Model Parameters Research Articles

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

Impacts of inter-satellite links on the ECOM model performance for BDS-3 MEO satellites

Inter-satellite link (ISL) plays an essential role in current and future Global Navigation Satellite System (GNSS). In this study, we investigate the impact of ISL observations on precise orbit determination for BeiDou-3 Navigation Satellite System (BDS-3) Medium Earth Orbit (MEO) satellites based on different Extended CODE Orbit Models (ECOM). Thanks to the better observation geometry of the Ka-band ISL data compared to the L-band data for BDS-3 MEO satellites, the ISL solution substantially reduces Orbit Boundary Discontinuity (OBD) errors, except for C30, which suffers from unstable Ka-band hardware delay. From the external quality analysis, ISL significantly enhances the reliability of the orbit of MEO satellites manufactured by the China Academy of Space Technology (CAST). The standard deviation (STD) of the satellite laser ranging (SLR) residuals is approximately 2.5 cm, and the root mean square (RMS) is reduced by 10–23% compared to L-band solutions. Besides, the Sun-elongation angle dependent systematic error in SLR residuals nearly vanishes based on the reduced 5-parameter ECOM (ECOM1) or extended 7-parameter ECOM (ECOM2) with ISL data. This is because the ISL reduces the correlation between state parameters and solar radiation pressure (SRP) parameters as well as those among SRP parameters, leading to a more accurate estimation of both orbit and SRP perturbations, particularly those along B direction. This confirms that the deficiency of the SRP models for BDS-3 CAST satellites can be compensated by using better observation geometry from ISL data. On the other hand, for the satellite manufactured by Shanghai Engineering Center for Microsatellites (SECM), the ISL allows for a more accurate estimation of the Bc1 parameter in the ECOM1 model. This only reduces linear systematic error, possibly because the impact generated by the satellite bus cannot be entirely absorbed by the B-direction parameters.

Chemical Potential Analysis as an Alternative to the van't Hoff Method: Hypothetical Limits of Solar Thermochemical Hydrogen.

The van't Hoff method is a standard approach for determining reaction enthalpies and entropies, e.g., in the thermochemical reduction of oxides, which is an important process for solar thermochemical fuels and numerous other applications. However, by analyzing the oxygen partial pressure pO2, e.g., as measured by thermogravimetric analysis (TGA), this method convolutes the properties of the probe gas with the solid-state properties of the examined oxides, which define their suitability for specific applications. The "chemical potential method" is here proposed as an alternative. Using the oxygen chemical potential ΔμO instead of pO2 for the analysis, this method does not only decouple gas-phase and solid-state contributions but also affords a simple and transparent approach to extracting the temperature dependence of the reduction enthalpy and entropy, which carries important information about the defect mechanism. For demonstration of the approach, this work considers three model systems; (1) a generic oxide with noninteracting, charge-neutral oxygen vacancy defects, (2) Sr0.86Ce0.14MnO3(1-δ) alloys with interacting vacancies, and (3) a model for charged vacancy formation in CeO2, which reproduces the extensive experimental TGA data available in the literature. The reduction behavior of these model systems obtained from the chemical potential method is correlated with simulated results for the thermochemical water splitting cycle, highlighting the exceptional behavior of CeO2, which originates from defect ionization. The theoretical performance limits for solar thermochemical hydrogen within the charged defect mechanism are assessed by considering hypothetical materials described by a variation of the CeO2 model parameters within a plausible range.

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants.

The intricate development of liquid-crystal lubricants necessitates the timely and accurate prediction of their tribological performance in different environments and an assessment of the importance of relevant parameters. In this study, a classification model using Gaussian noise extreme gradient boosting (GNBoost) to predict tribological performance is proposed. Three additives, polysorbate-85, polysorbate-80, and graphene oxide, were selected to fabricate liquid-crystal lubricants. The coefficients of friction of these lubricants were tested in the rotational mode using a universal mechanical tester. A model was designed to predict the coefficient of friction through data augmentation of the initial data. The model parameters were optimized using particle swarm optimization techniques. This study provides an effective example for lubricant performance evaluation and formulation optimization.

The PDI model system for parameterizing soil hydraulic properties

AbstractThe Peters–Durner–Iden (PDI) model system for describing soil hydraulic properties (SHP) has been developed over a decade. Inspired by Rien van Genuchten's seminal work, the PDI system focuses on an efficient and simple parameterization of water retention curves and hydraulic conductivity curves (HCC) across the entire soil moisture spectrum. By combining capillary and non‐capillary components for water retention and conductivity, it aims to reconcile mathematical simplicity and insights on water adsorption and film flow in soils. Recent developments have reduced the number of free parameters of the conductivity model to zero, enhancing the model's applicability in cases of limited data availability. The first reduction was achieved by a prediction of absolute non‐capillary conductivity based on the consideration of film and corner flow on the pore scale, and the second by a prediction of absolute capillary conductivity by a capillary bundle model. This allows a complete characterization of SHP over the entire moisture range with only four retention curve parameters. The inclusion of a maximum pore size in the capillary conductivity model prevents an unrealistic drop of the HCC near saturation. This paper provides a comprehensive overview of the PDI model system, emphasizing its conceptual features and mathematical details. An Excel sheet and a Python code stored in a repository are provided for accessibility.

Internet of Things intrusion detection: Research and practice of NSENet and LSTM fusion models

To address the problems of complex environment, limited device computational resources and limited memory resources in the existing IoT, SELSTM, an intrusion detection system composed of NSENet and LSTM fusion based on SENet, is investigated. The NSENet part of the SELSTM system is based on the squeeze-and-excitation network (SENet). The lightweight computational modules NonLocal, SKConv and inverted residuals are fused into SE blocks, and self-attention of Nonlocal is used to improve the local receptive field of feature extraction. The channel attention and spatial attention of each part of the data are strengthened by the use of SKConv To enhance the adaptive convolution ability of the model and ensure the completeness of the information, the properties of the inverted residual structure are used to ensure that the gradient of the model decreases steadily without gradient explosion or disappearance. For the problem of data imbalance, the dataset is randomly resampled using the weight resampling technique to improve the balance of the dataset to ensure that the final detection effect of the model is more effective and generalized, while the data flow is divided into two parts for processing, and the model parameters are optimized using the model gradient optimizer consisting of the optimizer Lion and the optimization function Lookahead. The model extracts the spatial and temporal features of the data through multidimensional extraction to ensure the completeness of the data feature information in multiple dimensions, thus obtaining better detection results. The results of the experiments comparing the SELSTM model with other models on the intrusion dataset show that the intrusion detection model has a higher detection precision and accuracy than the traditional deep learning intrusion detection model, which indicates that the SELSTM has better detection performance properties and better practicality and effectiveness on IoT devices.

Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm to solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold algorithm is proposed, which is AHCSMA, to solve these problems efficiently. Specifically, there are three innovations in this paper: (i) new self-adaptive Cauchy mutation operator is developed to improve the mutation ability of the population; (ii) the crossover rate balance mechanism is proposed to make up for the neglected relationship between individuals and crossover rates. Then the differential vector information between the dominant individual and other individuals in the population is highly utilized to increase the evolution speed of the algorithm; (iii) self-adaptive restart hybrid opposition learning is designed to alleviate the situation where the algorithm falls into local optimality. To verify the competitive of AHCSMA, UAV path planning problems, engineering problems, nonlinear parameter extraction of photovoltaic model problems and parameter identification problems of highly nonlinear infinite impulse response are used to test the ability of the AHCSMA, accumulation more than 50 algorithms are used as comparison algorithms, and results report that AHCSMA is extremely competitive and performs better when optimizing these real-life problems.

Scalable Binary Neural Network Applications in Oblivious Inference

Binary neural network (BNN) delivers increased compute intensity and reduces memory/data requirements for computation. Scalable BNN enables inference in a limited time due to different constraints. This paper explores the application of Scalable BNN in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on his/her data by a trained model held by the server without disclosing the data or learning the model parameters. Two contributions of this paper are: (1) we devise lightweight cryptographic protocols explicitly designed to exploit the unique characteristics of BNNs. (2) we present an advanced dynamic exploration of the runtime-accuracy tradeoff of scalable BNNs in a single-shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under various computational budgets. Compared to CryptFlow2, the state-of-the-art technique in the oblivious inference of non-binary DNNs, our approach reaches 3× faster inference while keeping the same accuracy. Compared to XONN, the state-of-the-art technique in the oblivious inference of binary networks, we achieve 2× to 12× faster inference while obtaining higher accuracy.

An improved model for discrete element method simulation of spatial gradient distributions of freeze-thaw-induced damage to sandstone

The investigation into the freeze-thaw (F-T) damage holds paramount theoretical significance in comprehending the mechanisms underlying F-T disasters, predicting catastrophes, and devising engineering protection systems. F-T damage in rock evolves progressively from the surface towards the interior, however, current models exhibit conspicuous deficiencies in simulating such progressive damage. Drawing upon experimental findings concerning the evolution of F-T damage in water-saturated rock, an improved discrete element model (DEM) for rock F-T damage is developed. In this model, thermal conduction model and volume expansion theory are integrated to describe the volumetric expansion characteristics during the freezing of internal pore water, thus effectively simulating the initiation and progression characteristics of frost heave cracks. We validated this model through comparisons with theoretical analyses and experiments, exploring the effects of model parameters. Employing moment tensor, the spatial gradient distribution patterns of F-T damage in water-saturated rock from the vantage point of fracture energy was analyzed. The results reveal that at −20 °C, 20 F-T cycles are requisite for the propagation of damage from the outer layer to the center of the Sichuan sandstone samples. The extent of damage in the outer layer exceeds that of the center, with the maximum fracture energy density of the outer layer being approximately 38.7 times that of the center. This model aptly delineates the progressive development of F-T damage characteristics and holds promise for further applications in identifying and delineating F-T damage zones in cold region rock engineering.

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.

Mechanical properties of recycled aggregate concrete containing brick particles

To investigate the mechanical properties of recycled aggregate concrete containing brick particles(RACB), compressive strength tests were conducted on RACBs with different water cement ratios and recycled fine aggregate substitution rates. The changes in compressive strength, elastic modulus, and peak strain of RACB with recycled fine aggregate substitution rates were analyzed, and a stress–strain equation was established. A damage model was established using ultrasonic nondestructive testing technology to reveal the damage mechanism of RACB. Results show that the basic mechanical properties of RACB reach the best when the replacement rate of recycled fine aggregate (r) is 30%. The model parameter a of the stress–strain curve equation is consistent with the change rule of elastic modulus with r. The model parameter b has a quadratic function relationship with r, reflecting the brittleness of RACB. The initial damage of RACB is relatively large, and the cracks penetrate the recycled coarse aggregate, and lead to specimen failure.

Mechanistic Analysis of Hydrogen Evolution Reaction on Stationary Polycrystalline Gold Electrodes in H2SO4 Solutions

An impedance model based on the Volmer–Heyrovsky–Tafel mechanism was developed to study the kinetics of the hydrogen evolution reaction on polycrystalline gold electrodes at moderate overpotentials in aqueous H2SO4 (0.5 and 1.0 M) solutions. The model was optimized on data from potentiodynamic polarization and electrochemical impedance spectroscopy, and model parameters were extracted. Consistent with expectations, the magnitude of the impedance data indicated a higher rate of hydrogen evolution at lower pH. Also, the fractional surface coverage of adsorbed hydrogen (θHads) increases with increasing overpotential but the small value of θHads indicates only weak adsorption of H on gold. Tafel slopes and exchange current densities were estimated to be in the range of 81–124 mV/dec, and 10−6 and 10−5 A/cm2 in H2SO4 (0.5 and 1.0 M), respectively. The results show that the model accounts well for the experimental data, such as the steady-state current density. Sensitivity analysis reveals that the electrochemical parameters (α1, α2, k10, k−10, and k20) associated with the kinetics of the hydrogen evolution reaction have a major impact on the calculated impedance but the standard rate constant for hydrogen oxidation reaction (k−20) does not strongly affect the calculated impedance.

Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

SummaryAccurate estimation of lithium‐ion battery state of energy (SOE) is an important prerequisite for prolonging battery life and ensuring battery safety. To achieve a high‐precision estimation of the SOE, while a ternary lithium‐ion battery being the specifically targeted in this study, a novel method for SOE estimation is proposed, which combines limited‐memory recursive least squares (LM‐RLS) with strong tracking adaptive window Multi‐innovation cubature Kalman filtering (STW‐MCKF). In the LM‐RLS algorithm, the model parameters at the current time are updated with a limited dataset to solve the data saturation problem and improve the recognition accuracy of the RLS algorithm. The CKF algorithm is optimized by the STW algorithm in the STW‐CKF algorithm to enhance its robustness under strong disturbances. Additionally, a self‐adaptive window multiple innovation strategy is proposed to improve the accuracy of SOE estimation and the stability of the CKF algorithm, while maintaining a balance between computational complexity and SOE estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STW‐MCKF algorithm has a maximum convergence time of 4 s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the maximum convergence time is 3 s, and the SOE estimation error is within 2.34%. Furthermore, the STW‐MCKF algorithm demonstrates good stability under the two conditions, indicating the effectiveness of the proposed method for lithium‐ion battery SOE estimation.

Simulation and parameter determination of the net sorption of phenanthrene by sediment particles

The distribution of polycyclic aromatic hydrocarbons (PAHs) in the ocean is affected by the sorption-desorption process of sediment particles. This process is determined by the concentration of PAHs in seawater, water temperature, and organic matter content of sediment particles. Quantitative relationships between the net sorption rates (=the difference of sorption and desorption rates) and these factors have not been established yet and used in PAH transport models. In this study, phenanthrene was chosen as the representative of PAHs. Three groups of experimental data were collected to address the dependence of the net sorption processes on the initial concentration, water temperature, and organic carbon content representing organic matter content. One-site and two-compartment mass-transfer models were tested to represent the experimental data using various parameters. The results showed that the two-compartment mass-transfer model performed better than the one-site mass-transfer model. The parameters of the two-compartment mass-transfer model include the sorption rate coefficients kafand kas (L g−1 min−1), and the desorption rate coefficients kdf and kds (min−1). The parameters at different temperatures and organic carbon contents were obtained by numerical simulations. Linear relationships were obtained between the parameters and water temperature, as well as organic carbon content. kaf, kas and kdf decreased linearly, while kds increased linearly with temperature. kaf, kas and kdf increased linearly, while kds decreased linearly with organic carbon content. The r2 values between the simulation results based on the relationships and the experimental results reached 0.96–0.99, which supports the application of the model to simulate sorption-desorption processes at different water temperatures and organic carbon contents in a realistic ocean.

Quantitative modelling of type Ia supernovae spectral time series: Constraining the explosion physics

Abstract Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe and direct comparisons between observations and explosion model predictions. Such tools have provided useful insights, but the subjective nature with which empirical modelling is performed makes it difficult to obtain robust constraints on the explosion physics or expand studies to large populations of objects. Machine learning accelerated tools have therefore begun to gain traction. In this paper, we present riddler, a framework for automated fitting of SNe Ia spectral sequences up to shortly after maximum light. We train a series of neural networks on realistic ejecta profiles predicted by the W7 and N100 explosion models to emulate full radiative transfer simulations and apply nested sampling to determine the best-fitting model parameters for multiple spectra of a given SN simultaneously. We show that riddler is able to accurately recover the parameters of input spectra and use it to fit observations of two well-studied SNe Ia. We also investigate the impact of different weighting schemes when performing quantitative spectral fitting and show that best-fitting models and parameters are highly dependent on the assumed weighting schemes and priors. As spectroscopic samples of SNe Ia continue to grow, automated spectral fitting tools such as riddler will become increasingly important to maximise the physical constraints that can be gained in a quantitative and consistent manner.

Dynamics of a New Delayed Glucose–Insulin Model with Obesity

In this work, a new glucose–insulin model incorporating time delay and obesity is developed to gain insights of its dynamical mechanisms. Through the method of multiple scales, we theoretically demonstrate that time delay can drive the system to yield Hopf bifurcation, thereby producing oscillating solutions that are consistent with the simulation results. Moreover, obesity changes the level of glucose, but cannot induce oscillations. In particular, it is found that under the combined effect of obesity and time delay, obesity delays the appearance of Hopf bifurcation which is induced by time delay. Results show that a low calorie diet can achieve therapeutic effects including reducing blood glucose fluctuations and insulin resistance, which can be used as an adjuvant for the treatment of diabetes. In addition, our results indicate that the delay, together with an optimal rate of model parameters can cause a variety of dynamics and induce glucose oscillations. The result obtained in this paper may help to better understand the obesity, diabetes, and the interaction between glucose and insulin, so that control strategies can be designed to better regulate blood glucose levels and fluctuations and mitigate the occurrence of type-2 diabetes.

Classification of Astronomical Spectra Based on Multiscale Partial Convolution

The automated and efficient classification of astronomical spectra is an important research issue in the era of large sky surveys. Most current studies on automatic spectral classification primarily focus on specific data sets and demonstrate outstanding performance. However, the diversity in spectra poses formidable challenges for these classification models, as they exhibit limited capability to generalize across more comprehensive data sets. In response to these challenges, we pioneer a method called the multiscale partial convolution net (MSPC-Net), which amalgamates partial, large kernel, and grouped convolution to facilitate multilabel spectral classification. By harnessing the capabilities of partial convolution, MSPC-Net can effectively reduce the number of model parameters, accelerate the training process, and mitigate the overfitting issue. Integrating large kernel and grouped convolution empowers the model to capture local and global features simultaneously, enhancing its overall classification efficacy. To rigorously evaluate the model’s performance, we generate ten different data sets sourced from the Sloan Digital Sky Survey and Large Sky Area Multi-Object Spectroscopic Telescope. These data sets encompass stellar class, stellar subclass, and full classification, providing a comprehensive assessment across various application scenarios. The experimental results reveal that MSPC-Net consistently outperforms the other models across different data sets, especially demonstrating superior performance in the last two data sets with full classification. Consequently, MSPC-Net is poised to find extensive applications in the detailed classification for large-scale sky survey projects. This work not only addresses the challenges of generalization in spectral classification but also contributes significantly to the advancement of robust models for astronomical research.

Hidden Variable Models in Text Classification and Sentiment Analysis

In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors such as generalized Dirichlet (GD) and Beta-Liouville (BL), leading to GDMPCA and BLMPCA models, respectively. Besides using these priors as they generalize the Dir, importantly, we also implement a deterministic method that uses variational Bayesian inference for the fast convergence of the proposed algorithms. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, providing a computationally efficient method for inference. These two variational models offer higher flexibility while assigning each observation to a distinct cluster. We create several multitopic models and evaluate their strengths and weaknesses using real-world applications such as text classification and sentiment analysis.

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.

Investigating the antecedents of mHealth adoption by older adults in a lower-middle income country: The PLS-MGA approach

ABSTRACT The adoption of technology and the rising digital divide among the older adult population, predominantly in lower-middle income countries such as Bangladesh, have posed a precarious challenge. Hence, the time has come to navigate this upcoming challenge. Thus, the current research aims to investigate the impact of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR) on the behavioral intention to adopt mHealth (BI). Furthermore, multigroup analysis (MGA) is used to examine BI regarding gender and income groups. Data from 140 older adults in Bangladesh were gathered and later analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate the measurement and structural model parameters and produce concurrent bootstrap evaluations. The findings revealed that PE and PU substantially affect the intention to adopt mHealth, whereas PR has small effect on mHealth adoption. Furthermore, the MGA depicts that the effect of PR is statistically significant for older adult males and females. Additionally, the effect of PE on behavioral intention to adopt mHealth between income groups (lower income vs. higher income) is significant. Therefore, this research advances our theoretical and empirical understanding by yielding new scholarly evidence in the domain of mHealth adoption by older adults. Finally, the paper concludes with augmented practical considerations concerning the adoption of mHealth as a technology by older adults, which will assist in alleviating the digital divide for any such lower-middle income country across the globe.