Search Results (350)

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration. Full article

Maneuvering extended object tracking has garnered significant attention owing to the continuous advancements in the resolution capabilities of modern high-precision radar sensors. The efficacy of tracking algorithms for such objects is heavily contingent upon the design of the model set. However, existing methodologies for model set design often yield suboptimal performance when confronted with highly maneuvering extended objects. The expected model augmentation (EMA) algorithm offers a data-driven mechanism for updating the model set in real time. Despite its advantages, the EMA algorithm is constrained by the fixed parameters of its basic models and static transition probabilities between models, thereby limiting its adaptability to extended objects exhibiting complex and dynamic maneuvering behaviors. To address these limitations, this paper proposes a modified variable structure multiple model (VSMM) framework for maneuvering extended object tracking, referred to as the adaptive grid expected model augmentation based on the golden section (GSAG-EMA) algorithm. The approach adaptively adjusts both the model structure and parameters in a grid-based format to accommodate the varying maneuvering patterns. It incorporates both local and global weighting schemes, with two models within the grid based on the golden section. Furthermore, the transition probability matrix is dynamically updated following specific rules, and the execution strategy for each module is determined according to the filtering results. Simulation results under both weak and strong maneuvering scenarios demonstrate that the proposed GSAG-EMA algorithm consistently outperforms the IMM-based, EMA, and AG-BMA algorithms in terms of root mean square error (RMSE) and Hausdorff distance, thereby substantiating its superior tracking performance. Full article

This paper presents a fast and robust approach to evaluate the singular values of small (e.g., $4\times 4$, $5\times 5$) matrices on single- and multi-Graphics Processing Unit (GPU) systems, enabling the modulation of the accuracy–speed trade-off. Targeting applications that require only computations of the SVs in electromagnetics (e.g., Multiple Input Multiple Output—MIMO link capacity optimization) and emerging deep-learning kernels, our method contrasts with existing GPU singular value decomposition (SVD) routines by computing singular values only, thereby reducing overhead compared to full-SVD libraries such as cuSOLVER’s gesvd and MKL’s desvg. The method uses four steps: interlaced storage of the matrices in GPU global memory, bidiagonalization via Householder transformations, symmetric tridiagonalization, and root finding by bisection using Sturm sequences. We implemented the algorithm in CUDA and evaluated it on different single- and multi-GPU systems. The approach is particularly suited for the analysis and design of multiple-input/multiple-output (MIMO) communication links, where thousands of tiny SVDs must be computed rapidly. As an example of the satisfactory performance of our approach, the speed-up reached for large matrix batches against cuSOLVER’s gesvd has been around 20 for $4\times 4$ matrices. Furthermore, near-linear scaling across multi-GPUs systems has been reached, while maintaining root mean square errors below $2.3\times {10}^{-7}$ in single precision and below $2.3\times {10}^{-13}$ in double precision. Tightening the tolerance from $\delta ={10}^{-7}$ to $\delta ={10}^{-9}$ increased the total runtime by only about 10%. Full article

Solvent-exchange-induced in situ forming gel (ISG) refers to a drug delivery system that transforms from a solution state into a gel or solid matrix upon administration into the body and exposure to physiological aqueous fluid. This study investigates the molecular behavior and phase inversion process of cellulose acetate butyrate (CAB)-based in situ forming gel (ISG) formulations containing moxifloxacin (Mx) or benzydamine HCl (Bz) as model drugs dissolved in N-methyl pyrrolidone (NMP) using molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The simulations reveal a solvent exchange mechanism, where the diffusion of water molecules replaces NMP, driving the formation of the CAB matrix. Bz exhibited faster diffusion and a more uniform distribution compared to Mx, which aggregated into clusters due to its larger molecular size. The analysis of the root mean square deviation (RMSD) and radius of gyration confirmed the faster diffusion of Bz, which adopted a more extended conformation, while Mx remained compact. The phase transformation was driven by the disruption of CAB-NMP hydrogen bonds, while CAB–water interactions remained limited, suggesting that CAB does not dissolve in water, facilitating matrix formation. The molecular configuration revealed that drug–CAB interactions were primarily governed by hydrophobic forces and van der Waals interactions rather than hydrogen bonding, controlling the release mechanism of both compounds. DFT calculations and electrostatic potential (ESP) maps illustrated that the acetyl group of CAB played a key role in drug–polymer interactions and that differences in CAB substitution degrees influenced the stability of drug-CAB complexes. Formation energy calculations indicated that Mx-CAB complexes were more stable than Bz-CAB complexes, resulting in a more prolonged release of Mx compared to Bz. Overall, this study provides valuable insights into the molecular behavior of CAB-based Mx-, Bz-ISG formulations. Full article

Background: Precision medicine refers to the formulation of personalized drug regimens according to the individual characteristics of patients to achieve optimal efficacy and minimize adverse reactions. Additive manufacturing (AM), also known as three-dimensional (3D) printing, has emerged as an optimal solution for precision drug delivery, enabling customizable and the fabrication of multifunctional structures with precise control over morphology and release behavior in pharmaceutics. However, the influence of 3D printing parameters on the printed tablets, especially regarding in vitro and in vivo performance, remains poorly understood, limiting the optimization of manufacturing processes for controlled-release profiles. Objective: To establish the fabrication process of 3D-printed controlled-release tablets via comprehensively understanding the printing parameters using fused deposition modeling (FDM) combined with hot-melt extrusion (HME) technologies. HPMC-AS/HPC-EF was used as the drug delivery matrix and carbamazepine (CBZ) was used as a model drug to investigate the in vitro drug delivery performance of the printed tablets. Methodology: Thermogravimetric analysis (TGA) was employed to assess the thermal compatibility of CBZ with HPMC-AS/HPC-EF excipients up to 230 °C, surpassing typical processing temperatures (160–200 °C). The formation of stable amorphous solid dispersions (ASDs) was validated using differential scanning calorimetry (DSC), hot-stage polarized light microscopy (PLM), and powder X-ray diffraction (PXRD). A 15-group full factorial design was then used to evaluate the effects of the fan speed (20–100%), platform temperature (40–80 °C), and printing speed (20–100 mm/s) on the tablet properties. Response surface modeling (RSM) with inverse square-root transformation was applied to analyze the dissolution kinetics, specifically t_50% (time for 50% drug release) and Q_4h (drug released at 4 h). Results: TGA confirmed the thermal compatibility of CBZ with HPMC-AS/HPC-EF, enabling stable ASD formation validated by DSC, PLM, and PXRD. The full factorial design revealed that printing speed was the dominant parameter governing dissolution behavior, with high speeds accelerating release and low speeds prolonging release through porosity-modulated diffusion control. RSM quadratic models showed optimal fits for t_50% (R² = 0.9936) and Q_4h (R² = 0.9019), highlighting the predictability of release kinetics via process parameter tuning. This work demonstrates the adaptability of polymer composite AM for tailoring drug release profiles, balancing mechanical integrity, release kinetics, and manufacturing scalability to advance multifunctional 3D-printed drug delivery devices in pharmaceutics. Full article

Cellulose acetate (CA), commercially produced from natural cellulose, is one of the promising candidates to solve the microplastic issue. In this study, attempts were made to prepare CA microparticles by means of melt extrusion of incompatible polymer blends comprising CA with plasticizer (triacetin (TA)) and polyvinyl alcohol (PVA) followed by selective removable of TA and PVA. As implied by semi-theoretical equation previously established by Wu (Wu’s equation), particle size decreased with increasing shear rate or decreasing viscosity ratio of polymers. CA microparticles with a controlled size of 2–8 μm, narrow particle size distribution, and smooth surface were successfully obtained. Efforts were made to determine the numerical solution of Wu’s equation to compare them with observed particle size. To this end, interfacial tension between dispersed and matrix phases to be incorporated in the equation was determined by group contribution methods. The root mean squared error (RMSE) between the observed and calculated particle size was unsatisfactorily large, 4.46 μm. It was found that one of the possible reasons for the limited prediction accuracy was migration of TA from the dispersed to matrix phase affecting the viscosity ratio. Further efforts will be required to achieve a better prediction. Full article

In this work, the development of Al-based metal matrix composites (MMCs) is achieved using hybrid SiC and ZrO₂ reinforcement particles for automotive applications. Powder metallurgy (PM) is employed with various combinations of important process parameters for the fabrication of MMCs. MMCs were characterized using scanning electron microscopy (SEM), X-ray diffractometry (XRD), and a microhardness study. All XRD graphs adequately exhibit Al, SiC, and ZrO₂ peaks, indicating that the hybrid MMC products were satisfactorily fabricated with appropriate mixing and sintering at all the considered fabrication conditions. Also, no impurity peaks were observed, confirming high composite purity. MMC products in all the XRD patterns, suitable for the desired applications. According to the SEM investigation, SiC and ZrO₂ reinforcement components are uniformly scattered throughout Al matrix in all produced MMC products. The occurrence of Al, Si, C, Zr, and O in EDS spectra demonstrates the effectiveness of composite ball milling and sintering under all manufacturing conditions. Moreover, an increase in interfacial bonding of fabricated composites at a higher sintering temperature indicated improved physical properties of the developed MMCs. The highest microhardness value is 86.6 HVN amid all the fabricated composites at 7% silica, 14% zirconium dioxide, 500° sintering temperature, 90 min sintering time, and 60 min milling time. An integrated Particle Swarm Optimization–Support Vector Machine (PSO-SVM) model was developed to predict microhardness based on the input parameters. The model demonstrated strong predictive performance, as evidenced by low values of various statistical metrics for both training and testing datasets, highlighting the PSO-SVM model’s robustness and generalization capability. Specifically, the model achieved a coefficient of determination of 0.995 and a root mean square error of 0.920 on the training set, while on the testing set, it attained a coefficient of determination of 0.982 and a root mean square error of 1.557. These results underscore the potential of the PSO-SVM framework, which can be effectively leveraged to optimize process parameters for achieving targeted microhardness levels for the developed Al-SiC-ZrO₂ Composites. Full article

In this paper, we investigate the possibility of recognizing a superposition of wave aberrations from a focal pattern based on a matrix of partial sums. Due to the peculiarities of the focal pattern, some types of the considered superpositions are recognized ambiguously from the intensity pattern in the focal plane by standard error-reduction algorithms. It is numerically shown that when recognizing superpositions of Zernike functions from the intensity pattern in the focal plane, the use of step-by-step optimization in combination with the Levenberg–Marquardt algorithm yields good results only with an initial approximation close to the solution. In some cases, the root mean square reaches 0.3, which is unacceptable for precise detection in optical systems that require prompt correction of aberrations in real time. Therefore, to overcome this drawback, an algorithm was developed that considers partial sums, which made it possible to increase the convergence range and achieve unambiguous recognition results for aberrations (root mean square does not exceed 10⁻⁸) described by superpositions of Zernike functions up to n = 5. Full article

Lithium batteries have become one of the best choices for current consumer electric vehicle batteries due to their good stability and high energy density. To ensure the safety and reliability of electric vehicles (EVs), the battery management system (BMS) must provide accurate and real-time information on the usage status of the onboard battery. This article highlights the precise estimation of the state of charge (SOC) applied to four models of lithium-ion batteries (Turnigy, LG, SAMSUNG, and PANASONIC) for electric vehicles in order to ensure optimal use of the battery and extend its lifespan, which is frequently influenced by certain parameters such as temperature, current, number of charge and discharge cycles, and so on. Because of the work’s novelty, the methodological approach combines the extended Kalman filter algorithm (EKF) with the noise matrix, which is updated in this case through an iterative process. This leads to the migration to a new adaptive extended Kalman filter algorithm (AEKF) in the MATLAB Simulink 2022.b environment, which is novel or original in the sense that it has a first-order association. The four models of batteries from various manufacturers were directly subjected to the Venin estimator, which allowed for direct comparison of the models under a variety of temperature scenarios while keeping an eye on performance metrics. The results obtained were mapped charging status (SOC) versus open circuit voltage (OCV), and the high-performance primitives collection (HPPC) tests were carried out at 40 °C, 25 °C, 10 °C, 0 °C and −10 °C. At these temperatures, their corresponding values for the root mean square error (RMSE) of (SOC) for the Turnigy graphene battery model were found to be: 1.944, 9.6237, 1.253, 1.6963, 16.9715, and for (OCV): 1.3154, 4.895, 4.149, 4.1808, and 17.2167, respectively. The tests cover the SOC range, from 100% to 5% with four different charge and discharge currents at rates of 1, 2, 5 and 10 A. After characterization, the battery was subjected to urban dynamometer driving program (UDDS), Energy Saving Test (HWFET) driving cycles, LA92 (Dynamometric Test), US06 (aggressive driving), as well as combinations of these cycles. Driving cycles were sampled every 0.1 s, and other tests were sampled at a slower or variable frequency, thus verifying the reliability and robustness of the estimator to 97%. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

Proton exchange membrane fuel cells are a clean energy technology with wide application in transportation and stationary energy systems. Due to the problem of voltage degradation under long-term dynamic loads, predicting their performance degradation trend is of great significance for extending the life of proton exchange membrane fuel cells and improving system reliability. This study adopts a data-driven approach to construct a degradation prediction model. In view of the problem of many input parameters and complex distribution of degradation features, a neural network model based on a multi-head attention mechanism and class token is first proposed to analyze the impact of different operating parameters on the output voltage prediction. The importance of each input variable is quantified by the attention weight matrix to assist feature screening. Subsequently, a prediction model is constructed based on Transformer to characterize the voltage degradation trend of fuel cells under dynamic conditions. The experimental results show that the root mean square error and mean absolute error of the model in the test phase are 0.008954 and 0.006590, showing strong prediction performance. Based on the importance evaluation provided by the first model, 11 key parameters were selected as inputs. After this input simplification, the model still maintained a prediction accuracy comparable to that of the full-feature model. This result verifies the effectiveness of the feature screening strategy and demonstrates its contribution to improved generalization and robustness. Full article

In the assembly of spacecraft cabins, the presence of uncertain and time-varying temperature environments can induce thermal deformation in bulkheads, potentially affecting dimensional stability. Online sensing of thermal deformation is critical for mitigating such risks. However, conventional finite element methods (FEMs) rely on cascading thermal and structural analyses, which suffer from inefficiency. To address this issue, we propose a methodology that integrates a physical model with a data-driven temperature field measurement technique, demonstrated through case studies involving a spacecraft porthole bulkhead. First, leveraging the geometric invariance of the bulkhead during assembly, a purely static FE model is established offline. Second, multi-point temperature measurements combined with Kriging estimation are employed to directly reconstruct the temperature field, circumventing the computationally intensive FEM-based thermal analysis process. Finally, by utilizing the precomputed inverse stiffness matrix and performing an online conversion from temperature to equivalent forces, thermal deformation is rapidly resolved. The numerical results demonstrate that the root-mean-square errors of the predicted full-field deformation are maintained at the micron level, with an average computation time of less than 0.14 s. Furthermore, a meticulously designed experiment was conducted, where the predicted thermal displacements of several key points showed good agreement with measurements by means of a laser tracker. This research provides a promising tool to achieve digital twinning of thermal deformation states for aerospace components. Full article

Transformer-based models have achieved remarkable success across various AI tasks, but their growing complexity has led to significant computational and memory demands. While most optimization efforts have focused on linear operations such as matrix multiplications, non-linear functions like Softmax and layer normalization (LayerNorm) are increasingly dominating inference latency, especially for long sequences and high-dimensional inputs. To address this emerging bottleneck, we present a hardware accelerator that jointly approximates these non-linear functions using piecewise linear approximation for the exponential in Softmax and Newton–Raphson iteration for the square root in LayerNorm. The proposed unified architecture dynamically switches operation modes while reusing hardware resources. The proposed accelerator was implemented on a Xilinx VU37P FPGA and evaluated with BERT and GPT-2 models. Experimental results demonstrate speedups of up to 7.6× for Softmax and 2.0× for LayerNorm, while maintaining less than 1% accuracy degradation on classification tasks with conservative approximation settings. However, generation tasks showed greater sensitivity to approximation, underscoring the need for task-specific tuning. Full article

The electrically evoked compound action potential (ECAP) is a crucial physiological signal used by clinicians to evaluate auditory nerve functionality. Clean ECAP recordings help to accurately estimate auditory neural activity patterns and ECAP magnitudes, particularly through the panoramic ECAP (PECAP) framework. However, noise—especially in low-signal-to-noise ratio (SNR) conditions—can lead to significant errors in parameter estimation. This study proposes a two-stage preprocessing denoising (TSPD) algorithm to address this issue and enhance ECAP signals. First, an ECAP matrix is constructed using the forward-masking technique, representing the signal as a two-dimensional image. This matrix undergoes spatial noise reduction via an improved spatial median (I-Median) filter. In the second stage, the denoised matrix is vectorized and further processed using a log-spectral amplitude (LSA) Wiener filter for spectral domain denoising. The enhanced vector is then reconstructed into the ECAP matrix for parameter estimation using PECAP. The above integrated spatial-spectral denoising framework is denoted as PECAP-TSPD in this work. Evaluations are conducted using a simulation-based ECAP model mixed with simulated and experimental noise, designed to emulate the spatial characteristics of real ECAPs. Three objective quality measures—namely, normalized root mean square error (RMSE), two-dimensional correlation coefficient (TDCC), and structural similarity index (SSIM)—are used. Simulated and experimental results show that the proposed PECAP-TSPD method has the lowest average RMSE of PECAP magnitudes (1.952%) and auditory neural patterns (1.407%), highest average TDCC (0.9988), and average SSIM (0.9931) compared to PECAP (6.446%, 5.703%, 0.9859, 0.8997), PECAP with convolutional neural network (CNN)-based denoising mask (PECAP-CNN) (9.700%, 7.111%, 0.9766, 0.8832), and PECAP with improved median filtering (PECAP-I-Median) (4.515%, 3.321%, 0.9949, 0.9470) under impulse noise conditions. Full article

We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive integrated moving average (ARIMA($p,\mathrm{d},\mathrm{q}$)) model. The stationarity of China’s energy consumption data from 2000 to 2018 is assessed, with an augmented Dickey–Fuller (ADF) test conducted on the $\mathrm{d}$-order difference series. Based on the auto correlation function (ACF) and partial auto correlation function (PACF) plots of the difference time series, the optimal parameters $\mathrm{p}$ and $\mathrm{q}$ are selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), thereby determining the specific ARIMA configuration. By simulating real values using the ARIMA model and calculating relative errors, the estimated values are categorized into states. These states are then combined with a Markov transition probability matrix to determine the final predicted values. The ARIMAMKM model is validated using China’s energy consumption data, achieving high prediction accuracy as evidenced by metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), $\mathrm{S}\mathrm{T}\mathrm{D}$, and ${\mathrm{R}}^2$. Comparative analysis demonstrates that the ARIMAMKM model outperforms five other competitive models: the grey model (GM(1,1)), ARIMA(0,4,2), quadratic function model (QFM), nonlinear auto regressive neural network (NAR), and fractional grey model (FGM(1,1)) in terms of fitting performance. Additionally, the model is applied to Guangdong province’s resident population data to further verify its validity and practicality. Full article