yielded the highest five-fold cross-validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting-edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs.

An advanced approach for accurate pneumonia detection using combined deep convolutional neural networks

Pneumonia, a lung infection caused by viral or bacterial agents, poses a significant health risk by affecting one or both lungs in humans. Accurate diagnosis, particularly in pediatric cases, is crucial for timely intervention. chest X-rays (CXRs) are a common and non-invasive diagnostic tool to detect pneumonia-related abnormalities. Nonetheless, the minimal radiation exposure suitable for pediatric diagnosis poses a challenge in accurately detecting pneumonia in children. This work proposes a concatenation model that combines two pre-trained convolutional neural networks (CNNs) depending on the transfer learning (TL) technique and optimizes the training parameters to build a highly accurate model for detecting pediatric pneumonia from CXR images. The concatenated extracted features from the two pre-trained CNNs are passed through a convolutional layer to select more valuable semantic features to reduce the extracted features, which helps reduce the model parameters and execution time. Experimental results demonstrate that the feature concatenation technique, along with optimization of training parameters, surpasses the performance of individual CNNs and several state-of-the-art methods. The proposed method achieves a classification accuracy of 98.5%, precision of 99.5%, sensitivity of 98.4%, and F1 score of 99.1%. The primary objective of the proposed approach is to aid radiologists in achieving accurate pneumonia diagnosis in real-time.

Analysis of the Electromagnetic Effects on the Large Floating Roof Oil Tanks by nearby Lightning Strike Based on TLM

Lightning fire is one of the biggest dangers for chemical and petrochemical storage tank farms, particularly in floating roof oil tanks (FROTs). To improve the lightning protection of FROTs, the process of floating roof tanks affected by lightning is analyzed. Lightning inductive effects can result in the undesired operation of the above-ground storage tank equipment and endanger exploitation safety. The time domain finite element analysis based on the transmission line model (TLM) method is regarded as a numerical technique to solve field problems by implementing circuit equivalents. This study aims to analyze lightning strike characteristics using the Heidler current function and simulate electromagnetic field strength (EMFS) distribution and surface current density (SCD). Calculation of minimum possible effective strike distance and shielding effectiveness (SE) is performed. Parametric modeling ensures accurate modeling of the roof's bypass conductor to the shell. By utilizing the Parameter Sweep technique, multiple simulations can be carried out with varying structure parameter values. This approach ensures optimal efficiency and accuracy in the results obtained.

Predicting mortality within 1 year of ART initiation in children and adolescents living with HIV in sub-Saharan Africa: a retrospective observational cohort study

Differentiated service delivery (DSD) for children and adolescents living with HIV can improve targeted resource use. We derived a mortality prediction score to guide clinical decision making for children and adolescents living with HIV. Data for this retrospective observational cohort study were evaluated for all children and adolescents living with HIV and initiating antiretroviral therapy (ART); aged 0-19 years; and enrolled at Baylor clinics in Eswatini, Malawi, Lesotho, Tanzania, and Uganda between 2005 and 2020. Data for clinical prediction, including anthropometric values, physical examination, ART, WHO stage, and laboratory tests were captured at ART initiation. Backward stepwise variable selection and logistic regression were performed to develop predictive models for mortality within 1 year of ART initiation. Probabilities of mortality were generated, compared with true outcomes, internally validated, and evaluated against WHO advanced HIV criteria. The study population included 16 958 children and adolescents living with HIV and initiated on ART between May 18, 2005, and Dec 18, 2020. Predictive variables for the most accurate model included: age, CD4 percentage, white blood cell count, haemoglobin concentration, platelet count, and BMI Z score as continuous variables, and WHO clinical stage and oedema, abnormal muscle tone and respiratory distress on examination as categorical variables. The area under the curve (AUC) of the predictive model was 0·851 (95% CI 0·839-0·863) in the training set and 0·822 (0·800-0·845) in the test set, compared with 0·606 (0·595-0·617) for the WHO advanced HIV criteria (p<0·0001). This study evaluated a large, multinational population to derive a mortality prediction tool for children and adolescents living with HIV. The model more accurately predicted clinical outcomes than the WHO advanced HIV criteria and has the potential to improve DSD for children and adolescents living with HIV in high-burden settings. National Institute of Health Fogarty International Center.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

An online battery–state of charge estimation method using the varying forgetting factor recursive least square - unscented Kalman filter algorithm on electric vehicles

Accurate and fast estimation of the state of charge is important for the battery management system of electric vehicles. This paper proposes a method to estimate the state of charge of Lithium-ion batteries by the &lt;span lang="EN-US"&gt;variable&lt;/span&gt; forgetting factor recursive least square (VFFRLS) – unscented Kalman filter (UKF) algorithm in real-time without the off-line battery testing data. Since the state observation requires an accurate model, an equivalent circuit model was constructed. Then, the VFFRLS algorithm is used to identify online the battery model parameters based on voltage and current measurements. An advantage of this algorithm is that it requires less initial information and shorter identification time than offline parameter identification. After the model parameters are well identified, the unscented Kalman filter estimates the state of charge and minimizes noise characteristics and uncertainty in the parameter identification process. The VFFRLS algorithm applied in this paper has shown a good result with the model output error of less than 1%, and the identification achieves real-time response. The state of charge obtained by the UKF algorithm has shown satisfactory estimation results with fast convergence speed and small errors. The UKF filter provides the results with a 1.5% error from the reference and converges after 10 cycles.

Prediction and Analysis of Liver Patient Data

Abstract: Prediction and Analysis of Liver Patient Data is a project that aims to gain insights into liver diseases and develop a system for predicting the likelihood of liver-related health conditions by using data analysis techniques and predictive modeling. This project examines data from liver patients, focusing on the relationships between a key list of liver enzymes, proteins, age, and gender in order to predict the likelihood of liver disease. In this case, we are constructing a model by employing various machine learning algorithms to find the most accurate model. And connect to a flask-based web application. The user can predict the disease by entering parameters into the web application. The project's goal is to uncover significant indicators and build a predictive model to aid in diagnosing liver diseases and identifying potential risks by exploring patterns and relationships within the data.

Phishing Website Detection

Abstract: Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Our findings show that the model based on the K means clustering is the most accurate of the other four techniques and outperforms other solutions in the literature.

Computational modelling of tyre behavior on concrete and sandy soil surfaces using solid works software

Tyres are integral components of vehicles, crucial for controlling movement, supporting vehicle weight, and providing traction. Consequently, there's a growing need for accurate tyre modelling to optimize performance under varying conditions. While extensive literature exists on tyre-soil interaction, information is scarce regarding tyre behaviour across different soil types. This study aims to address this gap by employing computational modelling techniques to analyse tyre behaviour on two distinct surfaces: concrete and sandy soil. Utilizing SolidWorks software, a comprehensive investigation was conducted to evaluate tyre performance under varying road conditions. The study focused on analysing shear forces, soil resistances, and partial soil resistant forces exerted on the tyres. The findings indicate that tyres exhibit superior performance on concrete surfaces compared to sandy soil. The computational simulations predicted shear forces of 0.6 kN, soil resistances of 0.2 kN, and partial soil resistant forces of 0.25 kN for the concrete surfaces. These results were notably higher than those observed on sandy soil surfaces. This study sheds light on the intricate dynamics of tyre behaviour under different road conditions and underscores the importance of computational modelling in understanding and optimizing tyre performance. The insights gained from this research have significant implications for vehicle design, road engineering, and the development of more efficient and durable tyres tailored to specific environmental conditions.

Estimation of the mean effective pressure of a spark ignition internal combustion engine using a neural network, considering the wall-wetting dynamics

The management and development of internal combustion engines stand as critical pursuits within the automotive and related industries. Utilizing cylinder pressure as feedback, engine controllers rely on intricate systems to regulate performance. However, due to the inherent complexity and nonlinearity of engines, direct measurement of cylinder pressure through pressure sensors is costly and computationally demanding. Consequently, the need for accurate and detailed engine models becomes paramount. Neural networks offer a promising avenue for simulating internal combustion engines, combining speed and precision. By treating the engine as an enigmatic entity, neural networks can construct detailed models. This study aims to employ two types of neural networks—multilayer perceptron and radial basis functions—to train and build a model of an internal combustion engine. These networks will simulate and estimate the engine's mean suitable pressure, allowing for a comparison of their effectiveness. Prior to implementing the neural network architecture, an engine model was constructed in MATLAB to gather necessary training data. This preliminary step ensured a robust foundation for subsequent network design and implementation. In summary, this research focuses on leveraging neural networks to model internal combustion engines, utilizing both multilayer perceptron and radial basis functions to simulate engine behavior and estimate mean suitable pressure.

Multi-view 3D reconstruction of seedling using 2D image contour

3D reconstruction of seedling can provide comprehensive and quantitative spatial structure information, offering an effective digital tool for breeding research. However, accurate and efficient reconstruction of seedling is still a challenging work due to limited performance of depth sensor for seedling with small-size stem and unavoidable error for multi-view point cloud registration. Therefore, in this paper, we propose an accurate multi-view 3D reconstruction method for seedling using 2D image contour to constrain 3D point cloud. The rotation axis is calibrated and optimised by minimising point-to-contour distance between 2D image contour and projected exterior points from 3D point cloud. Then, to remove outliers and noise, we introduce the seedling mask of 2D image to constrained and delete projected outlier points of 3D model from corresponding view. Furthermore, we propose a residual-guided method to recognise missing region for 3D model and complete 3D model of small-size stem. Finally, we can obtain an accurate 3D model of seedling. The reconstruction accuracy is evaluated by average distance between projected contour of 3D model and 2D image contour of all views (0.3185 mm). Then, the phenotypic parameters were calculated from 3D model and the results are close to manual measurements (Plant height: R2 = 0.98, RMSE = 2.3 mm, rRMSE = 1.52%; Petioles inclination angle: R2 = 0.99, RMSE = 0.73°, rRMSE = 1.41%; Leaf area: R2 = 0.66, RMSE = 1.05 cm2, rRMSE = 7.63%; Leaf inclination angle: R2 = 0.99, RMSE = 1.01°, rRMSE = 1.72%; Stem diameter: R2 = 0.95, RMSE = 0.12 mm, rRMSE = 5.43%). Breeders can improve the selection of more resilient varieties and cultivars to different growing conditions starting from the dynamic analysis of their phenotype.

Skeletal Kinetics Reduction for Astrophysical Reaction Networks

A novel methodology is developed to extract accurate skeletal reaction models for nuclear combustion. Local sensitivities of isotope mass fractions with respect to reaction rates are modeled based on the forced optimally time-dependent (f-OTD) scheme. These sensitivities are then analyzed temporally to generate skeletal models. The methodology is demonstrated by conducting skeletal reduction of constant density and temperature burning of carbon and oxygen relevant to Type Ia supernovae (SNe Ia). The 495-isotopes Torch model is chosen as the detailed reaction network. A map of maximum production of 56Ni in SNe Ia is produced for different temperatures, densities, and proton-to-neutron ratios. The f-OTD simulations and the sensitivity analyses are then performed with initial conditions from this map. A series of skeletal models are derived and their performances are assessed by comparison against currently existing skeletal models. Previous models have been constructed intuitively by assuming the dominance of α-chain reactions. The comparison of the newly generated skeletal models against previous models is based on the predicted energy release and 44Ti and 56Ni abundances by each model. The consequences of y e ≠ 0.5 in the initial composition are also explored where y e is the electron fraction. The simulated results show that 56Ni production decreases by decreasing y e as expected, and that the 43Sc is a key isotope in proton and neutron channels toward 56Ni production. It is shown that an f-OTD skeletal model with 150 isotopes can accurately predict the 56Ni abundance in SNe Ia for y e ≲ 0.5 initial conditions.

Neutron emission characterization of IPR 14 MeV neutron generator

Accelerator-based neutron sources are being developed worldwide due to their wide applications. Institute for Plasma Research in India has developed an accelerator-based high-yield 14 MeV neutron generator based on deuterium-tritium (D-T) reaction to perform fusion-related benchmark experiments. This neutron generator is designed to produce 1012 neutrons per second in both mode continuous and pulse. The emission of neutrons from an accelerator-based neutron generator is not isotropic due to target geometry and angular dependency of reaction cross-section. A detailed and accurate description of neutron source emission and distribution is required to use such a neutron generator for various experiments. The neutron generator is characterized using experiments and simulations to accomplish the request. Energy and angular distributions of emitted neutrons are measured using a calibrated single crystal diamond (SCD) detector. Experimental data of neutron distributions are used to validate source neutron definition for Monte Carlo N Particle (MCNP) simulation. An accurate model of the Titanium Tritide (TiT) target and its holder geometry is prepared for the simulation. Finally, to validate the simulation model, simulated neutron fluxes are compared with measured ones by the SCD detector. This paper discusses the methodology adopted to characterize emitted neutrons and its results.

Physics-guided neural network for grinding temperature prediction

Creep-feed grinding is a high-efficiency, high-precision grinding process widely used in the manufacturing of aviation engines. However, the workpiece burn and other quality issues caused by high processing temperature limit the yield of grinding. Therefore, the accurate model of grinding temperature has become the key to improving processing efficiency and quality. Different from the traditional physical or data-driven models, this paper attempts to combine both perspectives based on Physics-Guided Neural Networks (PGNN) to accurately predict grinding temperature with a small number of experiments. At the level of data acquisition, real grinding experiment data was obtained and a data augmentation method had been proposed. At the level of neural network structure, optimisation processes were implemented to enhance prediction performance, and a physics-guided loss function was inserted to guide network training. The experiment results shows that PGNN had better prediction accuracy than the physical model, while also mitigating the limitations of data-driven models on small sample sets. PGNN also performed better with noisy data and predictions out of the training data range, this reveals the benefits of PGNN for small sample problems in processing scenarios.

When humans were the hunted: bone-tipped arrow points in Prehispanic Sierras of Córdoba, Argentina

ABSTRACT This paper presents the techno-typological study of seven bone projectile points closely associated with a burial assemblage excavated from El Alto 5 (~550 cal BP, Sierras of Córdoba, Argentina). The bone points provide a comparative model for interpreting the function of arrow points, primarily concerning how archaeologists can accurately differentiate the arrow tips used for warfare or hunting. Bone points have barbed shoulders to resist removal from the wound and serrated stems for secure hafting. They required shafts a few millimeters thicker than stone-tipped arrows for hafting, involving more kinetic energy to inflict severe injuries. Identifying a dark residue spattered over the blades opens the possibility that poison was applied to enhance the effectiveness of the shoot. The study is considered a starting point to build more accurate models to identify interpersonal violence during the Late Prehispanic Period, where most bone points occurred as domestic refuse of campsites.