Search Results (149)

The rapid evolution of virtual reality systems and the broader metaverse landscape has prompted growing research interest in biometric authentication methods for user verification. These solutions offer an additional layer of access control that surpasses traditional password-based approaches by leveraging unique physiological or behavioral traits. Current literature emphasizes analyzing controller position and orientation data, which presents challenges when using convolutional neural networks (CNNs) with non-continuous Euler angles. The novelty of the presented approach is that it addresses this limitation. We propose a modality transformation approach that generates acceleration and angular velocity signals from trajectory and orientation data. Specifically, our work employs algebraic techniques—including quaternion algebra—to model these dynamic signals. Both the original and transformed data were then used to train various CNN architectures, including Vanilla CNNs, attention-enhanced CNNs, and Multi-Input CNNs. The proposed modification yielded significant performance improvements across all datasets. Specifically, F1-score accuracy increased from 0.80 to 0.82 for the Comos subset, from 0.77 to 0.82 for the Quest subset, and notably from 0.83 to 0.92 for the Vive subset. Full article

This study investigates the cooperative transport of a cable-suspended payload by two multirotor unmanned aerial vehicles (UAVs). A compact nonlinear control law that allows to simultaneously (i) track a slow reference trajectory, (ii) hold a prescribed inter-vehicle geometry, and (iii) actively damp load swing is developed. The model treats the two aerial robots and the payload as three point masses connected by linear-elastic cables, and the controller is obtained through a Newton–Euler formulation. A singular-perturbation analysis shows that, under modest gain–separation conditions, the closed-loop system is locally exponentially stable: fast dynamics govern formation holding and swing suppression, while slow dynamics takes into account trajectory tracking. Validation is performed in a realistic simulation scenario that includes six-degree-of-freedom rigid-body vehicles, Blade-Element theory rotor models, and sensor noise. Compared to an off-the-shelf, baseline controller, the proposed method significantly improves flying qualities while minimizing hazardous payload oscillations. Owing to its limited parameter set and the absence of heavy optimization, the approach is easy to tune and well suited for real-time implementation on resource-limited UAVs. Full article

Maintaining the required relative humidity values in the vehicle cabin is an important HVAC task, along with considerations related to the temperature, velocity, air pressure and noise. Deviation from the optimal values worsens the psycho-physiological state of the driver and affects the energy efficiency of the train. In this study, a model of liquid film formation on and removal from various cabin surfaces was constructed using the fundamental Navier–Stokes hydrodynamic equations. A special transport model based on the liquid vapor diffusion equation was used to simulate the air environment inside the cabin. The evaporation and condensation of surface films were simulated using the Euler film model, which directly considers liquid–gas and gas–liquid transitions. Numerical results were obtained using the RANS equations and a turbulence model by means of the finite volume method in Ansys CFD. Conjugate fields of temperature, velocity and moisture concentration were constructed for various time intervals, and the dependence values for the film thicknesses on various surfaces relative to time were determined. The verification was conducted in comparison with the experimental data, based on the protocol for measuring the microclimate indicators in workplaces, as applied to the train cabin: the average ranges encompassed temperature changes from 11% to 18%, and relative humidity ranges from 16% to 26%. Comparison with the results of other studies, without considering the phase transition and condensation, shows that, for the warm mode, the average air temperature in the cabin with condensation is 12.5% lower than without condensation, which is related to the process of liquid evaporation from the heated walls. The difference in temperature values for the model with and without condensation ranged from −12.5% to +4.9%. We demonstrate that, with an effective mode of removing condensate film from the window surface, including recirculation modes, the energy consumption of the climate control system improves significantly, but this requires a more accurate consideration of thermodynamic parameters and relative humidity. Thus, considering the moisture condensation model reveals that this variable can significantly affect other parameters of the microclimate in cabins: in particular, the temperature. This means that it should be considered in the numerical modeling, along with the basic heat transfer equations. Full article

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development. Full article

This work systematically addresses the dual challenges of non-inertial dynamic coupling and kinematic constraint redundancy encountered in dynamic modeling of serial–parallel–serial hybrid robotic mechanisms, and proposes an improved Newton–Euler modeling method with constraint compensation. Taking the Skiing Simulation Platform with 6-DOF as the research mechanism, the inverse kinematic model of the closed-chain mechanism is established through $G_F$ set theory, with explicit analytical expressions derived for the motion parameters of limb mass centers. Introducing a principal inertial coordinate system into the dynamics equations, a recursive algorithm incorporating force/moment coupling terms is developed. Numerical simulations reveal a 9.25% periodic deviation in joint moments using conventional methods. Through analysis of the mechanism’s intrinsic properties, it is identified that the lack of angular momentum conservation constraints on the end-effector in non-inertial frames leads to system controllability degradation. Accordingly, a constraint compensation strategy is proposed: establishing linearly independent differential algebraic equations supplemented with momentum/angular momentum balance equations for the end platform. Co-Simulation results demonstrate that the optimized model reduces the maximum relative error of actuator joint moments to 0.98%, and maintains numerical stability across the entire configuration space. The constraint compensation framework provides a universal solution for dynamics modeling of complex closed-chain mechanisms, validated through applications in flight simulators and automotive driving simulators. Full article

Integer-order models cannot characterize the dynamic behavior of the flexible two-link manipulator (FTLM) system accurately due to its viscoelastic characteristics and flexible oscillation. Hence, this paper proposes a fractional-order modeling method and identification algorithm for the FTLM system. Firstly, we exploit the memory and history-dependent properties of fractional calculus to describe the flexible link’s viscoelastic potential energy and viscous friction. Secondly, we establish a fractional-order differential equation for the flexible link based on the fractional-order Euler–Lagrange equation to characterize the flexible oscillation process accurately. Accordingly, we derive the fractional-order model of the FTLM system by analyzing the motor–link coupling as well as the symmetry of the system structure. Additionally, a system identification algorithm based on the multi-innovation integration operational matrix (MIOM) is proposed. The multi-innovation technique is combined with the least-squares algorithm to solve the operational matrix and achieve accurate system identification. Finally, experiments based on actual data are conducted to verify the effectiveness of the proposed modeling method and identification algorithm. The results show that the MIOM algorithm can improve system identification accuracy and that the fractional-order model can describe the dynamic behavior of the FTLM system more accurately than the integer-order model. Full article

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions. Full article

To address the issue of reduced observer accuracy under low carrier–frequency ratio (CFR) conditions in the sensorless control of high-speed motors, which limits system performance, this paper proposes a discrete mathematical modeling method for surface-mounted permanent magnet synchronous motors (SPMSMs). Based on this established accurate discrete motor model, the influence of low CFR on the phase estimation error of back electromotive force (EMF) is analyzed. Building on this foundation, an accurate discrete Luenberger observer (ALO) is designed, and a corresponding phase compensation control method is proposed. A motor drive control system comprising hardware, software, and experimental test setups is constructed. The experimental results demonstrate that, compared to the Euler model, the discrete mathematical model established by this method significantly improves position observation accuracy under low CFR conditions. Furthermore, compared to the traditional Luenberger observer (TLO), the estimation error of the proposed observer under a low CFR is reduced by approximately 85%. This approach exhibits high application value in the sensorless control of high-speed and high-frequency motors. Full article

The shallow, soft subsea formations, characterized by low strength and poor stability, lead to complex interactions between the screw motor drilling tool and the wellbore wall during directional drilling, complicating the accurate evaluation of the tool’s deflection capability. To address this issue, this paper proposes an integrated mechanical analysis method combining three-dimensional finite element analysis and transient dynamic analysis. By establishing a finite element model using 12-DOF (degree-of-freedom) spatial rigid-frame Euler–Bernoulli beam elements, coupled with well trajectory coordinate transformation and Rayleigh damping matrix, a precise description of drill string dynamic behavior is achieved. Furthermore, the introduction of pipe–soil dynamics and the p-y curve method improves the calculation of contact reaction forces between drilling tools and formation. Case studies demonstrate that increasing the tool face rotation angle intensifies lateral forces at the bit and stabilizer, with the predicted maximum dogleg severity within the first 10 m ahead of the bit progressively increasing. When the tool face rotation angle exceeds 2.5°, the maximum dogleg severity reaches 17.938°/30 m. With a gradual increase in the drilling pressure, the maximum bending stress on the drilling tool, maximum lateral cutting force, and stabilizer lateral forces progressively decrease, while vertical cutting forces and bit lateral forces gradually increase. However, the predicted maximum dogleg severity increases within the first 10 m ahead of the bit remain relatively moderate, suggesting the necessity for the multi-objective optimization of drilling pressure and related parameters prior to actual operations. Full article

An optimization framework that effectively balances dose–volume constraints and treatment objectives is required in intensity-modulated radiation therapy (IMRT) planning. In our previous work, we proposed a dynamical systems-based approach in which dose constraints, along with beam coefficients, are treated as state variables and dynamically evolve within a continuous-time system. This method improved the accuracy of the solution by dynamically adjusting the dose constraints, but it had a significant drawback. Specifically, because it is as an iterative process derived from discretization of a linear differential equation system using the additive Euler method, a lower-bound clipping procedure is required to prevent the state variables for both beam coefficients and dose constraints from taking negative values. This issue could prevent constrained optimization from functioning properly and undermine the feasibility of the treatment plan. To address this problem, we propose two types of multiplicative continuous-time dynamical system that inherently preserve the nonnegativity of the state variables. We theoretically prove that the initial value problem for these systems converges to a solution that satisfies the constraints of consistent IMRT planning. Furthermore, to ensure computational practicality, we derive discretized iterative schemes from the continuous-time systems and confirm that their iterations maintain nonnegativity. This framework eliminates the need for artificial clipping procedures and leads to the multiplicative variable dose-constraints method, which dynamically adjusts dose constraints during the optimization process. Finally, numerical experiments are conducted to support and illustrate the theoretical results, showing how the proposed method achieves high-precision IMRT planning while ensuring physically meaningful solutions. Full article

The score-based diffusion model has made significant progress in the field of computer vision, surpassing the performance of generative models, such as variational autoencoders, and has been extended to applications such as speech enhancement and recognition. This paper proposes a U-Net architecture using a score-based diffusion model and an efficient multi-scale attention mechanism (EMA) for the speech enhancement task. The model leverages the symmetric structure of U-Net to extract speech features and captures contextual information and local details across different scales using the EMA mechanism, improving speech quality in noisy environments. We evaluate the method on the VoiceBank-DEMAND (VB-DMD) dataset and the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus–TUT Sound Events 2017 (TIMIT-TUT) dataset. The experimental results show that the proposed model performed well in terms of speech quality perception (PESQ), extended short-time objective intelligibility (ESTOI), and scale-invariant signal-to-distortion ratio (SI-SDR). Especially when processing out-of-dataset noisy speech, the proposed method achieved excellent speech enhancement results compared to other methods, demonstrating the model’s strong generalization capability. We also conducted an ablation study on the SDE solver and the EMA mechanism, and the results show that the reverse diffusion method outperformed the Euler–Maruyama method, and the EMA strategy could improve the model performance. The results demonstrate the effectiveness of these two techniques in our system. Nevertheless, since the model is specifically designed for Gaussian noise, its performance under non-Gaussian or complex noise conditions may be limited. Full article

The grey prediction model is designed to characterize systems comprising both partially known information (referred to as white) and partially unknown dynamics (referred to as black). However, traditional GM(1,1) models are based on linear differential equations, which limits their capacity to capture nonlinear and non-stationary behaviors. To address this issue, this paper develops a generalized grey differential prediction approach based on the Bernoulli equation framework. We incorporate the Bernoulli mechanism with a nonlinear exponent n and a dynamic polynomial-driven term. In this work, we propose a new model designated as BPGM(1,1). Another key innovation of this work is the adoption of a nonlinear least squares direct parameter identification strategy to calculate the exponent and polynomial parameters in the Bernoulli equation, which achieves a higher degree of freedom in parameter selection and effectively circumvents the model distortion issues caused by traditional background value estimation. Furthermore, the Euler discretization method is utilized for numerical solving, reducing the reliance on traditional analytical solutions for linear structures. Numerical experiments indicate that BPGM(1,1) surpasses GM(1,1), NFBM(1,1), and their improved versions. By leveraging the synergistic mechanism between Bernoulli-type nonlinear regulation and polynomial-driven external excitation, this framework significantly enhances prediction accuracy for systems characterized by non-stationary behaviors and multi-scale trends. Full article

Background: The aging population is increasing rapidly, with individuals aged 65 and older now representing more than 15% of the global population. This demographic shift is associated with a rising incidence of age-related cardiovascular diseases (CVDs). Early prediction and prevention of cardiovascular aging are essential to improve health outcomes among elderly patients. Objective: This study aimed to develop and externally validate a mathematical model for predicting cardiovascular aging in individuals aged 65 and older, based on general clinical and behavioral data. Methods: The model was built using data from 800 individuals aged 65+ from Almaty, Kazakhstan. Predictors included sex, marital status, education, smoking, alcohol use, disability, physical activity, total cholesterol, hypertension, BMI, coronary artery disease (CAD), myocardial infarction, diabetes mellitus, and chronic heart failure. A system of ordinary differential equations was used to simulate the dynamic interactions of these factors. Numerical integration was performed using the Runge–Kutta, Adams–Bashforth, and backward Euler methods. The model was verified statistically using Pearson correlation analysis and externally validated on independent age cohorts. In addition, we applied k-means clustering to identify hidden patterns and risk profiles within the dataset. A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. Results: The model trained on the 65–74 age group achieved an external validation accuracy of 98.8% and an AUC of 0.989 when applied to the 75–89 group. Risk modeling showed that in the 65–74 group, smoking and alcohol increased the risk of myocardial infarction, hypertension, and obesity by up to 53%. In the 75–89 group, these factors increased the likelihood of hypertension by 21%, chronic heart failure by 16%, and CAD by 14%. Among individuals aged 90+, hypercholesterolemia increased the risk of chronic heart failure by 17%, while hypertension increased myocardial infarction risk by 16%. Conclusions: The proposed model demonstrated high accuracy in predicting cardiovascular aging and identifying high-risk individuals across elderly subgroups. The integration of clustering and classification methods (k-means and Random Forest) provided additional insights and confirmed the consistency of the findings. This multi-method approach may serve as a valuable tool for developing personalized prevention strategies in geriatric care and improving healthy life expectancy. Full article

In practical engineering, uncertainties inevitably exist in the models and measurement data used for structures. Therefore, a statistical strategy related to damage detection methods become crucial. In this paper, a probabilistic statistical damage detection method for FG Euler–Bernoulli beam structures is proposed, extending the approach originally developed for isotropic materials. Our approach determines the probability of damage occurrence for each element, which aids in evaluating whether beam structures have been damaged. This evaluation is based on integrating the sensitivity of modal strain energy for each element with the perturbation method. To demonstrate the effectiveness and accuracy of the proposed method, several numerical examples are investigated. These examples include a simply supported FG Euler–Bernoulli beam subjected to both single and multiple element damages. The influence of gradient index, damage severity, boundary condition, and noise level on the accuracy of detection are also considered. The studies demonstrate that the probability of damage for each element remains relatively stable despite variations in the gradient indices. For the damaged elements, these probabilities approach 1, indicating that the proposed method effectively identifies damage in FG beams even when the gradient index varies. Additionally, as the level of damage increases, the accuracy of damage detection tends to improve. However, varying boundary conditions can substantially affect the outcomes of damage identification, potentially leading to inconsistencies in results. Furthermore, our proposed method demonstrates excellent resistance against noise levels of up to 5%. We also found that different boundary conditions have a great impact on the damage detection. Full article

This study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) control methods were designed and used for active control to increase vehicle handling and passenger comfort, with the aim of reducing or eliminating vibrations by performing active control of passive suspension systems using these methods. The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. In addition, the obtained results were compared with each other and with other studies using the integral time-weighted absolute error (ITAE) performance criterion. The proposed control method yielded significant improvements in vehicle parameters compared with the passive suspension system. Vehicle body movement, vehicle acceleration, suspension deflection, and tire deflection improved by approximately 88.2%, 91.5%, 88%, and 89.4%, respectively. Thus, vehicle driving comfort was significantly enhanced based on the proposed system. Full article